Table of contents
  1. Story
  2. Sharing Best Practices for the Implementation of Big Data Applications in Government and Science Communities
    1. Abstract
    2. I. Introduction
    3. II. Organization of Working Group
      1. A. Mission Statement
      2. B. Framework of Activities
    4. III. Keynote and Panel Discussion
    5. V. Conclusion
    6. Acknowledgements
    7. References
  3. Slides
    1. Slide 1 Challenges and Solutions for Big Data in the Public Sector
    2. Slide 2 Overview
    3. Slide 3 Mission Statement
    4. Slide 4 Decisions = Science + Art: The Challenger Accident
    5. Slide 5 Fourth Paradigm and Fourth Question
    6. Slide 6 Symposium on Predictive Analytics for Defense and Government
    7. Slide 7 NIH Data Commons
    8. Slide 8  Best Practices for Data: A Biologists View
    9. Slide 9 Examples of Data Publications in Data Browsers for Senior Government People
    10. Slide 10 Data Science Central: Meteors Descriptive and Predictive​
    11. Slide 11 Data Science for JHU DIBBs Project: Knowledge Bases
    12. Slide 12 Data Science for JHU DIBBs Project​: Analytics & Visualizations
    13. Slide 13 Cover Page-Performance Analytics: FDA TRACK
    14. Slide 14 October 6th Meetup Agenda
  4. Spotfire Dashboard
  5. Research Notes
  6. From SkyServer to SciServer: The JHU DIBBs Project
    1. Agenda
    2. 1. Introduction (Alex)
      1. Big Data in Science
      2. Science is Changing
      3. Gray's Law of Data Engineering
      4. Scientific Data Analysis Today
      5. Exponential Data Growth
      6. Data Access is Hitting a Wall
      7. Non-Incremental Changes
    3. 2. Background, Vision and Goals (Alex)
      1. Why I Astronomy Interesting?
      2. Sloan Digital Sky Survey (SDSS)
      3. Skyserver
      4. Impact of Sky Surveys
      5. GalaxyZoo
      6. The SDSS Genealogy
      7. Oncospace
      8. Life Under Your Feet
      9. Cumulative Sensor Data
      10. Data in HPC Simulations
      11. Immersive Turbulence
      12. Daily Usage
      13. Simulations in the DB
      14. Scalable Data-Intensive Analysis
      15. The Long Tail
      16. JHU Data-Scope
      17. SciServer
    4. 3. Science Collaboration (Alex)
      1. Collaborative Science Projects 1
      2. Collaborative Science Projects 2
      3. The SDSS "CAStle"
      4. How Skyserver Changed Astronomy
    5. 4. SDSS Unification (Ani)
      1. The SDSS Genealogy (Updated)
      2. The Broad Impact of SDSS
      3. Reusable Building Blocks 1
      4. Reusable Building Blocks 2
      5. SkyServer
      6. ImgCutout
      7. CAS Jobs
      8. SciDrive & Portal
      9. SkyQuery
    6. 5. Project Operations, Roadmap and Progress (Mike)
      1. Project Management
      2. Teams and Governance
      3. Roadmap
      4. High Level Plan
      5. High Level Roadmap
      6. Architectural Development
      7. Architecture - At Project Start
      8. Architecture - Now
      9. Architecture - 18 Months
      10. Architecture - 3 Years
      11. SkyQuery Cluster Configuration
      12. Progress Overview (1)
      13. Progress Overview (2)
      14. SDSS Data Migration Progress
      15. Unified SDSS Website
      16. Progress on GLUSEEN
      17. Process Improvement
    7. 6. Outreach and Collaboration (Jordan)
      1. Branding and Website
      2. Community Engagement
      3. Training
      4. User Feedback
      5. Citizen Science
      6. Student Notebook
      7. Beyond SDSS SkyServer
      8. New Educational Activities
      9. DIBBs Partner Collaboration
      10. Trends
    8. 7. Summary (Alex)
      1. Summary
      2. Henry Ford Quote
      3. Contacts
  7. The Sloan Digital Sky Survey: Mapping the Universe
    1. Acknowledgments
    2. SDSS has been supported by
  8. Explore
    1. Data Access
      1. Current and Future Data
        1. Accessing the Data
      2. Past Data Releases
      3. Working with SDSS Data
    2. SDSS-III Data Release 10
      1. The Tenth SDSS Data Release (DR10)
      2. Using DR10
      3. Acknowledging DR10
    3. Scope of DR10
      1. The Scope of DR10
      2. Optical Data
      3. Optical imaging data statistics
      4. Optical spectroscopic data statistics
      5. Infrared (APOGEE) Data
      6. Infrared (APOGEE) spectroscopic data statistics
    4. DR10 Tutorials
      1. Tutorials
        1. Getting Started
        2. Getting Data for Individual Objects
        3. Searching for Data
        4. Downloading Data
        5. Reading SDSS Data Files
        6. Working with APOGEE
        7. Working with SEGUE
  9. Learn
    1. Surveys
      1. Sloan Digital Sky Surveys
      2. SDSS-IV: Current Surveys (2014-2020)
        1. APOGEE-2
        2. eBOSS
        3. MaNGA
      3. SDSS-III: Prior Surveys (2008-2014)
        1. APOGEE
        2. BOSS
        3. MARVELS
        4. SEGUE-2
      4. SDSS-I/II: Prior Surveys (2000-2008)
        1. Legacy
        2. Supernova
        3. SEGUE-1
    2. Instruments
      1. Telescopes
        1. The Sloan Foundation 2.5m Telescope at Apache Point Observatory
        2. The Irénée du Pont Telescope at Las Campanas Observatory
      2. Spectrographs
        1. eBOSS / BOSS
        2. APOGEE-2 / APOGEE
        3. MaNGA
        4. MARVELS
      3. Camera
    3. Education
      1. SDSS: Data For All
      2. SDSS News
      3. Connect with Us
      4. Citizen Science with SDSS
      5. SDSS for Educators
      6. Introduction to SDSS
      7. SDSS Education Group
      8. People
    4. Results & Science
      1. Science Results
        1. Cosmology
        2. Quasars
        3. Galaxies
        4. The Local Group
        5. The Milky Way
        6. Stars
        7. Solar System
        8. Other Science Results
      2. Press Releases
      3. SDSS Science Blog
      4. Publications
  10. Collaboration
    1. Policies
      1. Publication Policy
      2. External Collaborators
      3. Survey Science Teams & Working Groups
      4. Image Use Policy
    2. SDSS Institutions
      1. Full Member Institutions
      2. Associate Member Institutions
      3. Participation Groups
    3. Official SDSS Acknowledgment
  11. About
    1. How to Cite SDSS
    2. Image Use Policy
    3. Publication Policy
    4. Contact Us
      1. SDSS Collaboration
        1. Director
        2. Project Scientist
        3. Scientific Spokesperson
        4. Education and Public Outreach Director
      2. Contacting SDSS-III
        1. Director
        2. Project Scientist
        3. Scientific Spokesperson
        4. Education and Public Outreach Director
      3. Survey Contacts
        1. SDSS-IV Surveys
          1. APOGEE-2
          2. eBOSS
          3. MaNGA  
        2. SDSS-III Surveys
          1. APOGEE
          2. BOSS
          3. MARVELS
          4. SEGUE-2
  12. THE TENTH DATA RELEASE OF THE SLOAN DIGITAL SKY SURVEY: FIRST SPECTROSCOPIC DATA FROM THE SDSS-III APACHE POINT OBSERVATORY GALACTIC EVOLUTION EXPERIMENT
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. SCOPE OF DR10
      1. Table 1 Contents of DR10
        1. Optical Imaging a
        2. APOGEE Spectroscopy
        3. BOSS Spectroscopy
        4. All Optical Spectroscopy from SDSS up through DR10
      2. Figure 1. The distribution on the sky of all APOGEE DR10 pointings in Galactic coordinates
      3. Figure 2. The distribution on the sky of all SDSS imaging
    4. 3. THE APACHE POINT OBSERVATORY GALAXY EVOLUTION EXPERIMENT (APOGEE)
      1. 3.1. Overview of APOGEE
      2. 3.2. The APOGEE Instrument and Observations
        1. Figure 3. (top) A 2D spectrogram from the APOGEE instrument
        2. Figure 4. The distribution of number of spectroscopic visits for APOGEE stars included in DR10
        3. Figure 5. The distribution of time between visits for APOGEE stars, useful for determining the sensitivity to radial velocity variations due to binarity
        4. Figure 6. Reported S/N per pixel of APOGEE DR10 co-added stellar spectra
        5. Figure 7. S/N per pixel of spectra of stars as a function of their apparent H-band magnitude
      3. 3.3. APOGEE Main and Ancillary Targets
        1. Figure 8. Two-dimensional histogram of the APOGEE DR10 stars
      4. 3.4. APOKASC
      5. 3.5. APOGEE Data Analysis
        1. Figure 9. Typical APOGEE spectra at high S/N
      6. 3.6. Issues with APOGEE Spectra
      7. 3.7. APOGEE Stellar Parameter and Chemical Abundances Pipeline (ASPCAP)
        1. Figure 10. (upper lines) An example ASPCAP fit (red) to a typical APOGEE co-added stellar spectrum (black). (lower lines)
        2. 3.7.1. Parameter Accuracies
        3. 3.7.2. ASPCAP Outputs
          1. Figure 11. The one-dimensional and two-dimensional distributions of APOGEE stellar parameters
          2. Figure 12. ASPCAP log g vs. Teff with the points color-coded by [M/H]
      8. 3.8. APOGEE Data Products
    5. 4. THE BARYON OSCILLATION SPECTROSCOPIC SURVEY (BOSS)
      1. Figure 13. BOSS DR10 spectroscopic sky coverage in the Northern Galactic Cap (top) and Southern Galactic Cap (bottom)
      2. Figure 14. The distribution of BOSS DR10 spectroscopic objects versus lookback time
      3. Figure 15. N(z) of SDSS-III BOSS spectra in DR10 compared to that of the SDSS-I/II Legacy spectra for galaxies (top) and quasars (bottom).
      4. 4.1. A New Quasar Target Class in DR10
      5. 4.2. Updates to BOSS Data Processing
      6. 4.3. Updates to BOSS Galaxy Stellar Population Parameters
    6. 5. DATA DISTRIBUTION
    7. 6. FUTURE
    8. Footnotes
      1. 1
      2. 2
      3. 3
      4. 4
      5. 5
      6. 6
      7. 7
      8. 8
      9. 9
      10. 10
      11. 11
      12. 12
      13. 13
      14. 14
      15. 15
      16. 16
      17. 17
      18. 18
      19. 19
      20. 20
      21. 21
      22. 22
      23. 23
      24. 24
      25. 25
      26. 26
      27. 27
      28. 28
      29. 29
      30. 30
      31. 31
      32. 32
      33. 33
      34. 34
      35. 35
      36. 36
      37. 37
      38. 38
      39. 39
      40. 40
      41. 41
      42. 42
      43. 43
      44. 44
      45. 45
      46. 46
      47. 47
      48. 48
      49. 49
      50. 50
      51. 51
      52. 52
      53. 53
      54. 54
      55. 55
      56. 56
      57. 57
      58. 58
      59. 59
      60. 60
      61. 61
      62. 62
      63. 63
      64. 64
      65. 65
      66. 66
      67. 67
      68. 68
      69. 69
      70. 70
      71. 71
      72. 72
      73. 73
      74. 74
      75. 75
      76. 76
      77. 77
      78. 78
      79. 79
      80. 80
      81. 81
      82. 82
      83. 83
      84. 84
      85. 85
      86. 86
      87. 87
      88. 88
      89. 89
      90. 90
      91. 91
      92. 92
      93. 93
      94. 94
      95. 95
      96. 96
      97. 97
      98. 98
      99. 99
      100. 100
      101. 101
      102. 102
      103. 103
      104. 104
      105. 105
      106. 106
      107. 107
    9. REFERENCES

Data Science for JHU DIBBs Project

Last modified
Table of contents
  1. Story
  2. Sharing Best Practices for the Implementation of Big Data Applications in Government and Science Communities
    1. Abstract
    2. I. Introduction
    3. II. Organization of Working Group
      1. A. Mission Statement
      2. B. Framework of Activities
    4. III. Keynote and Panel Discussion
    5. V. Conclusion
    6. Acknowledgements
    7. References
  3. Slides
    1. Slide 1 Challenges and Solutions for Big Data in the Public Sector
    2. Slide 2 Overview
    3. Slide 3 Mission Statement
    4. Slide 4 Decisions = Science + Art: The Challenger Accident
    5. Slide 5 Fourth Paradigm and Fourth Question
    6. Slide 6 Symposium on Predictive Analytics for Defense and Government
    7. Slide 7 NIH Data Commons
    8. Slide 8  Best Practices for Data: A Biologists View
    9. Slide 9 Examples of Data Publications in Data Browsers for Senior Government People
    10. Slide 10 Data Science Central: Meteors Descriptive and Predictive​
    11. Slide 11 Data Science for JHU DIBBs Project: Knowledge Bases
    12. Slide 12 Data Science for JHU DIBBs Project​: Analytics & Visualizations
    13. Slide 13 Cover Page-Performance Analytics: FDA TRACK
    14. Slide 14 October 6th Meetup Agenda
  4. Spotfire Dashboard
  5. Research Notes
  6. From SkyServer to SciServer: The JHU DIBBs Project
    1. Agenda
    2. 1. Introduction (Alex)
      1. Big Data in Science
      2. Science is Changing
      3. Gray's Law of Data Engineering
      4. Scientific Data Analysis Today
      5. Exponential Data Growth
      6. Data Access is Hitting a Wall
      7. Non-Incremental Changes
    3. 2. Background, Vision and Goals (Alex)
      1. Why I Astronomy Interesting?
      2. Sloan Digital Sky Survey (SDSS)
      3. Skyserver
      4. Impact of Sky Surveys
      5. GalaxyZoo
      6. The SDSS Genealogy
      7. Oncospace
      8. Life Under Your Feet
      9. Cumulative Sensor Data
      10. Data in HPC Simulations
      11. Immersive Turbulence
      12. Daily Usage
      13. Simulations in the DB
      14. Scalable Data-Intensive Analysis
      15. The Long Tail
      16. JHU Data-Scope
      17. SciServer
    4. 3. Science Collaboration (Alex)
      1. Collaborative Science Projects 1
      2. Collaborative Science Projects 2
      3. The SDSS "CAStle"
      4. How Skyserver Changed Astronomy
    5. 4. SDSS Unification (Ani)
      1. The SDSS Genealogy (Updated)
      2. The Broad Impact of SDSS
      3. Reusable Building Blocks 1
      4. Reusable Building Blocks 2
      5. SkyServer
      6. ImgCutout
      7. CAS Jobs
      8. SciDrive & Portal
      9. SkyQuery
    6. 5. Project Operations, Roadmap and Progress (Mike)
      1. Project Management
      2. Teams and Governance
      3. Roadmap
      4. High Level Plan
      5. High Level Roadmap
      6. Architectural Development
      7. Architecture - At Project Start
      8. Architecture - Now
      9. Architecture - 18 Months
      10. Architecture - 3 Years
      11. SkyQuery Cluster Configuration
      12. Progress Overview (1)
      13. Progress Overview (2)
      14. SDSS Data Migration Progress
      15. Unified SDSS Website
      16. Progress on GLUSEEN
      17. Process Improvement
    7. 6. Outreach and Collaboration (Jordan)
      1. Branding and Website
      2. Community Engagement
      3. Training
      4. User Feedback
      5. Citizen Science
      6. Student Notebook
      7. Beyond SDSS SkyServer
      8. New Educational Activities
      9. DIBBs Partner Collaboration
      10. Trends
    8. 7. Summary (Alex)
      1. Summary
      2. Henry Ford Quote
      3. Contacts
  7. The Sloan Digital Sky Survey: Mapping the Universe
    1. Acknowledgments
    2. SDSS has been supported by
  8. Explore
    1. Data Access
      1. Current and Future Data
        1. Accessing the Data
      2. Past Data Releases
      3. Working with SDSS Data
    2. SDSS-III Data Release 10
      1. The Tenth SDSS Data Release (DR10)
      2. Using DR10
      3. Acknowledging DR10
    3. Scope of DR10
      1. The Scope of DR10
      2. Optical Data
      3. Optical imaging data statistics
      4. Optical spectroscopic data statistics
      5. Infrared (APOGEE) Data
      6. Infrared (APOGEE) spectroscopic data statistics
    4. DR10 Tutorials
      1. Tutorials
        1. Getting Started
        2. Getting Data for Individual Objects
        3. Searching for Data
        4. Downloading Data
        5. Reading SDSS Data Files
        6. Working with APOGEE
        7. Working with SEGUE
  9. Learn
    1. Surveys
      1. Sloan Digital Sky Surveys
      2. SDSS-IV: Current Surveys (2014-2020)
        1. APOGEE-2
        2. eBOSS
        3. MaNGA
      3. SDSS-III: Prior Surveys (2008-2014)
        1. APOGEE
        2. BOSS
        3. MARVELS
        4. SEGUE-2
      4. SDSS-I/II: Prior Surveys (2000-2008)
        1. Legacy
        2. Supernova
        3. SEGUE-1
    2. Instruments
      1. Telescopes
        1. The Sloan Foundation 2.5m Telescope at Apache Point Observatory
        2. The Irénée du Pont Telescope at Las Campanas Observatory
      2. Spectrographs
        1. eBOSS / BOSS
        2. APOGEE-2 / APOGEE
        3. MaNGA
        4. MARVELS
      3. Camera
    3. Education
      1. SDSS: Data For All
      2. SDSS News
      3. Connect with Us
      4. Citizen Science with SDSS
      5. SDSS for Educators
      6. Introduction to SDSS
      7. SDSS Education Group
      8. People
    4. Results & Science
      1. Science Results
        1. Cosmology
        2. Quasars
        3. Galaxies
        4. The Local Group
        5. The Milky Way
        6. Stars
        7. Solar System
        8. Other Science Results
      2. Press Releases
      3. SDSS Science Blog
      4. Publications
  10. Collaboration
    1. Policies
      1. Publication Policy
      2. External Collaborators
      3. Survey Science Teams & Working Groups
      4. Image Use Policy
    2. SDSS Institutions
      1. Full Member Institutions
      2. Associate Member Institutions
      3. Participation Groups
    3. Official SDSS Acknowledgment
  11. About
    1. How to Cite SDSS
    2. Image Use Policy
    3. Publication Policy
    4. Contact Us
      1. SDSS Collaboration
        1. Director
        2. Project Scientist
        3. Scientific Spokesperson
        4. Education and Public Outreach Director
      2. Contacting SDSS-III
        1. Director
        2. Project Scientist
        3. Scientific Spokesperson
        4. Education and Public Outreach Director
      3. Survey Contacts
        1. SDSS-IV Surveys
          1. APOGEE-2
          2. eBOSS
          3. MaNGA  
        2. SDSS-III Surveys
          1. APOGEE
          2. BOSS
          3. MARVELS
          4. SEGUE-2
  12. THE TENTH DATA RELEASE OF THE SLOAN DIGITAL SKY SURVEY: FIRST SPECTROSCOPIC DATA FROM THE SDSS-III APACHE POINT OBSERVATORY GALACTIC EVOLUTION EXPERIMENT
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. SCOPE OF DR10
      1. Table 1 Contents of DR10
        1. Optical Imaging a
        2. APOGEE Spectroscopy
        3. BOSS Spectroscopy
        4. All Optical Spectroscopy from SDSS up through DR10
      2. Figure 1. The distribution on the sky of all APOGEE DR10 pointings in Galactic coordinates
      3. Figure 2. The distribution on the sky of all SDSS imaging
    4. 3. THE APACHE POINT OBSERVATORY GALAXY EVOLUTION EXPERIMENT (APOGEE)
      1. 3.1. Overview of APOGEE
      2. 3.2. The APOGEE Instrument and Observations
        1. Figure 3. (top) A 2D spectrogram from the APOGEE instrument
        2. Figure 4. The distribution of number of spectroscopic visits for APOGEE stars included in DR10
        3. Figure 5. The distribution of time between visits for APOGEE stars, useful for determining the sensitivity to radial velocity variations due to binarity
        4. Figure 6. Reported S/N per pixel of APOGEE DR10 co-added stellar spectra
        5. Figure 7. S/N per pixel of spectra of stars as a function of their apparent H-band magnitude
      3. 3.3. APOGEE Main and Ancillary Targets
        1. Figure 8. Two-dimensional histogram of the APOGEE DR10 stars
      4. 3.4. APOKASC
      5. 3.5. APOGEE Data Analysis
        1. Figure 9. Typical APOGEE spectra at high S/N
      6. 3.6. Issues with APOGEE Spectra
      7. 3.7. APOGEE Stellar Parameter and Chemical Abundances Pipeline (ASPCAP)
        1. Figure 10. (upper lines) An example ASPCAP fit (red) to a typical APOGEE co-added stellar spectrum (black). (lower lines)
        2. 3.7.1. Parameter Accuracies
        3. 3.7.2. ASPCAP Outputs
          1. Figure 11. The one-dimensional and two-dimensional distributions of APOGEE stellar parameters
          2. Figure 12. ASPCAP log g vs. Teff with the points color-coded by [M/H]
      8. 3.8. APOGEE Data Products
    5. 4. THE BARYON OSCILLATION SPECTROSCOPIC SURVEY (BOSS)
      1. Figure 13. BOSS DR10 spectroscopic sky coverage in the Northern Galactic Cap (top) and Southern Galactic Cap (bottom)
      2. Figure 14. The distribution of BOSS DR10 spectroscopic objects versus lookback time
      3. Figure 15. N(z) of SDSS-III BOSS spectra in DR10 compared to that of the SDSS-I/II Legacy spectra for galaxies (top) and quasars (bottom).
      4. 4.1. A New Quasar Target Class in DR10
      5. 4.2. Updates to BOSS Data Processing
      6. 4.3. Updates to BOSS Galaxy Stellar Population Parameters
    6. 5. DATA DISTRIBUTION
    7. 6. FUTURE
    8. Footnotes
      1. 1
      2. 2
      3. 3
      4. 4
      5. 5
      6. 6
      7. 7
      8. 8
      9. 9
      10. 10
      11. 11
      12. 12
      13. 13
      14. 14
      15. 15
      16. 16
      17. 17
      18. 18
      19. 19
      20. 20
      21. 21
      22. 22
      23. 23
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      25. 25
      26. 26
      27. 27
      28. 28
      29. 29
      30. 30
      31. 31
      32. 32
      33. 33
      34. 34
      35. 35
      36. 36
      37. 37
      38. 38
      39. 39
      40. 40
      41. 41
      42. 42
      43. 43
      44. 44
      45. 45
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      50. 50
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      52. 52
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      59. 59
      60. 60
      61. 61
      62. 62
      63. 63
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      65. 65
      66. 66
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      71. 71
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      73. 73
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      81. 81
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      90. 90
      91. 91
      92. 92
      93. 93
      94. 94
      95. 95
      96. 96
      97. 97
      98. 98
      99. 99
      100. 100
      101. 101
      102. 102
      103. 103
      104. 104
      105. 105
      106. 106
      107. 107
    9. REFERENCES

  1. Story
  2. Sharing Best Practices for the Implementation of Big Data Applications in Government and Science Communities
    1. Abstract
    2. I. Introduction
    3. II. Organization of Working Group
      1. A. Mission Statement
      2. B. Framework of Activities
    4. III. Keynote and Panel Discussion
    5. V. Conclusion
    6. Acknowledgements
    7. References
  3. Slides
    1. Slide 1 Challenges and Solutions for Big Data in the Public Sector
    2. Slide 2 Overview
    3. Slide 3 Mission Statement
    4. Slide 4 Decisions = Science + Art: The Challenger Accident
    5. Slide 5 Fourth Paradigm and Fourth Question
    6. Slide 6 Symposium on Predictive Analytics for Defense and Government
    7. Slide 7 NIH Data Commons
    8. Slide 8  Best Practices for Data: A Biologists View
    9. Slide 9 Examples of Data Publications in Data Browsers for Senior Government People
    10. Slide 10 Data Science Central: Meteors Descriptive and Predictive​
    11. Slide 11 Data Science for JHU DIBBs Project: Knowledge Bases
    12. Slide 12 Data Science for JHU DIBBs Project​: Analytics & Visualizations
    13. Slide 13 Cover Page-Performance Analytics: FDA TRACK
    14. Slide 14 October 6th Meetup Agenda
  4. Spotfire Dashboard
  5. Research Notes
  6. From SkyServer to SciServer: The JHU DIBBs Project
    1. Agenda
    2. 1. Introduction (Alex)
      1. Big Data in Science
      2. Science is Changing
      3. Gray's Law of Data Engineering
      4. Scientific Data Analysis Today
      5. Exponential Data Growth
      6. Data Access is Hitting a Wall
      7. Non-Incremental Changes
    3. 2. Background, Vision and Goals (Alex)
      1. Why I Astronomy Interesting?
      2. Sloan Digital Sky Survey (SDSS)
      3. Skyserver
      4. Impact of Sky Surveys
      5. GalaxyZoo
      6. The SDSS Genealogy
      7. Oncospace
      8. Life Under Your Feet
      9. Cumulative Sensor Data
      10. Data in HPC Simulations
      11. Immersive Turbulence
      12. Daily Usage
      13. Simulations in the DB
      14. Scalable Data-Intensive Analysis
      15. The Long Tail
      16. JHU Data-Scope
      17. SciServer
    4. 3. Science Collaboration (Alex)
      1. Collaborative Science Projects 1
      2. Collaborative Science Projects 2
      3. The SDSS "CAStle"
      4. How Skyserver Changed Astronomy
    5. 4. SDSS Unification (Ani)
      1. The SDSS Genealogy (Updated)
      2. The Broad Impact of SDSS
      3. Reusable Building Blocks 1
      4. Reusable Building Blocks 2
      5. SkyServer
      6. ImgCutout
      7. CAS Jobs
      8. SciDrive & Portal
      9. SkyQuery
    6. 5. Project Operations, Roadmap and Progress (Mike)
      1. Project Management
      2. Teams and Governance
      3. Roadmap
      4. High Level Plan
      5. High Level Roadmap
      6. Architectural Development
      7. Architecture - At Project Start
      8. Architecture - Now
      9. Architecture - 18 Months
      10. Architecture - 3 Years
      11. SkyQuery Cluster Configuration
      12. Progress Overview (1)
      13. Progress Overview (2)
      14. SDSS Data Migration Progress
      15. Unified SDSS Website
      16. Progress on GLUSEEN
      17. Process Improvement
    7. 6. Outreach and Collaboration (Jordan)
      1. Branding and Website
      2. Community Engagement
      3. Training
      4. User Feedback
      5. Citizen Science
      6. Student Notebook
      7. Beyond SDSS SkyServer
      8. New Educational Activities
      9. DIBBs Partner Collaboration
      10. Trends
    8. 7. Summary (Alex)
      1. Summary
      2. Henry Ford Quote
      3. Contacts
  7. The Sloan Digital Sky Survey: Mapping the Universe
    1. Acknowledgments
    2. SDSS has been supported by
  8. Explore
    1. Data Access
      1. Current and Future Data
        1. Accessing the Data
      2. Past Data Releases
      3. Working with SDSS Data
    2. SDSS-III Data Release 10
      1. The Tenth SDSS Data Release (DR10)
      2. Using DR10
      3. Acknowledging DR10
    3. Scope of DR10
      1. The Scope of DR10
      2. Optical Data
      3. Optical imaging data statistics
      4. Optical spectroscopic data statistics
      5. Infrared (APOGEE) Data
      6. Infrared (APOGEE) spectroscopic data statistics
    4. DR10 Tutorials
      1. Tutorials
        1. Getting Started
        2. Getting Data for Individual Objects
        3. Searching for Data
        4. Downloading Data
        5. Reading SDSS Data Files
        6. Working with APOGEE
        7. Working with SEGUE
  9. Learn
    1. Surveys
      1. Sloan Digital Sky Surveys
      2. SDSS-IV: Current Surveys (2014-2020)
        1. APOGEE-2
        2. eBOSS
        3. MaNGA
      3. SDSS-III: Prior Surveys (2008-2014)
        1. APOGEE
        2. BOSS
        3. MARVELS
        4. SEGUE-2
      4. SDSS-I/II: Prior Surveys (2000-2008)
        1. Legacy
        2. Supernova
        3. SEGUE-1
    2. Instruments
      1. Telescopes
        1. The Sloan Foundation 2.5m Telescope at Apache Point Observatory
        2. The Irénée du Pont Telescope at Las Campanas Observatory
      2. Spectrographs
        1. eBOSS / BOSS
        2. APOGEE-2 / APOGEE
        3. MaNGA
        4. MARVELS
      3. Camera
    3. Education
      1. SDSS: Data For All
      2. SDSS News
      3. Connect with Us
      4. Citizen Science with SDSS
      5. SDSS for Educators
      6. Introduction to SDSS
      7. SDSS Education Group
      8. People
    4. Results & Science
      1. Science Results
        1. Cosmology
        2. Quasars
        3. Galaxies
        4. The Local Group
        5. The Milky Way
        6. Stars
        7. Solar System
        8. Other Science Results
      2. Press Releases
      3. SDSS Science Blog
      4. Publications
  10. Collaboration
    1. Policies
      1. Publication Policy
      2. External Collaborators
      3. Survey Science Teams & Working Groups
      4. Image Use Policy
    2. SDSS Institutions
      1. Full Member Institutions
      2. Associate Member Institutions
      3. Participation Groups
    3. Official SDSS Acknowledgment
  11. About
    1. How to Cite SDSS
    2. Image Use Policy
    3. Publication Policy
    4. Contact Us
      1. SDSS Collaboration
        1. Director
        2. Project Scientist
        3. Scientific Spokesperson
        4. Education and Public Outreach Director
      2. Contacting SDSS-III
        1. Director
        2. Project Scientist
        3. Scientific Spokesperson
        4. Education and Public Outreach Director
      3. Survey Contacts
        1. SDSS-IV Surveys
          1. APOGEE-2
          2. eBOSS
          3. MaNGA  
        2. SDSS-III Surveys
          1. APOGEE
          2. BOSS
          3. MARVELS
          4. SEGUE-2
  12. THE TENTH DATA RELEASE OF THE SLOAN DIGITAL SKY SURVEY: FIRST SPECTROSCOPIC DATA FROM THE SDSS-III APACHE POINT OBSERVATORY GALACTIC EVOLUTION EXPERIMENT
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. SCOPE OF DR10
      1. Table 1 Contents of DR10
        1. Optical Imaging a
        2. APOGEE Spectroscopy
        3. BOSS Spectroscopy
        4. All Optical Spectroscopy from SDSS up through DR10
      2. Figure 1. The distribution on the sky of all APOGEE DR10 pointings in Galactic coordinates
      3. Figure 2. The distribution on the sky of all SDSS imaging
    4. 3. THE APACHE POINT OBSERVATORY GALAXY EVOLUTION EXPERIMENT (APOGEE)
      1. 3.1. Overview of APOGEE
      2. 3.2. The APOGEE Instrument and Observations
        1. Figure 3. (top) A 2D spectrogram from the APOGEE instrument
        2. Figure 4. The distribution of number of spectroscopic visits for APOGEE stars included in DR10
        3. Figure 5. The distribution of time between visits for APOGEE stars, useful for determining the sensitivity to radial velocity variations due to binarity
        4. Figure 6. Reported S/N per pixel of APOGEE DR10 co-added stellar spectra
        5. Figure 7. S/N per pixel of spectra of stars as a function of their apparent H-band magnitude
      3. 3.3. APOGEE Main and Ancillary Targets
        1. Figure 8. Two-dimensional histogram of the APOGEE DR10 stars
      4. 3.4. APOKASC
      5. 3.5. APOGEE Data Analysis
        1. Figure 9. Typical APOGEE spectra at high S/N
      6. 3.6. Issues with APOGEE Spectra
      7. 3.7. APOGEE Stellar Parameter and Chemical Abundances Pipeline (ASPCAP)
        1. Figure 10. (upper lines) An example ASPCAP fit (red) to a typical APOGEE co-added stellar spectrum (black). (lower lines)
        2. 3.7.1. Parameter Accuracies
        3. 3.7.2. ASPCAP Outputs
          1. Figure 11. The one-dimensional and two-dimensional distributions of APOGEE stellar parameters
          2. Figure 12. ASPCAP log g vs. Teff with the points color-coded by [M/H]
      8. 3.8. APOGEE Data Products
    5. 4. THE BARYON OSCILLATION SPECTROSCOPIC SURVEY (BOSS)
      1. Figure 13. BOSS DR10 spectroscopic sky coverage in the Northern Galactic Cap (top) and Southern Galactic Cap (bottom)
      2. Figure 14. The distribution of BOSS DR10 spectroscopic objects versus lookback time
      3. Figure 15. N(z) of SDSS-III BOSS spectra in DR10 compared to that of the SDSS-I/II Legacy spectra for galaxies (top) and quasars (bottom).
      4. 4.1. A New Quasar Target Class in DR10
      5. 4.2. Updates to BOSS Data Processing
      6. 4.3. Updates to BOSS Galaxy Stellar Population Parameters
    6. 5. DATA DISTRIBUTION
    7. 6. FUTURE
    8. Footnotes
      1. 1
      2. 2
      3. 3
      4. 4
      5. 5
      6. 6
      7. 7
      8. 8
      9. 9
      10. 10
      11. 11
      12. 12
      13. 13
      14. 14
      15. 15
      16. 16
      17. 17
      18. 18
      19. 19
      20. 20
      21. 21
      22. 22
      23. 23
      24. 24
      25. 25
      26. 26
      27. 27
      28. 28
      29. 29
      30. 30
      31. 31
      32. 32
      33. 33
      34. 34
      35. 35
      36. 36
      37. 37
      38. 38
      39. 39
      40. 40
      41. 41
      42. 42
      43. 43
      44. 44
      45. 45
      46. 46
      47. 47
      48. 48
      49. 49
      50. 50
      51. 51
      52. 52
      53. 53
      54. 54
      55. 55
      56. 56
      57. 57
      58. 58
      59. 59
      60. 60
      61. 61
      62. 62
      63. 63
      64. 64
      65. 65
      66. 66
      67. 67
      68. 68
      69. 69
      70. 70
      71. 71
      72. 72
      73. 73
      74. 74
      75. 75
      76. 76
      77. 77
      78. 78
      79. 79
      80. 80
      81. 81
      82. 82
      83. 83
      84. 84
      85. 85
      86. 86
      87. 87
      88. 88
      89. 89
      90. 90
      91. 91
      92. 92
      93. 93
      94. 94
      95. 95
      96. 96
      97. 97
      98. 98
      99. 99
      100. 100
      101. 101
      102. 102
      103. 103
      104. 104
      105. 105
      106. 106
      107. 107
    9. REFERENCES

Story

Data Science for JHU DIBBs Project

The Federal Big Data Working Group Meetup participated in the August 19, 2014, FASTER CoP Presentation at NSF on From SkyServer to SciServer: The JHU DIBBs Project, featuring JHU Professor Alex Szalay.

The Data Infrastructure Building Blocks (DIBBs) program is an integral part of Cyberinfrastructure Framework for 21st Century Science and Engineering (CIF21). The DIBBs program encourages development of robust and shared data-centric cyberinfrastructure capabilities to accelerate interdisciplinary and collaborative research in areas of inquiry stimulated by data.

Effective solutions will bring together cyberinfrastructure expertise and domain researchers, to ensure that the resulting cyberinfrastructure components address the researchers' data needs.  The activities address the data challenges arising in a disciplinary or cross-disciplinary context.

The Slides: From SkyServer to SciServer: The JHU DIBBs Project, are shown below as in-line images and a WebCast was posted to YouTube.

Dr. Alexander Szalay said he liked our explanations of data science data publications because they would largely solve the metadata problem and would look at Spotfire (Dr. Ben Shneiderman's invention) as an example of a tool that is "both a microscope and a telescope for big data".

I mentioned the astronomy use case pilot we are doing with Professor Kirk Borne involving ontology, graph computing, and SciDB.org and he thought it was a great idea and would like to followup with us. So I think we will do a joint Meetup with Dr. Alexander Szalay.

Professor Alexander Szalay's, NSF FASTER CoP Presentation included Gray's Laws of Data Engineering:

  1. Scientific computing is revolving around data
  2. Need scale‐out solution for analysis
  3. Take the analysis to the data!
  4. Start with “20 queries”
  5. Go from “working to working”

Note that SDSS started with the TerraServer which was developed for USGS land images!

We plan to use Gray's Laws of Data Engineering (along with the Ontological Engineering we have been using) for DGI's First Annual Big Data Conference, October 9, 2014, as follows:

Session title: Challenges and Solutions for Big Data in the Public Sector

Moderator:

  • Dr. Brand Niemann, Director and Senior Data Scientist, Semantic Community and Co-organizer of the Federal Big Data Working Group Meetup
    • ​​See introductory Slides below with initial results for our Astronomy pilot

Panelists:

  • Dr. Kirk Borne, Professor of Astrophysics and Computational Science, George Mason University and
  • Dr. Tom Rindflesch, Information Research Specialist at Cognitive Science Branch, National Institutes for Health (NIH)
    • Semantic Data Science Team used ontology UMLS), graph computing (Cray URIKA), and scientific databases (MySQL) for Semantic Medline

We developed an initial SDSS Ontology from the recent FASTER NITRD presentation (August 19, 2014 - From SkyServer to SciServer: The JHU DIBBs Project) and new Web Site

The click trail to learn about SDSS and build a data science data publication was:

Excerpts: This website presents data from the Sloan Digital Sky Survey, a project to make a map of a large part of the universe. We would like to show you the beauty of the universe, and share with you our excitement as we build the largest map in the history of the world. The Sloan Digital Sky Survey is one of the most cited surveys in the history of astronomy. The SDSS has produced literally thousands of peer-reviewed publications in astronomy and other sciences - far too many to list one by one. The section below contains a page that queries the Astrophysics Data System (ADS) for all refereed papers that contain "Sloan Survey" or "SDSS" in the title or abstract, in decreasing order of the number of citations. Use the scrollbars in the section below to navigate through the list.

Excerpts: A small subset of the SkyServer database (about 1.3 GB SQL Server database) can fit (compressed) on a CD or be downloaded over the web. This includes the website and all the photo and spectrographic objects in a 6º square of the sky. This personal SkyServer fits on laptops and desktops. It is useful for experimenting with queries,for developing the web site, and for giving demos. Essentially, any classroom can have a mini-SkyServer per student. With disk technology improvements, a large slice of the public data will fit on a single disk by 2005.

So the attached spreadsheet contains the following:

  • Knowledge Base
  • SDSS Data Release Publications
  • Data Volume Table
  • Other SDSS Publications
  • Peer-reviewed Publications
  • Glossary of SDSS-III
  • DR10 Authors
  • Data Model Directory
  • Data Model File Index
  • Data Ecosystem

So far

The results of preparing and analyzing these data sets which illustrate Gray's Laws of Data Engineering are shown in the slides below:

Slide 8 Data Science for JHU DIBBs Project: Knowledge Bases

Slide 9 Data Science for JHU DIBBs Project: Analytics & Visualizations

Slide 10 Data Science for JHU DIBBs Project​: Conclusions

The Knowledge Bases are essentially ontologies which are then imported into Spotfire to create content, network, and data analytics that provide "a microscope into big data". If we create more Spotfire files from these data and put them into a cloud library, then we will have "a telescope into big data".

I have successfully downloaded the small subset of the SkyServer database (about 1.3 GB SQL Server database) and will strat to work with it next. JHU should create more of these so we can follow our manta: "The Best Way to get BIG DATA is By Starting Small".

MORE TO FOLLOW

Short Bios for Government Big Data Conference, October 9, 2014

Brand Niemann, former Senior Enterprise Architect & Data Scientist with the US EPA, works as a data scientist, produces data science products, and publishes data stories for Semantic Community, AOL Government, & Data Science & Data Visualization DC. He founded and co-organizes the Federal Big Data Working Group Meetup.

Dr. Kirk D. Borne is a Data Scientist and Professor of Astrophysics & Computational Science at George Mason University (since 2003). He does research, teaches, and advises students in the theory and practice of data science. He is also an active consultant to numerous organizations in data science and big data analytics.  He previously spent 18 years supporting large scientific data systems for NASA astrophysics missions, including the Hubble Space Telescope. He was identified in 2013 as the #1 Big Data influencer on Twitter at @KirkDBorne.

Thomas C. Rindflesch conducts research in natural language processing in the Lister Hill Center for Biomedical Communications at the National Library of Medicine. He leads a research group that focuses on developing semantic interpretation of biomedical text and exploiting results for innovative biomedical information management applications.

Sharing Best Practices for the Implementation of Big Data Applications in Government and Science Communities

Source: PDF

Joan L. Aron

Independent Consultant

Columbia, Maryland, U.S.A.

joanaron@ymail.com

 

Brand Niemann

Semantic Community

Fairfax, Virginia, U.S.A.

bniemann@cox.net

Abstract

The Federal Big Data Working Group supports the Federal Big Data Initiative but is not endorsed by the Federal Government or its agencies. This working group uses meetups with onsite and virtual participation to share best practices for the implementation of Big Data applications in government and science communities. Decision-makers and the scientific community interact with data science in order to take advantage of the Big Data transformation of how information is used in science, decision support, data discovery and data publishing. The working group federates use cases, data publications, solutions and technologies. The range of topics is illustrated in a keynote and panel discussion at a recent Big Data conference and in a summary of recent working group meetups.

Keywords—Big Data; use cases; data publications; semantic analysis; privacy; high-performance computation; scientific community; decision-makers

I. Introduction

The Federal Big Data Working Group supports the Federal Big Data Initiative but is not endorsed by the Federal Government or its agencies. This working group uses meetups with onsite and virtual participation to share best practices for the implementation of Big Data applications in government and science communities. The overarching theme is that Big Data has the power to fundamentally transform how information is managed and used. For example, new tools for semantic analysis can link quantitative data and text more effectively than keyword searches. The challenge for scientists and government program managers is to understand the benefits and the required infrastructure.

This report describes the organization of the working group to show how it fosters the sharing of best practices. The range of topics is illustrated in a keynote and panel discussion at a recent Big Data conference and in recent meetups.

II. Organization of Working Group

The Federal Big Data Working Group Meetup is a broad community of participants focused on Big Data products for the Federal Big Data Initiative. All are welcome to attend these Meetups and learn Big Data science from tutorials and be mentored in their university and professional work and proposal writing.

Currently around 300 participants (government and nongovernment) have signed up for the Federal Big Data Working Group. The kickoff meeting was held on January 7, 2014. Meetings are hosted by Xcelerate Solutions in McLean, Virginia once or twice a month with facilities for in-person and virtual participation.

Our Meetup presentations focus on answering four essential questions.

  • How were the data collected?
  • Where are the data stored?
  • What are the data results?
  • Does the data story persuade?

A. Mission Statement

Our mission supports the Federal Big Data Initiative but is not endorsed by the Federal Government or its agencies.

  • Our mission supports the Federal Digital Government Strategy, which is “treating all content as data,” so that Big Data is all of your content.
  • Our mission supports the establishment of a working group of Data Science Teams composed of Federal Government and non-Federal Government experts producing Big Data products.
  • Our mission supports the use of infrastructure for Meetup, which is the world’s largest network of local groups to revitalize local communities and help people around the world self-organize.

B. Framework of Activities

  • Provide leadership of the Semantic Data Science Team that produced Semantic Medline running on the Yarc Data Graph Appliance.
  • Organize a Community of Data Scientists and Related Fields to focus on treating all of your content as “Big Data” by founding and coorganizing the Federal Big Data Working Group Meetup.
  • Prepare a graduate class for GMU (George Mason University) entitled “Practical Data Science for Data Scientists.”
  • Follow CRISP – DM (Cross Industry Standard Process for Data Mining) to build a Data Science Knowledge Base.
  • Mine prominent scientific journals for data policy, data bases and data results that can be reused, such as Data Science and Digital Earth scientific journals for the CODATA (Committee on Data for Science and Technology) International Workshop on Big Data for International Scientific Programmes (June 8-9, 2014, in Beijing).
  • Participate in the Data FAIRport (Findable, Accessible, Interoperable and Reusable) with “Data Publication in Data Browsers.”
  • Seek National Science Foundation funding for sustained data science for data publications work over a period of years.
  • Provide data stories that persuade and presentation materials for public education conferences, such as the COM.BigData Conference 2014 (August 4-6, 2014 in Washington, DC).

III. Keynote and Panel Discussion

Dr. Brand Niemann made a keynote presentation on the evolution of the Federal Big Data Initiative at COM.Big Data 2014, the first international summit on Big Data computing (August 4 – 6, 2014 in Washington, DC). The timeline began with the Report of the Interagency Working Group on Digital Data to the National Science and Technology Council in January of 2009. Production of data publications in data browsers was a major focus with examples of public reports related to the work of ten senior government officials. There was a note of urgency to address the fact that the federal government is lagging behind the private sector in the application of Big Data techniques and technologies

Following up on the keynote, six members of the working group presented a panel on the uses of Big Data for knowledge discovery and decision support and the challenges in developing applications. The six panelists in sequence were Dr. Katherine Goodier, Dr. Chuck Rehberg, Dr. Kirk Borne, Dr. Tom Rindflesch, Ms. Mary Galvin and Dr. Joan L. Aron. The key points of these presentations are briefly summarized.

1) The federal government has greater needs for aggregating data while maintaining compliance with privacy and security requirements. Cognitive metadata, which is the metadata coming from enhancing machine learning with our human perception, reasoning or intuition, can be used for personalization purposes and conversely for protecting PII (personally identifiable information).

2) A new technology for natural language understanding can be used to find high-value information in a large body of texts, such as a collection of agency reports, with little specialized training.

3) A semantic Medline for searching biomedical research literature uses hardware built for RDF (Resource Description Framework) triples in a graph database and semantic processing developed at the National Library of Medicine.

4) The fundamental concepts of data science demonstrate the benefits and the pitfalls. The first mile is the hardest because of ubiquitous heterogeneous data but the last mile of producing actionable intelligence is also the hardest.

5) A high-performance computing cluster environment is in use for searching public records, patent data, case law and news articles

6) Use cases with a focus on environment and Earth system science illustrate achievements and challenges for the use of Big Data in data publishing, data access, data discovery and decision support. Workforce development for the scientific community and decision-makers should help them to work more effectively with data science.

IV. Recent Meetups

Recent meetups illustrate the range of topics addressed as the working group federates use cases, data publications, solutions and technologies. A brief summary is shown.

1) The kickoff meetup was held on January 7, 2014. Data stories were Tutorials Start: Practical Data Science for Data Scientists and Semantic Big Data Science Application for Semantic Medline on the YarcData Graph Appliance.

2) The second meetup was held on February 4, 2014. The topics were Healthcare.gov Data Science and Be Informed Prototype Video.

3) The third meetup was held on February 18, 2014. The topics were the Evolution of Semantic Technologies: The Value of Merging Smart Data with Big Data as well as Modus Operandi Semantic Knowledge Base.

4) The fourth meetup was held on March 4, 2014. This meetup was joint with the NSF (National Science Foundation) and the NIH (National Institutes of Health) on biomedical Big Data research. It referenced the NIST (National Institute of Standards and Technology) Data Science Symposium, Euretos BRAIN, and Data Culture at the NIH.

5) The fifth meetup was held on March 18, 2014. The aim was to continue the data science tutorial and learn about Bigdata SYSTAP. It included a discussion of Bigdata SYSTAP Literature Survey of Graph Databases and Graph Databases

6) The sixth meetup was held on April 1, 2014. Marc Smith presented on Network Analytics and Kate Goodier presented on Big Data Privacy. The data stories were Data Science for VIVO, NodeXL and Sci2 for Data Science and the Big Data Privacy Workshop.

7) The seventh meetup was held on April 15, 2014. Kate Goodier presented on Cognitive Metadata. Other topics were Cambridge Semantics, Insider Trading and Data Science for FIBO (Financial Industry Business Ontology).

8) The eighth meetup was held on May 6, 2014. Joan Aron and Brand Niemann presented on EPA (Environmental Protection Agency)/NASA (National Aeronautics and Space Administration) Climate-Environmental Data Analytics. Another topic was A Redesigned, Open Data.gov. The data stories were Data Science for EPA Air Data, Chesapeake Bay Program and NASA Big Data.

9) The ninth meetup was held on May 20, 2014. The topics were Data Science at GMU as well as Elsevier Research Data Services. The data stories were A Data Science Big Mechanism for DARPA (Defense Advanced Research Projects Agency) and Data Science for Climate Change Impacts

10) The tenth meetup was held on June 2, 2014. The topics were Ontology Summit 2014 Postmortem and Reading & Reasoning with Semantic Insights for the DARPA Big Mechanism. The data stories were Ontology for Big Data, Big Data Science for CODATA and Semantic Insights.

11) The eleventh meetup was held on June 30, 2014. The toipcs were the MIT (Massachusetts Institute of Technology) Big Data Initiative: Sam Madden and Current Elephants: Michael Stonebraker. The data stories were the MIT Big Data Initiative (bigdata@CAIL) and the new Intel Science and Technology Center for Big Data, Sam Madden and Why the current “elephants” are good at nothing, Data Tamer, and data integration issues, Michael Stonebraker and Workshops on Extremely Large Databases.

12) The twelfth meetup was held on July 7, 2014. The topics were the Data Science of White House Big Data Review and Brooke Aker’s presentation on a Big Data Lens on OpenFDA (Food and Drug Administration). The data stories were about Mary Galvin and HPCC (High Performance Computing Cluster) Systems of Lexis-Nexis Risk Solutions, Georgetown University McCourt School of Public Policy’s Massive Data Institute and Katherine Goodier of Xcelerate Solutions and the Legislative Data and Transparency Conference. In addition, Chuck Rehberg presented Part II on SIRA technology from Semantic Insights for real-time document research requiring little specialized training

13) The thirteenth meetup was held on August 4, 2014 at the COM.Big Data 2014 conference in Washington, DC. The working group presented the keynote and panel discussion described in Section III.

More information about the working group may be found at http://www.meetup.com/Federal-Big-Data-Working-Group. Products from the working group, including presentations from meetups and conferences mentioned in this report, are available at http://semanticommunity.info.

V. Conclusion

The Federal Big Data Working Group is making a unique contribution to the Federal Big Data Initiative. This working group has formed a diverse community to share best practices between different disciplines and between the producers and users of new technologies. Its activities are aiding the implementation of Big Data applications in government and science communities.

Acknowledgements

We appreciate the congenial and productive interaction of members of the Federal Big Data Working Group in advancing the understanding of Big Data applications in science and government communities. We give special thanks to Dr. Katherine Goodier at Xcelerate Solutions for providing leadership and hosting the meetups.

References

Slides

Slides

Slide 1 Challenges and Solutions for Big Data in the Public Sector

http://semanticommunity.info/
http://www.meetup.com/Federal-Big-Data-Working-Group/
http://semanticommunity.info/Data_Science/Federal_Big_Data_Working_Group_Meetup

BrandNiemann10092014Slide1.PNG

Slide 3 Mission Statement

BrandNiemann10092014Slide3.PNG

Slide 5 Fourth Paradigm and Fourth Question

http://commons.wikimedia.org/wiki/Fi..._telescope.jpg

BrandNiemann10092014Slide5.PNG

Slide 6 Symposium on Predictive Analytics for Defense and Government

http://semanticommunity.info/Data_Science/Data_Science_Central#9_.22must_read.22_articles

BrandNiemann10092014Slide6.PNG

Slide 9 Examples of Data Publications in Data Browsers for Senior Government People

Semantic Community NSF BIG DATA PROPOSAL

BrandNiemann10092014Slide9.PNG

Slide 10 Data Science Central: Meteors Descriptive and Predictive​

Web Player

BrandNiemann10092014Slide10.PNG

Slide 11 Data Science for JHU DIBBs Project: Knowledge Bases

Data Science for JHU DIBBs Project and SDSS.xlsx

BrandNiemann10092014Slide11.PNG

Slide 12 Data Science for JHU DIBBs Project​: Analytics & Visualizations

Web Player

 

BrandNiemann10092014Slide12.PNG

Slide 13 Cover Page-Performance Analytics: FDA TRACK

Web Player

BrandNiemann10092014Slide13.PNG

Slide 14 October 6th Meetup Agenda

http://www.meetup.com/Federal-Big-Da...nts/209068792/

BrandNiemann10092014Slide14.PNG

Spotfire Dashboard

Slide 9.png

For Internet Explorer Users and Those Wanting Full Screen Display Use: Web Player Get Spotfire for iPad App

Error: Embedded data could not be displayed. Use Google Chrome

Research Notes

August 19, 2014 - From SkyServer to SciServer: The JHU DIBBs Project

Update on the Johns Hopkins University Data Infrastructure Building Blocks (DIBBs) Project latest developments

Webcast: http://youtu.be/Q35Xeh3KDTk

An informative presentation and discussion with Dr. Alexander Szalay who shared his perspectives regarding the Johns Hopkins University Data Infrastructure Building Blocks (DIBBs) Project. Dr. Szalay is the Alumni Centennial Professor of Astronomy at the Johns Hopkins University, and also a professor in the Department of Computer Science.

From SkyServer to SciServer: The JHU DIBBs Project

Slides: https://www.nitrd.gov/nitrdgroups/im...stribution.pdf (PDF)

AlexSzalay08192014Slide1.png

Agenda

AlexSzalay08192014Slide2.png

 

1. Introduction (Alex)

Big Data in Science

AlexSzalay08192014Slide3.png

Science is Changing

AlexSzalay08192014Slide4.png

Gray's Law of Data Engineering

AlexSzalay08192014Slide5.png

Scientific Data Analysis Today

AlexSzalay08192014Slide6.png

Exponential Data Growth

AlexSzalay08192014Slide7.png

Data Access is Hitting a Wall

AlexSzalay08192014Slide8.png

Non-Incremental Changes

AlexSzalay08192014Slide9.png

2. Background, Vision and Goals (Alex)

Why I Astronomy Interesting?

AlexSzalay08192014Slide10.png

Sloan Digital Sky Survey (SDSS)

AlexSzalay08192014Slide11.png

Skyserver

AlexSzalay08192014Slide12.png

Impact of Sky Surveys

AlexSzalay08192014Slide13.png

GalaxyZoo

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The SDSS Genealogy

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Oncospace

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Life Under Your Feet

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Cumulative Sensor Data

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Data in HPC Simulations

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Immersive Turbulence

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Daily Usage

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Simulations in the DB

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Scalable Data-Intensive Analysis

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The Long Tail

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JHU Data-Scope

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SciServer

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3. Science Collaboration (Alex)

Collaborative Science Projects 1

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Collaborative Science Projects 2

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The SDSS "CAStle"

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How Skyserver Changed Astronomy

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4. SDSS Unification (Ani)

The SDSS Genealogy (Updated)

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The Broad Impact of SDSS

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Reusable Building Blocks 1

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Reusable Building Blocks 2

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SkyServer

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ImgCutout

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CAS Jobs

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SciDrive & Portal

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SkyQuery

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5. Project Operations, Roadmap and Progress (Mike)

Project Management

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Teams and Governance

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Roadmap

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High Level Plan

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High Level Roadmap

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Architectural Development

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Architecture - At Project Start

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Architecture - Now

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Architecture - 18 Months

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Architecture - 3 Years

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SkyQuery Cluster Configuration

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Progress Overview (1)

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Progress Overview (2)

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SDSS Data Migration Progress

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Unified SDSS Website

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Progress on GLUSEEN

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Process Improvement

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6. Outreach and Collaboration (Jordan)

Branding and Website

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Community Engagement

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Training

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User Feedback

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Citizen Science

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Student Notebook

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Beyond SDSS SkyServer

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New Educational Activities

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DIBBs Partner Collaboration

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Trends

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7. Summary (Alex)

Summary

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Henry Ford Quote

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Contacts

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The Sloan Digital Sky Survey: Mapping the Universe

Source: http://www.sdss.org

The Sloan Digital Sky Survey has created the most detailed three-dimensional maps of the Universe ever made, with deep multi-color images of one third of the sky, and spectra for more than three million astronomical objects. Learn and explore all phases and surveys—past, present, and future—of the SDSS.

The SDSS began regular survey operations in 2000, after a decade of design and construction.  It has progressed through several phases, SDSS-I (2000-2005), SDSS-II (2005-2008), SDSS-III (2008-2014), and SDSS-IV (2014-).  Each of these phases has involved multiple surveys with interlocking science goals.  The three surveys that comprise SDSS-IV are eBOSS, APOGEE-2, and MaNGA, described at the links below.  You can find more about the surveys of SDSS I-III by following the Prior Surveys link.

Acknowledgments

Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. The SDSS web site is www.sdss.org.

SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU) / University of Tokyo, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), Max-Planck-Institut für Astronomie (MPIA Heidelberg), National Astronomical Observatory of China, New Mexico State University, New York University, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Portsmouth, University of Utah, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University.

SDSS has been supported by

sloan logo nsf logo doe-logo

Funding for SDSS has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the U.S. Department of Energy Office of Science.

Copyright © 2014 Sloan Digital Sky Survey

Explore

Data Access

The most current data release of the Sloan Digital Sky Survey is Data Release 10 (DR10). DR10 is the first release of the spectra from the SDSS-III’s Apache Point Observatory Galactic Evolution Experiment (APOGEE), which uses infrared spectroscopy to study tens of thousands of stars in the Milky Way. It and other past data releases can be viewed below.

 

The bright spiral galaxy Messier 81

Current and Future Data

The most recent data from the SDSS is Data Release 10, available from the SDSS-III website.

Data Release 11 (DR11) is an internal release to the SDSS collaboration.

Data Release 12 (DR12) will be made publicly available in January 2015.

Accessing the Data

Each Data Release includes four types of data: imagesoptical spectrainfrared spectra, and catalog data (parameters measured from images and spectra, such as magnitudes and redshifts).

The SDSS offers several different online data access tools, each suited to a particular need:

Website Purpose
Science Archive Server Interactive spectra and image mosaics
SkyServer Browser-based access to the Catalog Archive Server (CAS) database, with resources for learning SQL and projects to teach science
CasJobs Flexible advanced SQL-based interface to the CAS, for all data releases (quick registration required)
DR10 FITS Direct download access to DR10 FITS data files for experts
Data Model Details of the SAS directory structure, file formats, and the contents of each file

Past Data Releases

Working with SDSS Data

If you use public SDSS data in your paper, please see our guide on how to cite the SDSS. We hope you find our resources useful!

If you have questions about working with SDSS data, please email our helpdesk.

SDSS-III Data Release 10

The Tenth SDSS Data Release (DR10)

DR10 Facts
Imaging sky coverage 14,555 square degrees
Optical galaxy spectra 1,848,851
Optical quasar spectra 308,377
Optical stellar spectra 736,484
Infrared stellar spectra 57,454

Data Release 10 (DR10) offers the latest data from the Sloan Digital Sky Survey. DR10 is the first release of the spectra from the SDSS-III's Apache Point Observatory Galactic Evolution Experiment (APOGEE), which uses infrared spectroscopy to study tens of thousands of stars in the Milky Way.

DR10 also includes hundreds of thousands of new galaxy and quasar spectra from the Baryon Oscillation Spectroscopic Survey (BOSS), in addition to all imaging and spectra from prior SDSS data releases. The principal changes from DR9 are summarized in the What's New in DR10.

 

DR10 SDSS/BOSS coverage
DR10 SDSS/BOSS sky coverage
(click for a larger version)
DR10 APOGEE coverage
DR10 APOGEE sky coverage
(click for a larger version)

Using DR10

The figures to the right shows the sky coverage of DR10. The top figure shows the sky coverage in imaging and optical spectroscopy; the bottom figure shows the sky coverage in infrared spectroscopy.

The Scope of DR10 provides more detailed information about DR10 sky coverage, and includes a coverage check form that links directly to SDSS imaging and optical spectroscopy data.

The items in the menubar above contain the following information about DR10:

  • What's new? explains the differences between DR10 and previous data releases.
  • Scope describes what data are available in DR10, including sky coverage, data size, and resolution information.
  • Data Access shows how to get common types of SDSS data, and provides links to all SDSS data access tools. This is the best place to look for a quick start using SDSS data.
  • Imaging explains what imaging data DR10 contains. It also provides details on the SDSS imaging pipeline, the calibration process, and what quantities (including units) are available in imaging catalog data.
  • Optical Spectra explains what data are available from the SDSS's two optical spectrographs (SDSS-I and BOSS), and provides details on associated data including target flags, redshifts, and classifications.
  • Infrared Spectra explains what data are available from the SDSS's new APOGEE infrared spectrograph, and provides details on associated data including information on the spectra, targets, radial velocities, and determinations of stellar atmospheric parameters.
  • Algorithms lists some of the principal SDSS-III data processing algorithms, including target selection, and contains complete details on how the algorithms work.
  • Software provides download instructions and documentation for a variety of software tools for working with SDSS data.
  • Help contains a glossary, Frequently Asked Questions, and other resources to help you get started in using DR10.
  • Tutorials provide step-by-step guides to common research and teaching tasks using SDSS data. This is a good place to look for guidance in doing your science with the SDSS.

Complete details about DR10 are documented on this site and in the Data Release 10 paper (Ahn et al. 2013, submitted to ApJS and posted at arXiv:1307.7735).

Acknowledging DR10

Publications using SDSS data are required to include the complete official SDSS-III acknowledgment. Data from the SDSS-III public archive may not be used for any commercial publication or other commercial purpose except with explicit approval by the Astrophysical Research Consortium (ARC). Requests for such use should be directed to the ARC Corporate Office via ARC's Business Manager:

Michael L. Evans
ARC Business Manager
c/o University of Washington, Department of Astronomy
Box 351580
Seattle, WA 98195
Phone: 206-685-7857
email: evans -at- astro dot washington dot edu.

Scope of DR10

The Scope of DR10

Data Release 10 (DR10), made publicly available in July 2013, contains the full imaging survey from the SDSS imaging camera, all of the spectra from the original SDSS 640-fiber spectrograph data, and an additional 684,000 new spectra from the BOSS 1000-fiber spectrograph. DR10 also contains the first data release from the SDSS-III APOGEE spectrograph, with spectra of 57,454 stars. This page describes the scope of DR10 data.

The new data in DR10 are:

  1. 684,000 new optical spectra from the SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS), and new reductions for all BOSS spectra
  2. Entirely new high resolution, infrared spectra for 57,454 Milky Way stars from the SDSS-III Apache Point Observatory Galactic Evolution Experiment (APOGEE), with stellar parameters, elemental abundances, and precise radial velocities

Jump to:

SDSS-III has committed to publicly release its raw and reduced data sets. We are doing so using the Catalog Archive Server for retrieval of catalog data from a powerful SQL database and a Science Archive Server for retrieval of calibrated spectra and images.

Optical Data

SDSS Optical data encompass the entirety of the original SDSS-I and -II imaging and spectroscopy, along with the follow-on SEGUE and BOSS surveys. All data previously released in DR1-DR9 are included in DR10.

To check whether a location is covered in DR10 optical data, please use the form below. Enter RA/Dec coordinates in the box, in decimal degrees, and click Submit. Your coordinates will be loaded into the DR10 Science Archive Server (SAS). If your point is in the DR10 optical survey area, results will include links to all available SDSS imaging and optical spectroscopic data.

SDSS Optical Data RA/Dec 
Coverage Check:
DR10 optical spectroscopic coverage
DR10 imaging and optical spectroscopic coverage in Equatorial coordinates (plot centered at RA = 6h, or 90 deg.)

The optical data are defined as a set of photometric runs and a set of spectroscopic plates (see the basics onimaging and spectroscopy). We provide links here to ASCII and FITS lists of the runs and the plates. These lists are essentially a summary of all of the data in the data release.

Optical imaging data statistics

Total unique area covered 14,555 square degrees
Total area of imaging (including overlaps) 31,637 square degrees (excluding supernova runs)
Individual image field size 1361x2048 pixels (0.0337 square degrees)
Number of individual fields 938,046 (excluding supernova runs)
Number of catalog objects 1,231,051,050
Number of unique detections 932,891,133
Number of unique, primary sources
Total 469,053,874
Stars 260,562,744
Galaxies 208,478,448
Unknown 12,682
Effective wavelengths and magnitude limits
(95% completeness for point sources)
u g r i z
3551 Å 4686 Å 6165 Å 7481 Å 8931 Å
22.0 22.2 22.2 21.3 20.5
Median PSF FWHM, r-band 1.3 arcsec
Pixel scale 0.396 arcsec
Exposure time per band 53.9 sec
Time difference between observations of each band 71.72 sec (in riuzg order)
Relative photometric calibration accuracy (RMS)
(Padmanabhan et al. 2008)
u g r i z
1.3% 0.8% 0.8% 0.7% 0.8%
Global astrometric precision 0.1 arcsec rms (absolute)

Optical spectroscopic data statistics

  SDSS spectrograph BOSS spectrograph
Area covered
Full unique coverage 9274 square degrees
SEGUE-1 coverage 1438 square degrees
SEGUE-2 coverage 1317 square degrees
Legacy coverage 7966 square degrees
Full unique coverage 6,373 square degrees
Number of plates
Category Total Good/
marginal
Primary
All programs 2880 2764 2654
Legacy 1926 1869 1794
SEGUE-1 442 427 407
SEGUE-2 211 211 204
Special 301 257 246
Category Total Good/
marginal
Primary
All programs 1622 1515 1489
BOSS 1545 1485 1467
Special 77 30 22
Plate area 1.49 deg radius, 6.97 deg2 1.49 deg radius, 6.97 deg2
Fibers per plate 640 1000
Numbers of spectra
Category Total On good or 
marginalplates
Unique
All programs 1,843,200 1,768,960 1,629,129
Main galaxy targets 778,410 755,111 711,726
LRG targets (excluding Main) 106,650 103,662 95,990
SEGUE-1 targets 250,422 242,008 220,851
SEGUE-2 targets 128,112 128,112 118,151
Stars 600,967 577,157 521,990
Galaxies 952,740 921,007 860,836
Quasars 130,300 126,368 116,003
Skies 110,288 103,046 93,187
Unknown 48,905 41,382 37,113
Category Total Unique
Total 1,507,954 1,391,792
Stars 159,327 144,968
Galaxies 927,844 859,322
Quasars 182,009 166,300
Sky 144,503 138,491
Unknown 101,550 89,003
Wavelength coverage 3800 to 9200 Å 3600 to 10,400 Å
Resolution 1800 to 2000 1400 to 2600 Å
Median S/N at g(fiber) = 20.2 mag, 4.2 per pixel (Legacy)
7.0 per pixel (SEGUE-1, -2)
at i(fiber) = 21 mag, 6.1 per pixel
Typical redshift accuracy 30 km/s rms for main galaxy sample (from repeat observations)
4.0 km/s rms for SEGUE near g=18th mag (from repeat observations)
1.8 km/s systematic limit for high signal-to-noise stars
65 km/s rms for Luminous Galaxy sample (from repeat observations)
1.8 km/s systematic limit for high signal-to-noise stars
Approximate magnitude limits
(Corrected for Galactic dust extinction)
Main sample galaxies Petrosian r< 17.77 Strauss et al. (2002)
Luminous Red Galaxies Petrosian r< 19.2 Eisenstein et al. (2001)
z < 3 quasars PSF i < 19.1 Richards et al. (2002)
z > 3 quasars PSF i < 20.2 Richards et al. (2002)
SEGUE-1 Faint PSF 17.8 <r < 20.1 Yanny et al. (2009)
SEGUE-1 Bright PSF r < 17.8 Yanny et al. (2009)
SEGUE-2 PSF r < 20.2  
Luminous Galaxies i < 19.9 White et al. (2011)
Quasars g < 22
i < 22
Ross et al. (2012)

Infrared (APOGEE) Data

DR10 includes the first data released by the APOGEE survey. SDSS APOGEE infrared spectroscopy data are defined by a set of field centers, defined in galactic coordiantes. Each field may have had multiple plates observed. (See the APOGEE target selection documentation for more on the concepts of fields and plates.)

APOGEE
		Coverage Map
Index map showing locations of APOGEE fields with spectra in DR10.
Note that different fields have been visited different numbers of times, and not all fields have been completed. The figure is an Aitoff projection of 2MASS survey data in galactic coordiantes.

The links below provide FITS lists of the field centers, associated plates, and the dates those plates were were observed. See the APOGEE infrared spectroscopic data page for more details.

More APOGEE fields will be included in Data Releases 11 and 12.

Infrared (APOGEE) spectroscopic data statistics

Data
Category Dates Observed (inclusive) MJD
Commissioning 2011 May 22 - 2011 Jul 18 55703-55760
Survey Year 1 2010 Aug 31 - 2012 Jul 13 55804-56121
Number of plates
Category Total
All plates 281
Commissioning 51
Survey Year 1 232
Note: Some plates were observed in both phases
Number of visits
Category Total
All visits 684
Commissioning 98
Survey Year 1 586
Plate area
Plate radius is a function of target selection.
1.49 deg radius, 6.97 deg2
0.99 deg radius, 3.08 deg2
0.89 deg radius, 2.49 deg2
0.75 deg radius, 1.77 deg2
0.50 deg radius, 0.79 deg2
Fibers per plate 
(for standard survey and ancillary programs)
Total 300
Science 230
Telluric Standard 35
Sky 35
Numbers of stellar spectra
Category Visits Unique Objects
Total 178,397 57,454
Survey Stars 153,454 47,452
Ancillary Targets 8,894 3,344
Telluric Standards 24,283 7,003
Wavelength coverage 1.51 to 1.69 μm
Resolution R = 21,500-24,000 depending on wavelength and fiber.
Spectral Sampling
Category 1.51-1.58μm 1.58-1.645μm 1.645-1.695μm
Single Exposure 0.326 Å/pixel 0.283 Å/pixel 0.236 Å/pixel
Dithered Pair 0.163 Å/pixel 0.142 Å/pixel 0.118 Å/pixel
Median S/N
at H = 11.0, S/N ~ 100 per dithered pixel in 1 hours total exposure
at H = 12.2, S/N ~ 100 per dithered pixel in 3 hours total exposure
Typical velocity accuracy < 100 m/s per visit with S/N > 20 
Approximate magnitude limits 
(at S/N = 100 per dithered pixel for completed plates)
1 hour field H < 11.0
3 hour field H < 12.2
10 hour field H < 12.8
24 hour field H < 13.2

DR10 Tutorials

Tutorials

These pages provide detailed worked examples of SDSS data retrieval using the various interfaces provided. In addition, we provide discussions of how to access and read some of the unusual file types used in the survey, and perform certain operations, such as calibration.

Learn

Surveys

Sloan Digital Sky Surveys

After nearly a decade of design and construction, the Sloan Digital Sky Survey saw first light on its giant mosaic camera in 1998 and entered routine operations in 2000. While the collaboration and scope of the SDSS have changed over the years, many of its key principles have stayed fixed: the use of highly efficient instruments and software to enable surveys of unprecedented scientific reach, a commitment to creating high quality public data sets, and investigations that draw on the full range of expertise in a large international collaboration. The generous support of the Alfred P. Sloan Foundation has been crucial in all phases of the SDSS, alongside support from the Participating Institutions and national funding agencies in the U.S. and other countries.

orangespider-smaller2

SDSS-IV: Current Surveys (2014-2020)

The latest generation of the SDSS (SDSS-IV, 2014-2020) is extending precision cosmological measurements to a critical early phase of cosmic history (eBOSS), expanding its revolutionary infrared spectroscopic survey of the Galaxy in the northern and southern hemispheres (APOGEE-2), and for the first time using the Sloan spectrographs to make spatially resolved maps of individual galaxies (MaNGA).

APOGEE-2

apogee

A stellar survey of the Milky Way, with two major components: a northern survey using the bright time at APO, and a southern survey using the 2.5m du Pont Telescope at Las Campanas.
Explore APOGEE-2

eBOSS

BOSS

Cosmological survey of quasars and galaxies, also encompassing subprograms to survey variable objects (TDSS) and X-Ray sources (SPIDERS).
Explore eBOSS

MaNGA

MaNGA

The galaxy survey for people who love galaxies! MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) will explore the detailed internal structure of nearly 10,000 nearby galaxies using spatially resolved spectroscopy.

 

SDSS-III: Prior Surveys (2008-2014)

SDSS-III (2008-2014) undertook a major upgrade of the venerable SDSS spectrographs and added two powerful new instruments to execute an interweaved set of four surveys, mapping the clustering of galaxies and intergalactic gas in the distant universe (BOSS), the dynamics and chemical evolution of the Milky Way (SEGUE-2 and APOGEE), and the population of extra-solar giant planets (MARVELS).

APOGEE

apogee

The Apache Point Observatory Galactic Evolution Experiment (APOGEE) focuses on the structure and evolution of our own Milky Way galaxy using high-resolution infrared spectroscopy.
Explore APOGEE

BOSS

BOSS

The Baryon Oscillation Spectroscopic Survey (BOSS) focused on mapping the Universe on the largest scales, creating the largest volume three-dimensional map of galaxies to date and measuring the scale of the Universe to one percent.

MARVELS

apogee

The Multi-Object APO Radial Velocity Exoplanet Large-area Survey (MARVELS) searches very nearby stars for evidence of “exoplanets” surrounding them.
Explore MARVELS

SEGUE-2

SEGUE Field of Streams

The Sloan Extension for Galactic Understanding and Exploration (SEGUE) focuses on the structure and evolution of our own Milky Way galaxy. The SEGUE-2 survey builds off of the work of SEGUE-1.
Explore SEGUE

SDSS-I/II: Prior Surveys (2000-2008)

In its first five years of operations, the SDSS carried out deep multi-color imaging over 8000 square degrees and measured spectra of more than 700,000 celestial objects.  With an ever-growing collaboration, SDSS-II (2005-2008) completed the original survey goals of imaging half the northern sky and mapping the 3-dimensional clustering of one million galaxies and 100,000 quasars. SDSS-II carried out two additional surveys: the Supernova Survey, which discovered and monitored hundreds of supernovae to measure the expansion history of the universe, and the Sloan Extension for Galactic Understanding and Exploration (SEGUE), which extended SDSS imaging towards the plane of the Galaxy and mapped the motions and composition of more than a quarter million Milky Way stars.

Legacy

The large spiral galaxy M51 with its small spiral companion

The original SDSS observing plan, which ran from 2000 to 2008, is now known as the SDSS Legacy Survey. It resulted in a uniform, well-calibrated map of the Universe that will be used for decades to scientific studies ranging from asteroids to the large-scale structure of the Universe.
Explore Legacy

Supernova

Supernova

The SDSS Supernova Survey, which ran from 2005 to 2008, performed repeat imaging of one stripe of sky along the celestial equator. The project discovered more than 500 type Ia supernovae, which have led to a deeper understanding of the history of the Universe.
Explore the Supernova Survey

SEGUE-1

SEGUE Field of Streams

The primary goal of SEGUE-1 was the kinematic and stellar population study of the high-latitude thick disk and halo of the Milky Way.
Explore SEGUE

Instruments

Explore below to learn more about the SDSS telescopes, the SDSS spectrographs, and the SDSS imager.

Telescopes

The Sloan Foundation 2.5m Telescope at Apache Point Observatory
Sloan Foundation 2.5m Telescope

The SDSS uses a dedicated 2.5-m f/5 modified Ritchey-Chrétien altitude-azimuth telescope located at Apache Point Observatory, in south east New Mexico (Latitude 32° 46′ 49.30″ N, Longitude 105° 49′ 13.50″ W, Elevation 2788m). A 1.08 m secondary mirror and two corrector lenses result in a 3° distortion-free field of view. The telescope is described in detail in a paper by Gunn et al. (2006).

The Irénée du Pont Telescope at Las Campanas Observatory
The new fourth phase of the SDSS will include observations from the Southern Hemisphere for the first time. The southern observations will be taken from the Irénée du Pont Telescope at Las Campanas Observatory in northern Chile (Latitude 29° 0′ 52.56″ S, Longitude 70° 41′ 33.36″ W, Elevation 2380m). The du Pont telescope is a Ritchey-Chrétien 2.5-m f/3 telescope with a Gascoigne corrector lens. The telescope is described in detail in a paper by Bowen & Vaughan (1973).
 
The Irenee du Pont Telescope at Las Campanas Observatory in Chile

Spectrographs

eBOSS / BOSS

The eBOSS spectroscopic survey will continue to use the spectrographs built for BOSS. These two identical spectrographs are rebuilt from the original SDSS spectrographs, which were used for the SDSS Legacy and SEGUE surveys. Each spectrograph has two cameras, one red and one blue, with a dichroic splitting the light at roughly 6000 Å and a full wavelength range from 3600 to 10,400 Å. Additional Information can be found on the BOSS spectrograph page.

APOGEE-2 / APOGEE

The APOGEE-2 survey will continue to use the high-resolution near-infrared spectrograph used for the APOGEE survey. The APOGEE spectrograph is a custom-built multi-object spectrograph which records the spectrum of 300 targets simultaneously across 1.51 to 1.70 micron within the H-band with a nominal resolution of 22,500. Additional Information can be found on the APOGEE spectrograph page.

MaNGA

MaNGA is a galaxy survey which aims to make detailed integral field unit (IFU) resolved spectroscopy measurements of 10,000 nearby galaxies. MaNGA uses the eBOSS / BOSS spectrograph, but instead of placing a single fiber on each galaxy, it uses specially-designed hexagonal ferrules to close-pack the circular fibers into hexagonal bundles. Each MaNGA cartridge contains 17 bundles ranging in size from 19 to 127 fibers, each enabling detailed observations of 17 galaxies simultaneously. Additional Information can be found on the Instrumentation section of the MaNGA surveys page.

MARVELS

MARVELS used a specially-built interferometric spectrograph to obtain high-precision radial velocity measurements of stars looking for exoplanet candidates. In contrast to the more traditional high-resolution echelle spectrographs, MARVELS used a new approach to measuring radial velocities. The spectrograph is a fiber-fed dispersed fixed-delay interferometer (DFDI), a combination of Michelson interferometer and a medium resolution (R~6,000-10,000) spectrograph, which overlays interferometer fringes on a long-slit stellar spectrum. Additional Information can be found on the MARVELS spectrograph page.

Camera

SDSS Imaging Camera
 
The SDSS’s imaging camera is now at the Smithsonian, but all the images it collected are available online. The imaging camera collected photometric imaging data using an array of 30 SITe/Tektronix 2048 by 2048 pixel CCDs arranged in six columns of five CCDs each, aligned with the pixel columns of the CCDs themselves. SDSS r, i, u, z, and g filters cover the respective rows of the array, in that order. The survey operated the instrument in a drift scan mode: the camera slowly reads the CCDs while the telescope moves along great circles on the sky so that images of objects move along the columns of the CCDs at the same rate the CCDs are being read. As an image of an object moves along the column of the CCDs, a CCD in each row collects data on that object. Therefore, the camera produces five images of a given object, all from the same column of CCDs, one from each CCD in that column. It takes an object 54 seconds to move from the beginning of a CCD to the end, so the effective exposure time in each filter is 54 seconds. Because there is some space between the rows of CCDs, it takes an image 71.7 seconds to move from the beginning of one row to the next. Each row corresponds to a different filter, so each object has one image in each filter, taken at 71.7 second intervals. Additional information can be found under Camera. A detailed description of the imager can be found in Gunn et al. (1998) and in the SDSS-I project book.

Education

The Sloan Digital Sky Survey is committed to working towards making the science and engineering results of our surveys accessible to the public. We aim to do this through informal and formal education, citizen science, news and social media. A selection of our Education and Public Engagement activities are described below.

SDSS: Data For All

SkyServer hosts all the latest data from SDSS, freely available for all to use. Tools, guides and a variety of suggested projects (from kids to college level) are provided to help new users learn how to use the data.
Explore SkyServer

SDSS News

We run a Science Blog, where you’ll find short descriptions of our scientific research and the latest discoveries from the SDSS. We’d love to see your comments and questions about what you read there!
Explore The SDSS Blog

Connect with Us

 

Citizen Science with SDSS

gz-logo

 
Galaxy Zoo launched in 2007 to deal with the 1 million images in the SDSS Main Galaxy Sample. It’s now the most productive citizen science project in existence, and this success spawned a “Zooniverse” of other citizen science projects across all areas of science. Join more than a million members of the Zooniverse to do real science online at the Zooniverse.
Additionally, the Zooteach website includes lesson plans and ideas to use Zooniverse projects (including Galaxy Zoo) in education.
Explore the Zooniverse

SDSS for Educators

launch

 
Currently under development Voyages is a new interface to the SDSS data especially designed to meet the needs of educators. With special themed sections for short and long activities.
Be a Voyages Beta Tester

Introduction to SDSS

One of the worlds largest astronomical collaborations, the SDSS makes all its data publicly available in annual releases. This includes images of more than a third of the sky and spectra of 5 million stars and galaxies. Watch this video intro to the SDSS from the American Museum of Natural History, and explore the rest of our website to learn more about SDSS.

SDSS Education Group

SDSS does not have a large team of educators, but instead relies on the collaborative efforts of our member scientists and a number of external collaborations for public engagement and outreach and to develop educational content.

The SDSS team has created materials for learners and educators through a number of collaborations and alliances, including:

People

  • Education and Public Outreach Director: Karen Masters (Portsmouth)
  • Public Information Officer: Jordan Raddick (JHU)
  • Educational Consultant: Kate Meredith

Contact us at outreach@sdss.org

You might be looking for resources linked from the Education page of classic.sdss.org, or the Education page of sdss3.org.

For collaboration members there is also more information on the EPO Pages of the SDSS Wiki.

Results & Science

Science Results

The Sloan Digital Sky Survey has been working for more than 15 years to make a map of the Universe, and will continue for many years to come. The video below shows a flythrough of the SDSS’s map of the large-scale structure of the Universe.

But this map in itself is not the SDSS’s real accomplishment; its real success is the revolutionary new knowledge that has been gained as a result. This page summarizes some of the major discoveries that the SDSS has enabled.

Cosmology

A slice of the Universe showing the large-scale structure of galaxies; each galaxy is a small orange, green, or blue dot.

The SDSS’s map of the Universe. Each dot is a galaxy; the color bar shows the local density.

Measurements of large-scale structure in SDSS maps of galaxies, quasars, and intergalactic gas have become a central pillar of the standard cosmological model that describes our understanding of the history and future of the Universe. SDSS data have helped to demonstrate that the Universe is dominated by unseen dark matter and pervasive dark energy, and seeded with structure by quantum fluctuations in the infant cosmos. Those fluctuations have grown into the large-scale structure we see today.

The SDSS’s high-precision maps of cosmic expansion history using baryon acoustic oscillations (BAOs) have been especially influential in quantifying these results, yielding exquisite constraints on the geometry and energy content of the universe. BAOs were first detected in galaxy clustering by the SDSS-I and in the contemporaneous 2dF Galaxy Redshift Survey, and have since also been detected in intergalactic hydrogen gas using Lyman-alpha forest techniques.

These BAO measurements are beautifully complemented by the results of the SDSS-II Supernova Survey, which has provided the most precise measurements yet of cosmic expansion rates over the last four billion years. In addition, statistical measurements of galaxy motions and weak gravitational lensing provide some of the strongest evidence to date that Einstein’s General Relativity is an accurate description of gravity on cosmological scales.

Quasars

Stacked spectra of more than 46,000 quasars from the SDSS; each spectrum has been converted to a single horizontal line, and they are stacked one above the other with the closest quasars at the bottom and the most distant quasars at the top.  Credit: X. Fan and the Sloan Digital Sky Survey.

Stacked spectra of more than 46,000 quasars from the SDSS; each spectrum has been converted to a single horizontal line, and they are stacked one above the other with the closest quasars at the bottom and the most distant quasars at the top.
Credit: X. Fan and the Sloan Digital Sky Survey.

Powered by the accretion of gas onto supermassive black holes at the centers of galaxies, quasars are the most luminous objects in the Universe. With discoveries from its earliest imaging campaigns, the SDSS extended the study of quasars back to the first billion years after the Big Bang, showing the rapid early growth of black holes and mapping the end stages of the epoch of reionization.

With full quasar samples hundreds of times larger than those that existed before, the SDSS has given us the most accurate descriptions of the growth of black holes over cosmic history.  SDSS spectra show  that the properties of quasars have changed remarkably little from the early universe to the present day.

SDSS studies have probed the dark matter environments of quasars through clustering measurements, revealed populations of quasars whose central engines are hidden by obscuring dust, captured changes in quasar spectra that show clouds moving in the gravitational grip of the central black hole, and allowed a comprehensive census of the much fainter accreting black holes (active galactic nuclei, or AGN) in present-day galaxies.

Galaxies

Two spiral galaxies side-by-side

The bright spiral galaxy M51 and its fainter companion

The SDSS has transformed the field of systematic galaxy analysis with accurate measurements of hundreds of parameters for hundreds of thousands of galaxies across the full range of cosmic environments. SDSS studies have demonstrated a bimodal distribution of galaxy properties, with a clear separation between populations of star-forming galaxies like the Milky Way, and passive galaxies that have little or no ongoing star formation.

Using weak gravitational lensing and statistical analyses of galaxy clustering, the SDSS has mapped out the multi-faceted relationships between galaxies and their surrounding halos of dark matter, showing that passive galaxies are found mainly at the centers of massive halos or as satellites orbiting larger galaxies.

The comprehensive census of present-day galaxies from the SDSS provides an essential testing ground for theoretical models of galaxy formation, and a crucial end-point comparison for studies of galaxy evolution from Hubble Space Telescope and other observatories.

A small blue irregular galaxy

The ultra-faint Milky Way Companion galaxy Leo I

The Local Group

SDSS imaging enabled the discovery of a new population of “ultra-faint” dwarf galaxies orbiting the Milky Way. To date, the majority of known Milky Way companions have been found by the SDSS, along with several new companions of the Andromeda galaxy.

With total light output as low as a thousand times the luminosity of the Sun, these tiny systems provide critical insights into the physics of galaxy formation and stringent tests of the properties of dark matter.

The Milky Way

The clumps and streams found in the first SDSS star maps showed that the outer Milky Way is full of complex substructure – a finding confirmed and quantified as the SDSS imaged larger areas and mapped the motions of hundreds of thousands of stars.

A plot of the sky showing regions with more stars in red and yellow and regions with fewer stars in blue and black

The SDSS’s “Field of Streams” map shows structures of stars in the outer Milky Way

SDSS results support a theoretical picture of “hierarchical” galaxy formation, in which the Milky Way continues to grow by accreting and destroying smaller galaxies. The SEGUE and APOGEE surveys have provided our most comprehensive picture of the formation history of the Milky Way’s stellar disk by separately measuring the abundances of elements produced quickly by exploding massive stars (Type II supernovae) and more slowly by exploding white dwarfs (Type Ia supernovae).

SDSS measurements of the motions of stars in the disk and stellar halo have yielded the most precise determinations of the mass distribution of the Milky Way’s dark matter halo, implying a total halo mass of approximately one trillion solar masses, lower than many previous estimates.

A bright cluster of white stars in the center of the image

The star cluster M13 as seen by the SDSS

Stars

With precise multi-color imaging of hundreds of millions of stars, the SDSS has enabled systematic characterization of stellar populations and the identification of large samples of rare or intrinsically faint objects.  Hand-in-hand with the discovery of the most distant quasars came the discovery of numerous “brown dwarfs,” objects that form like stars but are not massive enough to ignite steady hydrogen fusion at their centers.  SDSS catalogs have provided our most detailed understanding of the population of low mass stars, which are individually faint but collectively represent a large fraction of the Galaxy’s stellar mass.   SDSS catalogs of white dwarfs, the Earth-sized embers left behind by sun-like stars at the ends of their lives, have been especially influential, revealing many subtleties of white dwarf physics and identifying metal-enriched systems that appear to be accreting material from surrounding belts of asteroids.  The combination of APOGEE chemical abundance measurements with asteroseismological data from NASA’s Kepler satellite is opening a new era of stellar astrophysics that can probe the interior structure of thousands of stars.

Analyses of SDSS data have also led to the discovery and characterization of “hyper-velocity stars,” moving so quickly that they will escape from the Milky Way entirely, apparently as a result of interactions with the Galaxy’s central supermassive black hole.

Solar System
Asteroids can be identified in SDSS imaging because they change position during the course of an 8-minute exposure with the SDSS camera. The SDSS has identified and measured colors of more than 100,000 asteroids and other Solar System minor objects. The video to the left shows the orbits of many of the asteroids that the SDSS has discovered. SDSS asteroid measurements are available online through the SDSS Moving Object Catalog.

SDSS asteroid studies demonstrated a marked change in the size distribution of main belt asteroids at a diameter of about 5 km, implying fewer small asteroids than previously believed.  They also showed that families  of asteroids with distinct orbital properties also have distinctive colors, revealing the importance of “space weathering” that changes the surface appearance of asteroids over time.  Dynamical families appear to be the result of collisions in the asteroid belt that produce cascades of smaller bodies, exposing fresh material that was previously the interior of a larger body.

Most SDSS asteroids are in the main belt between Mars and Jupiter, but repeat imaging has also enabled the SDSS to discover objects in the outer Solar System, near or beyond the orbit of Neptune.  One of these, the remarkable object 2006 SQ 372, is on a highly eccentric orbit that takes it to a distance of 800 AU (800 times the Earth-Sun distance).  Modeling suggests that it has been dynamically scattered from the inner zone of the Oort Cloud, a cloud of distant cometary bodies that is a remnant of the Solar System’s formation.

Other Science Results
The account above is only a broad sketch of some of the SDSS’s major contributions.  More accounts of some of the numerous important discoveries from SDSS data can be found in our press releases and blog entries (see below) and in this 2007 video  from the American Museum of Natural History, which provides an excellent introduction to the science themes of SDSS-I. More information is available from the museum’s Science Bulletin website.
 

Press Releases

The SDSS’s active program of public engagement includes regular communication with the media through a series of press releases and a part-time press officer. The SDSS press officer is Jordan Raddick (press@sdss.org).

The newest SDSS press releases are available through the SDSS Press Releases page of this website.

Prior press releases from the SDSS-III (2008-2014) and SDSS Classic (2000-2008) can be found on their respective websites.

SDSS Science Blog

We also maintain a regular science blog of SDSS discoveries where you will find short descriptions of interesting scientific research and discoveries from the SDSS-III. We’d love to see your comments and questions about what you read here!

SDSS Science Blog

Publications

The Sloan Digital Sky Survey is one of the most cited surveys in the history of astronomy. Data from SDSS has been used in over 5,800 peer-reviewed publications in astronomy and other sciences; those papers in turn have been cited a total of 245,000 times. It is a testament to the public data commitment of SDSS that the overwhelming majority of those papers have been written by scientists outside the SDSS collaboration.

Publications produced within the SDSS collaboration are currently hosted on our SDSS-III website.

Collaboration

The SDSS is carried out by an international collaboration of hundreds of scientists at dozens of institutions. The scientific achievements of the SDSS have been enabled by the technical and financial contributions, and the collective scientific expertise, of this vibrant collaboration.

The active operational phase of the project is the SDSS-IV, which began on July 1, 2014. SDSS-III observing is complete, but activities will continue through the final SDSS-III data release in January 2015 and the scientific analyses of the completed surveys.

The SDSS-IV collaboration is still growing and continues to actively recruit new institutional partners. For more information about joining SDSS-IV, see the Joining Document and the Principles of Operation below.

The SDSS-IV Advisory Council is the collaboration’s governing body, formulating recommendations on project scope and budget to the Board of Governors of the Astrophysical Research Consortium (ARC), which has responsibility for all activities at Apache Point Observatory. Collaboration policies are developed and implemented by the SDSS Collaboration Council (CoCo), which has a representative from all of the full and associate institutional partners. The CoCo is chaired by the SDSS Spokesperson, who is elected by the collaboration for a 3-year term.

For more detailed information about the SDSS collaboration, and how it operates, please see the important policy documents below.

Policies

The top-level governing policy for SDSS-IV is the Principles of Operation document. The equivalent for SDSS-III is the SDSS-III Principles of Operation.

More detailed implementation policies, most importantly the Publication Policy and the External Collaborator Policy, are developed by the CoCo and approved by the Advisory Council.
These are the four main collaboration policies used within the SDSS projects are:

Publication Policy

The publication policy outlines how SDSS publications are created and organized. The SDSS-III Publication Policy, an update to the policies of SDSS-I/II,  is available on the  SDSS-III website. The SDSS-IV Publication Policy is under review and will be available shortly.

External Collaborators

The procedure for requesting external collaborator status as well as discussing other external participant rules is viewable on the SDSS-III website. These policies are essentially identical to the original SDSS-I/II policies, but the language updated for the SDSS-III collaboration.

Survey Science Teams & Working Groups

As described in the PoO and the Publication Policy, all science projects using non-public SDSS data must be announced to the Collaboration via the project-list web page when they are initiated. At a level above these individual projects, which will typically encompass one or several papers led by a few active team members, scientists may wish to collaborate and coordinate in larger groupings and thus create a “working group” (WG) for the sharing of common knowledge, facilities and information.

Image Use Policy

Any SDSS image on the SDSS Web site may be downloaded, linked to, or otherwise used for non-commercial purposes, provided that you agree to the following conditions:

  • You must maintain the image credits. Unless otherwise stated, images should be credited to the Sloan Digital Sky Survey
  • Your use of the image cannot be construed as an endorsement of any product or service
  • If the image is to be used on a Web page, we also ask as a courtesy that you provide a link back to our site at http://www.sdss3.org/

SDSS Images may be used in commercial publications, or for other commercial purposes, only with the explicit approval of the Astrophysical Research Consortium (ARC). Requests for such use should be directed to the ARC Corporate Office via ARC’s Business Manager as follows:

Michael Evans
ARC Business Manager
c/o Department of Astronomy, Box 351580
University of Washington
Seattle, WA 98195
Phone: +1-206-685-7857
Email: evans@astro.washington.edu
Please provide the exact URL of the image you are requesting.

If you have further questions about the appropriate usage of SDSS-III images, please contact the helpdesk.

Each of the individual surveys has its own Survey Science Team (SST), which acts as the highest level of working group for science associated with that survey.

Additional information on SSTs and WGs can be seen on the SDSS-III website.

SDSS Institutions

There are full member institutions, associate member institutions, and participation groups.

Full Member Institutions

  • Carnegie Institution for Science
  • Carnegie Mellon University
  • Instituto de Astrofisica de Canarias
  • The Johns Hopkins University
  • Kavli Institute for the Physics and Mathematics of the Universe (IPMU) / University of Tokyo
  • Lawrence Berkeley National Laboratory
  • Max-Planck-Intitut für Astrophysik (MPA Garching)
  • Max-Planck-Intitut für Extraterrestrische Physik (MPE)
  • Max-Planck-Intitut für Astronomie (MPIA Heidelberg)
  • National Astronomical Observatory of China
  • New Mexico State University
  • New York University
  • The Ohio State University
  • Pennsylvania State University
  • Shanghai Astronomical Observatory
  • Universidad Nacional Autónoma de México
  • University of Colorado Boulder
  • University of Portsmouth
  • University of Utah
  • University of Wisconsin
  • Yale University

Associate Member Institutions

  • Academia Sinica Institute of Astronomy and Astrophysics (ASIAA)
  • École Polytechnique Fédérale de Lausanne
  • Leibniz Institut für Astrophysik Potsdam (AIP)
  • Nanjing University
  • Princeton University
  • Texas Christian University
  • Tsinghua Center for Astrophysics
  • Universidad Autónoma de Madrid
  • University of Alabama, Tuscaloosa
  • University of Arizona
  • University of California, Irvine
  • University of Edinburgh
  • University of Iowa
  • University of Kentucky
  • University of Pennsylvania
  • University of Pittsburgh
  • University of Toronto
  • University of Washington
  • University of Wyoming
  • Vanderbilt University

Participation Groups

  • Harvard-Smithsonian Center for Astrophysics
  • Israel Center of Research Excellence (I-CORE)
    • Tel Aviv University
  • Korean Participation Group
    • Korea Institute for Advanced Study
    • Korea Astronomy and Space Science Institute
  • United Kingdom Participation Group
    • Liverpool John Moores University
    • University of Cambridge
    • University of Nottingham
    • University of Oxford

For further information about the SDSS Collaboration, please contact the Spokesperson at spokesperson@sdss.org.

Official SDSS Acknowledgment

We request that the following be added to the acknowledgment section of any paper using data from the SDSS.

Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. The SDSS web site is www.sdss.org.

SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU) / University of Tokyo, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), Max-Planck-Institut für Astronomie (MPIA Heidelberg), National Astronomical Observatory of China, New Mexico State University, New York University, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Portsmouth, University of Utah, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University.

About

How to Cite SDSS

If you have used public SDSS data in your paper, please cite the following papers describing the instruments, survey, and data analysis as appropriate:

Please cite the relevant survey description(s):

the Sloan Foundation 2.5-meter Telescope description:

the relevant Data Release Paper:

  • Ahn et al. 2014 (Data Release 10) is the most recent Data Release paper, but please cite the appropriate paper for the Data Release used for your download.

and the relevant instrumentation paper(s):

In addition, the appropriate SDSS acknowledgment(s) for the survey and data releases that were used should be included in the Acknowledgments section:

For reference, a more extensive list of technical SDSS papers that it may be appropriate to cite is available at the Technical Publications page of SDSS-III. In particular, this list includes papers that describe the target selection algorithms for the different SDSS spectroscopic surveys and the methods that have been used to extract physical quantities such as classifications, photometry, astronometry, redshifts, and stellar parameters from the survey data.

Image Use Policy

Any SDSS image on the SDSS Web site may be downloaded, linked to, or otherwise used for non-commercial purposes, provided that you agree to the following conditions:

  • You must maintain the image credits. Unless otherwise stated, images should be credited to the Sloan Digital Sky Survey
  • Your use of the image cannot be construed as an endorsement of any product or service
  • If the image is to be used on a Web page, we also ask as a courtesy that you provide a link back to our site at http://www.sdss3.org/

SDSS Images may be used in commercial publications, or for other commercial purposes, only with the explicit approval of the Astrophysical Research Consortium (ARC). Requests for such use should be directed to the ARC Corporate Office via ARC’s Business Manager as follows:

Michael Evans
ARC Business Manager
c/o Department of Astronomy, Box 351580
University of Washington
Seattle, WA 98195
Phone: +1-206-685-7857
Email: evans@astro.washington.edu
Please provide the exact URL of the image you are requesting.

If you have further questions about the appropriate usage of SDSS-III images, please contact the helpdesk.

Each of the individual surveys has its own Survey Science Team (SST), which acts as the highest level of working group for science associated with that survey.

Additional information on SSTs and WGs can be seen on the SDSS-III website.

Publication Policy

The publication policy outlines how SDSS publications are created and organized. The SDSS-III Publication Policy, an update to the policies of SDSS-I/II,  is available on the  SDSS-III website. The SDSS-IV Publication Policy is under review and will be available shortly.

Contact Us

For questions about using the data, please consult the latest Data Release’s help page, and if necessary the help desk contact listed there.

Questions about this web site should be directed to webmaster@sdss.org.

SDSS Collaboration

For more information about the SDSS collaboration, please contact:

Director
Michael Blanton
New York University
director@sdss.org
Project Scientist
Matthew Bershady
University of Wisconsin
projectscientist@sdss.org
Scientific Spokesperson
Jennifer Johnson
Ohio State University
spokesperson@sdss.org
Education and Public Outreach Director
Karen Masters
University of Portsmouth
outreach@sdss.org

Contacting SDSS-III

For more information about the SDSS-III collaboration (2008-2014), please contact:

Director
Daniel Eisenstein
Harvard-SAO
deisenstein@cfa.harvard.edu
Project Scientist
David Weinberg
Ohio State University
dhw@astronomy.ohio-state.edu
Scientific Spokesperson
Michael Wood-Vasey
University of Pittsburgh
wmwv@pitt.edu
Education and Public Outreach Director
Jordan Raddick
Johns Hopkins University
raddick@jhu.edu

Survey Contacts

For more information about the surveys that make up the SDSS, contact the Principal Investigators of each survey.
SDSS-IV Surveys
APOGEE-2
Steven Majewski
University of Virginia
srm4n@mail.astro.virginia.edu
eBOSS
Jean-Paul Kneib
Ecole Polytechnique Federale de Lausanne
jean-paul.kneib@epfl.ch
MaNGA  
Kevin Bundy
Kavli Institute IPMU
kevin.bundy@ipmu.jp
SDSS-III Surveys
APOGEE
Steven Majewski
University of Virginia
srm4n@mail.astro.virginia.edu
BOSS
David Schlegel
Lawrence Berkeley National Lab
djschlegel@lbl.gov
MARVELS
Jian Ge
University of Florida
jge@astro.ufl.edu
SEGUE-2
Connie Rockosi
University of California at Santa Cruz
UCO-Lick Observatory
crockosi@ucolick.org
 

THE TENTH DATA RELEASE OF THE SLOAN DIGITAL SKY SURVEY: FIRST SPECTROSCOPIC DATA FROM THE SDSS-III APACHE POINT OBSERVATORY GALACTIC EVOLUTION EXPERIMENT

Source: http://www.sdss3.org/science/dr10.pdf (PDF)

Draft version January 16, 2014

Preprint typeset using LATEX style emulateapj v. 12/16/11

1
Christopher P. Ahn
Timothy Anderton
Adam S. Bolton
Joel R. Brownstein
Kyle S. Dawson
J. G. Galbraith-Frew
Hong Guo
David W. Harris
Inese I. Ivans
Antonio D. Montero-Dorta
Matthew D. Olmstead
Jonathan C. Richards
Yiping Shu
Zheng Zheng
Department of Physics and Astronomy, University of Utah, Salt Lake City, UT 84112, USA.
2
Rachael Alexandroff
Bruce A. Gillespie
Ting-Wen Lan
Brice Menard
M. Jordan Raddick
Mubdi Rahman
Aniruddha R. Thakar
Jan Vandenberg
Gail Zasowski
Guangtun Zhu
Center for Astrophysical Sciences, Department of Physics and Astronomy, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA.
3
Carlos Allende Prieto
Massimiliano Esposito
D. Fabbian
Bruno Femena Castella
Emma Fernandez Alvar
D. A. Garca-Hernandez
R. Genova-Santos
Jonay I. Gonzalez Hernandez
Artemio Herrero Davo
A. Manchado
Sz. Meszaros
Rafael Rebolo
J. A. Rubino-Martin
Alina Streblyanska
O. Zamora
Instituto de Astrofsica de Canarias (IAC), C/Va Lactea, s/n, E-38200, La Laguna, Tenerife, Spain.
4
Carlos Allende Prieto
Massimiliano Esposito
Bruno Femena Castella
Emma Fernandez Alvar
D. A. Garca-Hernandez
R. Genova-Santos
Artemio Herrero Davo
A. Manchado
Sz. Meszaros
J. A. Rubino-Martin
Alina Streblyanska
O. Zamora
Departamento de Astrofsica, Universidad de La Laguna, E-38206, La Laguna, Tenerife, Spain.
5
Friedrich Anders
Dorothee Brauer
Cristina Chiappini
Sebastian E. Nuza
Matthias Steinmetz
Leibniz-Institut fur Astrophysik Potsdam (AIP), An der Sternwarte 16, D-14482 Potsdam, Germany.
6
Friedrich Anders
Technische Universitat Dresden (TUD), Institut fur Kernund Teilchenphysik, D-01062 Dresden, Germany.
7
Scott F. Anderson
John J. Bochanski
Michael L. Evans
Suzanne L. Hawley
Russell Owen
Conor Sayres
Department of Astronomy, University of Washington, Box 351580, Seattle, WA 98195, USA.
8
Brett H. Andrews
Courtney R. Epstein
Jennifer A. Johnson
Demitri Muna
M. H. Pinsonneault
Sarah J. Schmidt
Kris Sellgren
David H. Weinberg
Gail Zasowski
Department of Astronomy, Ohio State University, 140 West 18th Avenue, Columbus, OH 43210, USA.
9
Eric Aubourg
Julian E. Bautista
Nicolas G. Busca
APC, University of Paris Diderot, CNRS/IN2P3, CEA/IRFU, Observatoire de Paris, Sorbonne Paris Cite, F-75205 Paris, France.
10
Stephen Bailey
Florian Beutler
Vaishali Bhardwa
William Carithers
Andreu Font-Ribera
Beth A. Reid
Natalie A. Roe
Nicholas P. Ross
David J. Schlegel
Hee-Jong Seo
Nao Suzuki
Martin White
Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA.
11
Fabienne A. Bastien
Andreas A. Berlind
Jonathan C. Bird
Nathan De Lee
Saurav Dhital
Claude E. Mack III
Joshua Pepper
Keivan G. Stassun
Department of Physics and Astronomy, Vanderbilt University, VU Station 1807, Nashville, TN 37235, USA.
12
Timothy C. Beers
Verne V. Smith
National Optical Astronomy Observatory, 950 North Cherry Avenue, Tucson, AZ, 85719, USA.
13
Timothy C. Beers
Department of Physics and Astronomy and JINA: Joint Institute for Nuclear Astrophysics, Michigan State University, East Lansing, MI 48824, USA.
14
Alessandra Beifiori
Francesco Montesano
Ariel G. Sanchez
Max-Planck-Institut fur Extraterrestrische Physik, Giessenbachstrae, D-85748 Garching, Germany.
15
Chad F. Bender
W. N. Brandt
Rohit Deshpande
Nurten Filiz Ak
Scott W. Fleming
Samuel Halverson
Suvrath Mahadevan
Arpita Roy
Donald P. Schneider
Ryan C. Terrien
Department of Astronomy and Astrophysics, 525 Davey Laboratory, The Pennsylvania State University, University Park, PA 16802, USA.
16
Chad F. Bender
Rohit Deshpande
Scott W. Fleming
Suvrath Mahadevan
Ryan C. Terrien
Center for Exoplanets and Habitable Worlds, 525 Davey Laboratory, Pennsylvania State University, University Park, PA 16802, USA.
17
Vaishali Bhardwa
Dmitry Bizyaev
Alaina Shelden Bradley
J. Brinkmann
Garrett L. Ebelke
Bruce A. Gillespie
Joseph Huehnerhoff
K. Kinemuchi
Mark A. Klaene
Daniel C. Long
Elena Malanushenko
Viktor Malanushenko
Daniel J. Oravetz
Kaike Pan
Yiping Shu
Audrey E. Simmons
Stephanie A. Snedden
Apache Point Observatory, P.O. Box 59, Sunspot, NM 88349, USA.
18
Dmitry Bizyaev
Garrett L. Ebelke
Diane Feuillet
Sten Hasselquist
Michael Hayden
Jon A. Holtzman
K. Kinemuchi
Young Sun Lee
Daniel C. Long
Elena Malanushenko
Viktor Malanushenko
Nicole P. Vogt
Department of Astronomy, MSC 4500, New Mexico State University, P.O. Box 30001, Las Cruces, NM 88003, USA.
19
Cullen H. Blake
University of Pennsylvania, Department of Physics and Astronomy, 219 S. 33rd St., Philadelphia, PA 19104.
20
Michael R. Blanton
David W. Hogg
Jeremy L. Tinker
Benjamin A. Weaver
Center for Cosmology and Particle Physics, Department of Physics, New York University, 4 Washington Place, New York, NY 10003, USA.
21
Michael Blomqvist
David Kirkby
Daniel Margala
Department of Physics and Astronomy, University of California, Irvine, CA 92697, USA.
22
John J. Bochanski
Haverford College, Department of Physics and Astronomy, 370 Lancaster Avenue, Haverford, PA, 19041, USA.
23
Arnaud Borde
Timothee Delubac
Jean-Marc Le Goff
Nathalie Palanque-Delabrouille
Graziano Rossi
Christophe Yeche
CEA, Centre de Saclay, Irfu/SPP, F-91191 Gif-sur-Yvette, France.
24
Jo Bovy
Institute for Advanced Study, Einstein Drive, Princeton, NJ 08540, USA.
25
Jo Bovy
Beth A. Reid
Hubble fellow.
26
W. N. Brandt
Nurten Filiz Ak
Donald P. Schneider
Institute for Gravitation and the Cosmos, The Pennsylvania State University, University Park, PA 16802, USA.
27
Joleen K. Carlberg
Department of Terrestrial Magnetism, Carnegie Institution of Washington, 5241 Broad Branch Road, NW, Washington DC 20015, USA.
28
Aurelio R. Carnero
Katia Cunha
Luiz N. da Costa
Marcio A. G. Maia
Flavia Sobreira
Observatorio Nacional, Rua Gal. Jose Cristino 77, Rio de Janeiro, RJ - 20921-400, Brazil.
29
Aurelio R. Carnero
Cristina Chiappini
Katia Cunha
Luiz N. da Costa
Leo Girardi
Marcio A. G. Maia
Helio Dotto Perottoni
Rogerio Riffel
H. J. Rocha-Pinto
Baslio Santiago
Flavia Sobreira
Laboratorio Interinstitucional de e-Astronomia, - LIneA, Rua Gal.Jose Cristino 77, Rio de Janeiro, RJ - 20921-400, Brazil.
30
Aurelio R. Carnero
J. Richard Gott, III
James E. Gunn
Craig P. Loomis
Robert H. Lupton
Hironao Miyatake
Prachi Parihar
Michael A. Strauss
Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA.
31
S. Drew Chojnowski
Janice D. R. Dean
Ana E. Garca Perez
Frederick R. Hearty
Steven R. Majewski
David L. Nidever
Robert W. O'Connell
M. F. Skrutskie
Nicholas W. Troup
John C. Wilson
Department of Astronomy, University of Virginia, P.O.Box 400325, Charlottesville, VA 22904-4325, USA.
32
Chia-Hsun Chuang
Ginevra Favole
Francisco Prada
Instituto de Fsica Teorica, (UAM/CSIC), Universidad Autonoma de Madrid, Cantoblanco, E-28049 Madrid, Spain.
33
Johan Comparat
Jean-Paul Kneib
Laboratoire d'Astrophysique de Marseille, CNRSUniversit e de Provence, 38 rue F. Joliot-Curie, F-13388 Marseille cedex 13, France.
34
Justin R. Crepp
Department of Physics, 225 Nieuwland Science Hall, Notre Dame, IN, 46556, USA.
35
Stefano Cristiani
Matteo Viel
INAF, Osservatorio Astronomico di Trieste, Via G. B. Tiepolo 11, I-34131 Trieste, Italy.
36
Stefano Cristiani
Matteo Viel
INFN/National Institute for Nuclear Physics, Via Valerio 2, I-34127 Trieste, Italy.
37
Rupert A.C. Croft
Shirley Ho
Dustin Lang
Ross O'Connell
Mariana Vargas Magana
Bruce and Astrid McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA.
38
Antonio J. Cuesta
Nikhil Padmanabhan
John K. Parejko
Yale Center for Astronomy and Astrophysics, Yale University, New Haven, CT, 06520, USA.
39
Xiaohui Fan
Cameron K. McBride
Benjamin J. Weiner
Steward Observatory, 933 North Cherry Avenue, Tucson, AZ 85721, USA.
40
Saurav Dhital
Department of Physical Sciences, Embry-Riddle Aeronautical University, 600 South Clyde Morris Blvd., Daytona Beach, FL 32114, USA.
41
Anne Ealet
Stephanie Escoffier
Centre de Physique des Particules de Marseille, Aix- Marseille Universite, CNRS/IN2P3, E-13288 Marseille, France.
42
Edward M. Edmondson
Marc Manera
Claudia Maraston
Karen L. Masters
Robert C. Nichol
Will J. Percival
Matthew M. Pieri
Ashley J. Ross
Daniel Thomas
Rita Tojeiro
Gong-Bo Zhao
Institute of Cosmology and Gravitation, Dennis Sciama Building, University of Portsmouth, Portsmouth, PO1 3FX, UK.
43
Daniel J. Eisenstein
Cameron K. McBride
Yue Shen
Molly E. C. Swanson
Harvard-Smithsonian Center for Astrophysics, Harvard University, 60 Garden Street, Cambridge MA 02138, USA.
44
Nurten Filiz Ak
Faculty of Sciences, Department of Astronomy and Space Sciences, Erciyes University, 38039 Kayseri, Turkey.
45
Hayley Finley
Pasquier Noterdaeme
Patrick Petitjean
UPMC-CNRS, UMR7095, Institut d'Astrophysique de Paris, 98bis Boulevard Arago, F-75014, Paris, France.
46
Andreu Font-Ribera
Institute of Theoretical Physics, University of Zurich, 8057 Zurich, Switzerland.
47
Peter M. Frinchaboy
Kelly M. Jackson
Julia E. O'Connell
Benjamin A. Thompson
Department of Physics and Astronomy, Texas Christian University, 2800 South University Drive, Fort Worth, TX 76129, USA.
48
Jian Ge
Peng Jiang
Bo Ma
Department of Astronomy, University of Florida, Bryant Space Science Center, Gainesville, FL 32611-2055, USA.
49
Leo Girardi
Sara Lucatello
INAF, Osservatorio Astronomico di Padova, Vicolo dell'Osservatorio 5, I-35122 Padova, Italy.
50
Paul Harding
Heather L. Morrison
Idit Zehavi
Department of Astronomy, Case Western Reserve University, Cleveland, OH 44106, USA.
51
Klaus Honscheid
Department of Physics, Ohio State University, Columbus, OH 43210, USA.
52
Klaus Honscheid
Jennifer A. Johnson
Center for Cosmology and Astro-Particle Physics, Ohio State University, Columbus, OH 43210, USA.
53
Kelly M. Jackson
Department of Physics, University of Texas-Dallas, Dallas, TX 75080, USA.
54
Peng Jiang
Key Laboratory for Research in Galaxies and Cosmology, University of Science and Technology of China, Chinese Academy of Sciences, Hefei, Anhui, 230026, 

China.
55
Jean-Paul Kneib
Laboratoire d'Astrophysique, Ecole Polytechnique Federale de Lausanne (EPFL), Observatoire de Sauverny, 1290, Versoix, Switzerland.
56
Lars Koesterke
Texas Advanced Computer Center, University of Texas, 10100 Burnet Road (R8700), Austin, Texas 78758-4497, USA.
57
Alexie Leauthaud
Brice Menard
Surhud More
Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU, WPI), Todai Institutes for Advanced Study, The University of Tokyo, Kashiwa, 

277-8583, Japan.
58
Khee-Gan Lee
Max-Planck-Institut fur Astronomie, Konigstuhl 17, D-69117 Heidelberg, Germany.
59
Sarah L. Martell
Australian Astronomical Observatory, PO Box 915, North Ryde NSW 1670, Australia.
60
Richard G. McMahon
Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK.
61
Richard G. McMahon
Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK.
62
Brice Menard
Alfred P. Sloan fellow.
63
Jordi Miralda-Escude
Institucio Catalana de Recerca i Estudis Avancats, Barcelona E-08010, Spain.
64
Jordi Miralda-Escude
Ignasi Perez-Rafols
Institut de Ciencies del Cosmos, Universitat de Barcelona/IEEC, Barcelona E-08028, Spain.
65
Jeffrey A. Munn
US Naval Observatory, Flagsta Station, 10391 West Naval Observatory Road, Flagsta , AZ 86001-8521, USA.
66
Adam D. Myers
Department of Physics and Astronomy, University of Wyoming, Laramie, WY 82071, USA.
67
Duy Cuong Nguyen
Dunlap Institute for Astronomy and Astrophysics, University of Toronto, Toronto, ON, M5S 3H4, Canada.
68
David L. Nidever
Dept. of Astronomy, University of Michigan, Ann Arbor, MI, 48104, USA.
69
Isabelle Paris
Departamento de Astronoma, Universidad de Chile, Casilla 36-D, Santiago, Chile.
70
Joshua Pepper
Department of Physics, Lehigh University, 16 Memorial Drive East, Bethlehem, PA 18015, USA.
71
Ignasi Perez-Rafols
Departament d'Astronomia i Meteorologia, Facultat de Fsica, Universitat de Barcelona, E-08028 Barcelona, Spain.
72
Helio Dotto Perottoni
H. J. Rocha-Pinto
Federal do Rio de Janeiro, Observatorio do Valongo, Ladeira do Pedro Ant^onio 43, 20080-090 Rio de Janeiro, Brazil.
73
Francisco Prada
Campus of International Excellence UAM+CSIC, Cantoblanco, E-28049 Madrid, Spain.
74
Francisco Prada
Instituto de Astrofsica de Andaluca (CSIC), Glorieta de la Astronoma, E-18080 Granada, Spain.
75
Adrian M. Price-Whelan
Department of Astronomy, Columbia University, New York, NY 10027, USA.
76
Rafael Rebolo
Consejo Superior Investigaciones Cient cas, 28006 Madrid, Spain.
77
Rogerio Riffel
Instituto de Fsica, UFRGS, Caixa Postal 15051, Porto Alegre, RS - 91501-970, Brazil.
78
Annie C. Robin
Mathias Schultheis
Universite de Franche-Comte, Institut Utinam, UMR CNRS 6213, OSU Theta, Besancon, F-25010, France.
79
Constance M. Rockosi
Baslio Santiago
UCO/Lick Observatory, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA.
80
Cristiano G. Sabiu
School of Physics, Korea Institute for Advanced Study, 85 Hoegiro, Dongdaemun-gu, Seoul 130-722, Republic of Korea.
81
Ricardo P. Schiavon
Astrophysics Research Institute, Liverpool John Moores University, IC2, Liverpool Science Park 146 Brownlow Hill Liverpool L3 5RF United Kingdom.
82
Katharine J. Schlesinger
Research School of Astronomy and Astrophysics, Australian National University, Weston Creek, ACT, 2611, Australia.
83
Yue Shen
Observatories of the Carnegie Institution of Washington, 813 Santa Barbara Street, Pasadena, CA 91101, USA.
84
Matthew Shetrone
University of Texas, Hobby-Eberly Telescope, 32 Fowlkes Rd, McDonald Observatory, TX 79734-3005, USA.
85
Anze Slosar
Brookhaven National Laboratory, Bldg 510, Upton, NY 11973, USA.
86
Jennifer S. Sobeck
Department of Astronomy and Astrophysics and JINA, University of Chicago, Chicago, IL 60637, USA.
87
Keivan G. Stassun
Department of Physics, Fisk University, 1000 17th Avenue North, Nashville, TN 37208, USA.
88
Michael A. Strauss
W. M. Wood-Vasey
Corresponding authors.
89
David A. Wake
Department of Astronomy, University of Wisconsin- Madison, 475 North Charter Street, Madison WI 53703, USA.
90
Martin White
Department of Physics, University of California, Berkeley, CA 94720, USA.
91
Simon D.M. White
Max-Planck Institute for Astrophysics, Karl- SchwarzschildStr 1, D-85748 Garching, Germany.
92
John P. Wisniewski
H.L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman, OK 73019, USA.
93
W. M. Wood-Vasey
PITT PACC, Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15260, USA.
94
Donald G. York
Department of Astronomy and Astrophysics and the Enrico Fermi Institute, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637, USA.
95
Gong-Bo Zhao
National Astronomy Observatories, Chinese Academy of Science, Beijing, 100012, China.

Draft version January 16, 2014

ABSTRACT

The Sloan Digital Sky Survey (SDSS) has been in operation since 2000 April. This paper presents the tenth public data release (DR10) from its current incarnation, SDSS-III. This data release includes the first spectroscopic data from the Apache Point Observatory Galaxy Evolution Experiment (APOGEE), along with spectroscopic data from the Baryon Oscillation Spectroscopic Survey (BOSS) taken through 2012 July. The APOGEE instrument is a near-infrared R ~ 22,500 300-fiber spectrograph covering 1:514{1:696 um. The APOGEE survey is studying the chemical abundances and radial velocities of roughly 100,000 red giant star candidates in the bulge, bar, disk, and halo of the Milky Way. DR10 includes 178,397 spectra of 57,454 stars, each typically observed three or more times, from APOGEE. Derived quantities from these spectra (radial velocities, effective temperatures, surface gravities, and metallicities) are also included.

DR10 also roughly doubles the number of BOSS spectra over those included in the ninth data release. DR10 includes a total of 1,507,954 BOSS spectra, comprising 927,844 galaxy spectra; 182,009 quasar spectra; and 159,327 stellar spectra, selected over 6373.2 deg2.

Keywords: Atlases|Catalogs|Surveys

1. INTRODUCTION

The Sloan Digital Sky Survey (SDSS) has been in continuous operation since 2000 April. It uses a dedicated wide-field 2.5-m telescope (Gunn et al. 2006) at Apache Point Observatory (APO) in the Sacramento Mountains in Southern New Mexico. It was originally instrumented with a wide-field imaging camera with an effective area of 1.5 deg2 (Gunn et al. 1998), and a pair of double spectrographs fed by 640 fibers (Smee et al. 2013). The initial survey (York et al. 2000) carried out imaging in five broad bands (ugriz) (Fukugita et al. 1996) to a depth of r ~ 22:5 mag over 11,663 deg2 of high-latitude sky, and spectroscopy of 1.6 million galaxy, quasar, and stellar targets over 9380 deg2. The resulting images were calibrated astrometrically (Pier et al. 2003) and photometrically (Ivezic et al. 2004; Tucker et al. 2006; Padmanabhan et al. 2008), and the properties of the detected objects were measured (Lupton et al. 2001). The spectra were calibrated and redshifts and classifications determined (Bolton et al. 2012). The data have been released publicly in a series of roughly annual data releases (Stoughton et al. 2002; Abazajian et al. 2003, 2004, 2005; Adelman-McCarthy et al. 2006, 2007, 2008; Abazajian et al. 2009; hereafter EDR, DR1, DR2, DR3, DR4, DR5, DR6, DR7, respectively) as the project went through two funding phases, termed SDSS-I (2000-2005) and SDSS-II (2005-2008).

In 2008, the SDSS entered a new phase, designated SDSS-III (Eisenstein et al. 2011), in which it is currently operating. SDSS-III has four components. The Sloan Extension for Galactic Understanding and Exploration 2 (SEGUE-2), an expansion of a similar project carried out in SDSS-II (Yanny et al. 2009), used the SDSS spectrographs to obtain spectra of about 119,000 stars, mostly at high Galactic latitudes. The Baryon Oscillation Spectroscopic Survey (BOSS; Dawson et al. 2013) rebuilt the spectrographs to improve throughput and increase the number of fibers to 1000 (Smee et al. 2013). BOSS enlarged the imaging footprint of SDSS to 14,555 deg2, and is obtaining spectra of galaxies and quasars with the primary goal of measuring the oscillation signature in the clustering of matter as a cosmic yardstick to constrain cosmological models. The Multi-Object APO Radial Velocity Exoplanet Large-area Survey (MARVELS), which finished its data-taking in 2012, used a 60-fiber interferometric spectrograph to measure high-precision radial velocities of stars in a search for planets and brown dwarfs. Finally, the Apache Point Observatory Galactic Evolution Experiment (APOGEE) uses a 300-fiber spectrograph to observe bright (H < 13:8 mag) stars in the H band at high resolution (R 22,500) for accurate radial velocities and detailed elemental abundance determinations.

We have previously had two public data releases of data from SDSS-III. The Eighth Data Release (DR8; Aihara et al. 2011) included all data from the SEGUE-2 survey, as well as ~ 2500 deg2 of new imaging data in the Southern Galactic Cap as part of BOSS. The Ninth Data Release (DR9, Ahn et al. 2012) included the rst spectroscopic data from the BOSS survey: over 800,000 spectra selected from 3275 deg2 of sky.

This paper describes the Tenth Data Release (hereafter DR10) of the SDSS survey. This release includes almost 680,000 new BOSS spectra, covering an additional 3100 deg2 of sky. It also includes the first public release of APOGEE spectra, with almost 180,000 spectra of more than 57,000 stars in a wide range of Galactic environments. As in previous SDSS data releases, DR10 is cumulative; it includes all data that were part of DR1-9. All data released with DR10 are publicly available on the SDSS-III website 96 and links from it.

The scope of the data release is described in detail in Section 2. We describe the APOGEE data in Section 3, and the new BOSS data in Section 4. The mechanisms for data access are described in Section 5. We outline the future of SDSS in Section 6.

2. SCOPE OF DR10

DR10 presents the release of the first year of data from the SDSS-III APOGEE infrared spectroscopic survey and the first 2.5 years of data from the SDSS-III BOSS optical spectroscopic survey. In each case these data extend to the 2012 telescope shutdown for the summer monsoon season.

APOGEE was commissioned from 2011 May up through the summer shutdown in 2011 July. Survey-quality observations began 2011 Aug 31 (UTC-7), corresponding to Modified Julian Date (MJD) 55804. The APOGEE data presented in DR10 include all commissioning and survey data taken up to and including MJD 56121 (2012 July 13). However, detailed stellar parameters are only presented for APOGEE spectra obtained after commissioning was complete. The BOSS data include all data taken up to and including MJD 56107 (2012 June 29).

DR10 also includes the imaging and spectroscopic data from SDSS-I/II and SDSS-III SEGUE-2, the imaging data for the BOSS Southern Galactic Cap rst presented in DR8, as well as the spectroscopy from the first 2.5 years of BOSS. Table 1 lists the contents of the data release, including the imaging coverage and number of APOGEE and BOSS plates and spectra. APOGEE plates are observed multiple times ("visits") to build signal-to-noise ratio (S/N) and to search for radial velocity variations; thus the number of spectra in DR10 is significantly larger than the number of unique stars observed. While there are fewer repeat spectra in BOSS, we sill distinguish between the total number of spectra, and the number of unique objects observed in BOSS as well. The numbers for the imaging data, unchanged since DR8, also distinguish between unique and total area and number of detected objects. The multiple repeat observations of the Equatorial Stripe in the Fall sky (Annis et al. 2011), used to search for Type Ia supernovae (Frieman et al. 2008), dominate the difference between total and unique area imaged.

Table 1 Contents of DR10

Optical Imaging a
  Total Unique b
Area Imaged [deg2] 31,637 14,555
Cataloged Objects 1,231,051,050 469,053,874
APOGEE Spectroscopy
  Commiss. Survey Total
Plate-Visits 98 586 684
Plates 51 232 281
Pointings 43 150 170
       
    Spectra Stars
All Stars c    178,397 57,454
Commissioning Stars   24,943 11,987
Survey Stars   153,454 47,452
    Stars with S/N > 100 d   ..... 47,675
    Stars with 3 visits   ..... 29,701
    Stars with 12 visits   ..... 923
    Stellar parameter standards   5,178 1,065
    Radial velocity standards   162 16
    Telluric line standards   24,283 7,003
    Ancillary science program       objects   8894 3344
BOSS Spectroscopy
  Total Unique b
Spectroscopic effective area [deg2] ... 6,373.2
Plates e 1,515 1,489
Optical Spectra observed f 1,507,954 1,391,792
     
All Galaxies 927,844 859,322
   CMASS g 612,195 565,631
   LOWZ g 224,172 208,933
All Quasars 182,009 166,300
   Main h 159,808 147,242
   Main, 2:15 < z < 3:5 i 114,977 105,489
Ancillary program spectra 72,184 65,494
Stars 159327 144968
   Standard stars 30,514 27,003
Sky spectra 144,503 138,491
Unclassied spectra j 101,550 89,003
     
All Optical Spectroscopy from SDSS up through DR10
Total spectra 3,358,200
Total useful spectra k 3,276,914
   Galaxies 1,848,851
   Quasars 316,125
   Stars 736,484
   Sky 247,549
   Unclassiedj 138663

a These numbers are unchanged since DR8

b Removing all duplicates, overlaps, and repeat visits from the \Total" column.

c 2,155 stars were observed both during the commissioning and survey phases. The co-added spectra are kept separate between these two phases. Thus the number of coadded spectra is greater than the number of unique stars observed.

d Signal-to-noise ratio per half resolution element > 100.

e Twenty-six plates of the 1515 observed plates were re-plugged and re-observed for calibration purposes. Six of the 1489 unique plates are different drillings of the same set of objects.

f This excludes the small fraction of the observations through fibers that are broken or that fell out of their holes after plugging. There were 1,515,000 spectra attempted.

g "CMASS" and \LOWZ" refer to the two galaxy target categories used in BOSS (Ahn et al. 2012). They are both color-selected, with LOWZ galaxies in the redshift range 0:15 < z < 0:4, and CMASS galaxies in the range 0:4 < z < 0:8.

h This counts only quasars that were targeted by the main quasar survey (Ross et al. 2012), and thus does not include those from ancillary programs (Dawson et al. 2013).

i Quasars with redshifts in the range 2:15 < z < 3:5 provide the most signal in the BOSS spectra of the Ly- forest.

j Non-sky spectra for which the automated redshift/classification pipeline (Bolton et al. 2012) gave no reliable classification, as indicated by the ZWARNING ag.

k Spectra on good or marginal plates.

New in DR10 are morphological classifications of SDSS images of galaxies by 200,000 citizen scientists via the Galaxy Zoo project (Lintott et al. 2008, 2011; Willett et al. 2013). These classifications include both the basic (spiral{early-type) morphologies for all 1 million galaxies from the SDSS-I/II Main Galaxy Sample (Strauss et al. 2002), as well as more detailed classifications of the internal structures in the brightest 250,000 galaxies.

The celestial footprint of the APOGEE spectroscopic coverage in DR10 is shown in Figure 1 in Galactic coordinates; Figure 2 repeats this in Equatorial coordinates, and shows the imaging and BOSS spectroscopy sky coverage as well. The distribution on the sky of SDSS-I/II and SEGUE-2 spectroscopy is not shown here; see the DR7 and DR8 papers. APOGEE fields span all of the Galactic components visible from APO, including the Galactic center and disk, as well as fields at high Galactic latitudes to probe the halo. The Galactic center observations occur at high airmass, thus the differential atmospheric refraction across the field of view changes rapidly with hour angle. Therefore targets in these fields are not distributed over the full 7 deg2 of each plate, but rather over a smaller region from 0.8 to 3.1 deg2, as indicated by the smaller dots in Figure 1. The clump of points centered roughly at l = 75 degrees; b = +15 degrees are special plates targeting stars previously observed by NASA's Kepler mission, as described in detail in Section 3.4.

Figure 1. The distribution on the sky of all APOGEE DR10 pointings in Galactic coordinates

the Galactic Center is in the middle of the diagram. Each circle represents a pointing. APOGEE often has several distinct plates for a single location on the sky; DR10 includes 170 locations, which are shown above. Smaller circles (primarily near the Galactic Center) represent locations where plates were drilled over only a fraction of the 7 deg2 focal plane to minimize differential atmospheric refraction. Note the concentration of fields along the Galactic Plane. The concentration of pointings at l = 75 degrees; b = +15 degrees is a special program targeting stars observed by the Kepler telescope; see Section 3.4. (top) Distribution of pointings in both the commissioning and survey phases (both are included in DR10). (bottom) Pointings distinguished by the number of visits obtained by DR10 in the survey phase.

DR10PaperFigure1.png

Figure 2. The distribution on the sky of all SDSS imaging

(top; 14,555 deg- same as DR8 and DR9) and BOSS and APOGEE DR10 spectroscopy (bottom; 6373.2 deg2) in J2000 equatorial coordinates (alpha = 0 degrees is right of center in this projection). Grey shows regions included in DR9; the increment included in DR10 is in red. The blue shows the positions of APOGEE pointings included in DR10. The Galactic Plane is shown by the dotted line. The Northern Galactic Cap is on the left of the figure, and the Southern Galactic Cap on the right. The BOSS sky coverage shown is actually constructed using a random subsample of the BOSS DR10Q quasar catalog (Paris et al. 2013). The sky below zeta < -30 degrees is never at an airmass of of less than 2.0 from APO (latitude=+32 degrees 46 minutes 49 seconds).

DR10PaperFigure2.png

The additional BOSS spectroscopy fills in most of the "doughnut" defined by the DR9 coverage in the North Galactic Cap. The DR10 BOSS sky coverage relative to the 10,000 deg2 full survey region is described further in Section 4.

3. THE APACHE POINT OBSERVATORY GALAXY EVOLUTION EXPERIMENT (APOGEE)

3.1. Overview of APOGEE

Stellar spectra of red giants in the H band (1.5 - 1.8 um) show a rich range of absorption lines from a wide variety of elements. At these wavelengths, the absorption due to dust in the plane of the Milky Way is much reduced compared to that in the optical bands. A high-resolution study of stars in the H band allows studies of all components of the Milky Way, across the disk, in the bulge, and out to the halo.

APOGEE's goal is to trace the history of star formation in, and the assembly of, the Milky Way by obtaining H-band spectra of 100,000 red giant candidate stars throughout the Galaxy. Using an infrared multi-object spectrograph with a resolution of R = Lambda/Delta Lambda ~ 22,500, APOGEE can survey the halo, disk, and bulge in a much more uniform fashion than previous surveys. The APOGEE spectrograph features a 50.8 cm x 30.5 cm mosaiced volume-phase holographic (VPH) grating and a six-element camera having lenses with a maximum diameter of 40 cm. APOGEE takes advantage of the ber infrastructure on the SDSS telescope, using 300 bers, each subtending 200 on the sky, distributed over the full 7 deg2 field of view (with the exception of plates observed at high airmass, as noted above). The spectrograph itself sits in a temperature-controlled room, and thus does not move with the telescope. The light from the fibers falls onto three HAWAII-2RG 2K 2K infrared detectors (Garnett et al. 2004; Rieke 2007), that cover the wavelength range from 1.514 m to 1.696 um, with two gaps (see Section 3.2 for details). APOGEE targets are chosen with magnitude and color cuts from photometry of the Two-Micron All-Sky Survey (2MASS; Skrutskie et al. 2006), with a median H = 10:9 mag and with 99.6% of the stars brighter than H = 13:8 mag (on the 2MASS Vega-based system).

The high resolution of the spectra and the stability of the instrument allow accurate radial velocities with a typical uncertainty of 100 m s-1, and detailed abundance determinations for approximately 15 chemical elements. In addition to being key in identifying binary star systems, the radial velocity data are being used to explore the kinematical structure of the Milky Way and its substructures (e.g., Nidever et al. 2012) and to constrain dynamical models of its disk (e.g., Bovy et al. 2012). The chemical abundance data allow studies of the chemical evolution of the Galaxy (Garca Perez et al. 2013) and the history of star formation. The combination of kinematical and chemical data will allow important new constraints on the formation history of the Milky Way.

A full overview of the APOGEE survey will be presented in S. Majewski et al. (2014, in preparation). The APOGEE instrument will be detailed in J. Wilson et al. (2014, in preparation) and is summarized here in Section 3.2. The target selection process for APOGEE is described in Zasowski et al. (2013) and is presented in brief here in Section 3.3. In Section 3.4 we describe a unique cross-targeting program between SDSS-III APOGEE and asteroseismology measurements from the NASA Kepler telescope 97 (Gilliland et al. 2010). Section 3.5 describes the reduction pipeline that processes the APOGEE data and produces calibrated one-dimensional spectra of each star, including accurate radial velocities (D. Nidever et al., 2014, in preparation). Important caveats regarding APOGEE data of which potential users should be aware are described in Section 3.6. Section 3.7 describes the pipeline that measures stellar properties and elemental abundances - the APOGEE Stellar Parameters and Chemical Abundances Pipeline (ASPCAP; M. Shetrone et al., 2014, in preparation; A. Garca-Perez et al., 2014, in preparation, Meszaros et al. 2013). Section 3.8 summarizes the APOGEE data products available in DR10.

3.2. The APOGEE Instrument and Observations

The APOGEE spectrograph measures 300 spectra in a single observation: roughly 230 science targets, 35 on blank areas of sky to measure sky emission, and 35 hot, blue stars to calibrate atmospheric absorption. This multiplexing is accomplished using the same aluminum plates and ber optic technology as have been used for the optical spectrograph surveys of SDSS. Each plate corresponds to a specic patch of sky, and is pre-drilled with holes corresponding to the sky positions of objects in that area, meaning that each area requires one or more unique plates.

The APOGEE spectrograph uses three detectors to cover the H-band range, "blue": 1.514{1.581 um, "green": 1.585 - 1.644 um, and "red": 1.647 - 1.696 um. There are two gaps, each a few nm wide, in wavelength in the spectra. The spectral line spread function spans 1.6 - 3.2 pixels per spectral resolution element FWHM, increasing from blue to red across the detectors. Thus most of the blue detector is under-sampled. Figure 3 shows the results of a typical exposure. Each observation consists of at least one "AB" pair of exposures for a given pointing on the sky, with the detector array mechanically oset by 0.5 pixels along the dispersion direction between the two exposures. This well-controlled sub-pixel dithering allows the derivation of combined spectra with approximately twice the sampling of the individual exposures. Thus the combined spectra are properly sampled, including all wavelengths from the blue detector. The actual line spread function as a function of wavelength is provided as a Gauss-Hermite function for each APOGEE spectrum in DR10.

Figure 3. (top) A 2D spectrogram from the APOGEE instrument

The three chips ("blue", "green", and "red") are shown with wavelength increasing to the right across the full APOGEE wavelength range of 1.514 - 1.696 um. The gaps between the chips are slightly larger than as displayed in this image. Each fiber is imaged onto several pixels (vertically). Note the vertical series of points from sky lines in each fiber, and the horizontal spectra of faint stars and sky fibers. (bottom) Expanded view of the central 18 fibers and central 6 nm of each chip.

DR10PaperFigure3.png

A typical observation strategy is two "ABBA" sequences. Each sequence consists of four 500-second exposures to reach the target S/N for a given observation. The combination of all "AB" or "BA" pairs for a given plate during a night is called a "visit." The visit is the basic product for what are considered individual spectra for APOGEE (although the spectra from the individual exposures are also made available). While the total exposure time for a visit is 4,000 seconds (24500 seconds), due to the varying lengths of night and other scheduling issues, we often gathered more or less than the standard two "ABBA" sequences on a given plate in a night. APOGEE stars are observed over multiple visits (the goal is at least three visits) to achieve the planned S/N. Figure 4 shows the distribution of the number of visits for stars included in DR10; presently, most stars have three or fewer visits, but this distribution will broaden with the final data release. These visits are separated across different nights and often different seasons, allowing us to look for radial velocity variability due to binarity on a variety of timescales. The distribution of time intervals between visits is shown in Figure 5, with peaks at one and two lunations (30 and 60 days).

Figure 4. The distribution of number of spectroscopic visits for APOGEE stars included in DR10

While the bulk of stars have three or fewer visits, they may have reached our spectral S/N requirement if they are bright enough; see Figure 7.

DR10PaperFigure4.png

Figure 5. The distribution of time between visits for APOGEE stars, useful for determining the sensitivity to radial velocity variations due to binarity

This quantity is the absolute value of the time difference for all unique pairs of visits for each star. The most prominent peaks are at one and two months.

DR10PaperFigure5.png

Each visit is uniquely identified by the plate number and MJD of the observation. Plates are generally re-plugged between observations, so while "plate+MJD+fiber" remains a unique identifier in APOGEE spectra as it is in optical SDSS spectra, "plate+fiber" does not refer to the same object across all visits. The spectra from all visits are co-added to produce the aggregate spectrum of the star. The final co-added spectra are processed by the stellar parameters pipeline described in Section 3.7.

The aim is for a final co-added spectrum of each star with a S/N of > 100 per half-resolution element. 98 Figures 6 and 7 show the distribution of S/N; not surprisingly, S/N is strongly correlated with the brightness of the star. The DR10 data include some stars that have yet to receive their full complement of visits and thus have significantly lower quality spectra. Future data releases will include additional visits for many stars, leading to an increase in total co-added S/N as well as more refined stellar parameters.

Figure 6. Reported S/N per pixel of APOGEE DR10 co-added stellar spectra

Repeated observations imply that there is a practical limit of S/N ~ 200 in the co-added spectra, shown as the dot-dashed line. The dashed line denotes the goal of S/N ~ 100 per half-resolution element, corresponding to S/N ~ 80 per pixel in the co-added spectra.

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Figure 7. S/N per pixel of spectra of stars as a function of their apparent H-band magnitude

(density is on a log scale). The vertical dot-dashed lines indicate the magnitude limits for stars at each value of the final number of visits: 1, 3, 6, 12, 24 visits for H = 11:0, 12.2, 12.8, 13.3, and 13.8 mag. The horizontal dashed line denotes the target S/N ~ 100 per half-resolution element, corresponding to S/N ~ 80 per pixel in the co-added spectra.

DR10PaperFigure7.png

The APOGEE plates are drilled with the same plate-drilling machines used for BOSS, and the plate numbers are sequential. This scheme means that the BOSS and APOGEE plate numbers are interleaved and that no plate number is assigned to both a BOSS and APOGEE plate.

The quality of the APOGEE commissioning data (that taken prior to 2011 Aug 31) is lower than the survey data, due to optical distortions and focus issues that were resolved before the ocial survey was started. The biggest difference lies in the \red" chip, which has significantly worse spectral resolution in the commissioning data than in the survey data. Because of this degradation, the data were not under-sampled, and spectral dithering was not done during commissioning.

Many of the targets observed in commissioning were selected in the same way as those observed during the survey (Section 3.3), though several test plates were designed with different criteria to test the selection algorithms (e.g., without a color limit or with large numbers of potential telluric calibration stars). Total exposure times for the commissioning plates were similar to those of the survey plates. Because the spectral resolution of commissioning data is worse, it cannot be analyzed using ASPCAP with the same spectral libraries with which the survey data are analyzed. As a result, DR10 does not release any stellar parameters other than radial velocities for commissioning data; subsequent releases may include stellar parameters for APOGEE commissioning derived using appropriately matched libraries and/or with only a subset of the spectral range.

3.3. APOGEE Main and Ancillary Targets

APOGEE main targets are selected from 2MASS data (Skrutskie et al. 2006) using apparent magnitude limits to meet the S/N goals and a dereddened color cut of (J - Ks)0 > 0:5 mag to select red giants in multiple components of the Galaxy: the disk, bulge, and halo. This selection results in a sample of objects that are predominantly red giant stars with 3500 < Teff < 5200 K and log g < 3:5 (where g is in cm s-2 and the logarithm is base 10). Fields receiving three visits have a magnitude limit of H = 12:2; the deepest plates with 24 visits go to H = 13:8.

APOGEE has also implemented a number of ancillary programs to pursue specic investigations enabled by its unique instrument. The selection of the main target sample and the ancillary programs, together with the bit ags that can be used to identify why an object was targeted for spectroscopy, are described in detail in Zasowski et al. (2013). In DR10, APOGEE stars are named based on a slightly shortened version of their 2MASS ID (e.g., "2M21504373+4215257" is stored for the formal designation "2MASS 21504373+4215257"). A few objects that don't have 2MASS IDs are designated as "AP", followed by their coordinates.

APOGEE targets were chosen in a series of fields designed to sample a wide range of Galactic environments (Figure 1): in the halo predominantly at high latitudes, in the disk, in the central part of the Milky Way (limited in declination), as well as special targeted fields overlapping the Kepler survey (Section 3.4), and a variety of open and globular clusters with well-characterized metallicity in the literature.

The effects of Galactic extinction on 2MASS photometry can be quite signfiicant at low Galactic latitude. We correct for this using the Spitzer IRAC GLIMPSE survey (Benjamin et al. 2003; Churchwell et al. 2009) and the Wide-field Infrared Survey Explorer (WISE; Wright et al. 2010) Lambda = 4:5 um data following the Rayleigh- Jeans Color Excess Method described in Majewski et al. (2011) and Zasowski et al. (2013) using the color extinction curve from Indebetouw et al. (2005). Figure 8 shows the measured and reddening-corrected JHKs color-color and magnitude-color diagrams for the APOGEE stars included in DR10.

Figure 8. Two-dimensional histogram of the APOGEE DR10 stars

in (top) 2MASS JHKs color space; and (bottom) 2MASS H vs. JHKs. The left column shows observed magnitudes and colors from 2MASS, while the right column has been dereddened based on H - 4:5 um color as in Zasowski et al. (2013). The vertical dashed line at (J - Ks)0 = 0:5 shows the selection of the main APOGEE red giant sample; bluer objects include telluric calibration stars, data taken during commissioning, and ancillary program targets. The grey scale is logarithmic in number of stars.

DR10PaperFigure8.png

In regions of high interstellar extinction, even intrinsically blue main sequence stars can be reddened enough to overlap the nominal red giant locus. Dereddening these apparent colors allows us to remove these dwarfs with high efficiency from the final targeted sample. However, G and K dwarfs cannot be distinguished from red giants on the basis of their dereddened broadband colors, with the result that a fraction of the APOGEE sample is composed of such dwarfs. In the disk they are expected to comprise less than 20% of the sample, and this appears to be validated by our analysis of the spectra. Disk dwarfs are expected to be a larger contaminant in halo fields, so in many of these, target selection was supplemented by Washington and intermediate-band DDO51 photometry (Canterna 1976; Clark & McClure 1979; Majewski et al. 2000) using the 1.3-m telescope of the U.S. Naval Observatory, Flagsta Station. Combining this with 2MASS photometry allows us to distinguish dwarfs and giants (see Zasowski et al. 2013 for details).

Exceptions to the (J - Ks)0 > 0:5 mag color limit that appear in DR10 include the telluric calibration stars, early-type stars targeted in well-studied open clusters, stars observed on commissioning plates that did not employ the color limit, and stars in sparsely populated halo fields where a bluer color limit of (J - Ks)0 > 0:3 mag was employed to ensure that all fibers were utilized. Ancillary program targets may also have colors and magnitudes beyond the limits of APOGEE's normal red giant sample.

3.4. APOKASC

Non-radial oscillations are detected in virtually all red giants targeted by the Kepler mission (Borucki et al. 2010; Hekker et al. 2011), and the observed frequencies are sensitive diagnostics of basic stellar properties such as mass, radius, and age (for a review, see Chaplin & Miglio 2013). Abundances and surface gravities measured from high-resolution spectroscopy of these same stars are an important test of stellar evolution models, and allow observational degeneracies to be broken.

With this in mind, the "APOKASC" collaboration was formed between SDSS-III and the Kepler Astero-seismology Science Collaboration (KASC) to analyze APOGEE spectra for ~ 10; 000 stars in fields observed by the Kepler telescope (see Figure 1). The joint measurement of masses, radii, ages, evolutionary states, and chemical abundances for all these stars will enable significantly enhanced investigations of Galactic stellar populations and fundamental stellar physics.

DR10 presents 4,204 spectra of 2,308 stars of the anticipated final APOKASC sample. Astero-seismic data from the APOKASC collaboration were used to calibrate the APOGEE spectroscopic surface gravity results for all APOGEE stars presented in DR10 (Meszaros et al. 2013). A joint astero-seismic and spectroscopic value-added catalog will be released separately (M. Pinsonneault et al., 2014, in preparation).

3.5. APOGEE Data Analysis

The processing of the two-dimensional spectrograms and extraction of one-dimensional co-added spectra will be fully described in D. Nidever et al. (2014, in preparation). We provide here a brief summary to help the reader understand how individual APOGEE exposures are processed. A 500-second APOGEE exposure actually consists of a series of non-destructive readouts every 10.7 seconds that result in a three-dimensional data cube. The first step in processing is to extract a two-dimensional image from a combination of these measurements. After dark current subtraction, the "up-theramp" values for each pixel are fit to a line to derive the count rate for that pixel. Cosmic rays create characteristic jumps in the \up-the-ramp" signal that are easily recognized, removed, and flagged for future reference. The count rate in each pixel is multiplied by the exposure time to obtain a two-dimensional image. These two-dimensional images are then dark-subtracted and flat-fielded. One-dimensional spectra are extracted simultaneously for the entire set of 300 fibers based on wavelength and prole ts from flat-field calibration images. Both the at-eld response and spectral traces are very stable due to the controlled environment of the APOGEE instrument, which has been under vacuum and at a uniform temperature continuously since it was commissioned. Wavelength calibration is performed using emission lines from thorium-argon and uranium-neon hollow cathode lamps. The wavelength solution is then adjusted from the reference lamp calibration on an exposure-to-exposure basis using the location of the night sky lines.

The individual exposure spectra are then corrected for telluric absorption and sky emission using the sky spectra and telluric calibration star spectra, and combined accounting for the dither oset between each "A" and "B" exposure. This combined visit spectrum is flux-calibrated based on a model of the APOGEE instrument's response from observations of a blackbody source. The spectrum is then scaled to match the 2MASS measured apparent H-band magnitude. A preliminary radial velocity is measured after matching the visit spectrum to one from a pre-computed grid of synthetic stellar spectra, and is stored with the individual visit spectrum.

In addition to the individual visit spectra, the APOGEE software pipeline coadds the spectra from different visits to the same eld, yielding a higher S/N spectrum of each object. Figure 9 shows examples of high S/N co-added flux-calibrated spectra from APOGEE for stars with a range of Teff and with a range of [M/H]. A final and precise determination of the relative radial velocities on each visit is determined from cross-correlation of each visit spectrum with the combined spectrum; the velocities are put on an absolute scale by cross-correlating the combined spectrum with the best-matching spectrum in a pre-computed synthetic grid. The combined spectra are output on a rest-wavelength scale with logarithmically spaced pixels with approximately three pixels per spectral resolution element.

Figure 9. Typical APOGEE spectra at high S/N

(left) Spectra of stars with 5000 K> Teff > 3750 K at constant [M/H]= - 0:2 (a characteristic [M/H] for the sample). The trend in line intensity from top to bottom is driven by decreasing Teff (which is strongly correlated with log g { see Figure 11). (right) Spectra of stars with - 1:4 <[M/H]< +0:4 at constant Teff ~ 4650 K (a characteristic Teff for the sample). The trend of increasing absorption lines in the spectra from top to bottom is driven by the increasing [M/H]. All of these spectra have a reported S/N of at least 200 per co-added re-sampled pixel: each of the observed absorption lines in the spectra are real features of the observed stars. The apparent emission lines are actually residuals from the incomplete subtraction of airglow lines.

DR10PaperFigure9.png

3.6. Issues with APOGEE Spectra

Users should be aware of several features and potential issues with the APOGEE data. This is the rst data release for APOGEE; the handling of some of these issues by the pipelines may be improved in subsequent data releases.

Many of these issues are documented in the data by the use of bitmasks that flag various conditions. For the APOGEE spectral data, there are two bitmasks that accompany the main data products Each one-dimensional extracted spectrum includes a signal, uncertainty, and mask arrays. The mask array is a bitmask, APOGEE PIXMASK 99, that flags data-quality conditions that affect a given pixel. A non-zero APOGEE PIXMASK value for a pixel indicates a potential data-quality concern that affects that pixel. Each stellar-parameters analysis of each star is accompanied by a single bitmask, APOGEE STARFLAG 100, that flags conditions at the full spectrum level.

The most important data-quality features to be aware of include:

Gaps in the spectra: There are gaps in the spectra corresponding to the regions that fall between the three detectors. There are additional gaps due to bad or hot pixels on the arrays. As multiple dithered exposures are combined to make a visit spectrum, values from missing regions cannot be used to calculate the dither-combined signal in nearby pixels; as a result, these nearby pixels are set to zero and the BADPIX bit is set for these pixels in APOGEE PIXMASK. Generally, the bad pixels affect neighboring pixels only at a very low level, and the data in the latter may be usable; in subsequent data releases, we will preserve more of the data, while continuing to identify potential bad pixels in the pixel mask.

Imperfect night-sky-line subtraction: The Earth's atmosphere has strong and variable emission in OH lines in the APOGEE bandpass. At the location of these lines, the sky ux is many times brighter than the stellar ux for all except the brightest stars. Even if the sky subtraction algorithm were perfect, the photon noise at the positions of these sky lines would dominate the signal, so there is little useful information at the corresponding wavelengths. The spectra in these regions can show significant sky line residuals. These regions are masked for the stellar parameter analysis so that they do not impact the results. The affected pixels have the SIG SKYLINE bit set in APOGEE PIXMASK.

Error arrays do not track correlated errors: APOGEE spectra from an individual visit are made by combining multiple individual exposures taken at different dither positions. Because the dithers are not spaced by exactly 0.5 pixels, there is some correlation between pixels that is introduced when combined spectra are produced. The error arrays for the visit spectra do not include information about these correlations. In the visit spectra, these correlations are generally small because the dither mechanism is generally quite accurate. However, when multiple visit spectra are combined to make the nal combined spectra, they must be re-sampled onto a common wavelength grid, taking into account the different observer-frame velocities of each individual visit. This re-sampling introduces signicant additional correlated errors between adjacent pixels that are also not tracked in the error arrays.

Error arrays do not include systematic error floors: The errors that are reported for each spectrum are derived based on propagation of Poisson and readout noise. However, based on observations of bright hot stars, we believe that other, possibly systematic, uncertainties currently limit APOGEE observations to a maximum S/N per half resolution element of ~ 200. The error arrays published in DR10 currently report the estimated errors without any contribution from a systematic component. However, for the ASPCAP analysis, we impose an error floor corresponding to 0.5% of the continuum level.

Fiber crosstalk: While an eort is made not to put faint stars adjacent to bright ones on the detector to avoid excessive spillage of light from one to the other, this occasionally occurs. We flag objects (in APOGEE STARFLAG) with a BRIGHT NEIGHBOR flag if an adjacent star is > 10 times brighter than the object, and with a VERY BRIGHT NEIGHBOR flag if an adjacent star is > 100 times brighter; in the latter case, the individual spectra are marked as bad and are not used in combined spectra.

Persistence in the \blue" chip: There is a known "superpersistence" in 1/3 of the region of the \blue" APOGEE data array, and to a lesser extent in some regions of the "green" chip, whereby some of the charge from previous exposures persists in subsequent exposures. Thus the values read out in these locations depend on the previous exposure history for that chip. The effect of super-persistence can vary significantly, but residual signal can amount to as much as 10{ 20% of the signal from previous exposures. The current pipeline does not attempt to correct for this effect; any such correction is likely to be rather complex. For the current release, pixels known to be affected by persistence are flagged in APOGEE PIXMASK at three different levels (PERSIST LOW, PERSIST MEDIUM, PERSIST HIGH). Spectra that have signicant numbers of pixels (> 20% of total pixels) that fall in the persistence region have comparable bits set in the APOGEE STARFLAG bitmask to warn that the spectra for these objects may be contaminated. In a few cases, the eect of persistence is seen dramatically as an elevated number of counts in the blue chip relative to the other arrays; these are flagged as PERSIST JUMP POS in APOGEE STARFLAG. We are still actively investigating the effect of persistence on APOGEE spectra and derived stellar parameters, and are working on corrections that we intend to implement for future data releases.

3.7. APOGEE Stellar Parameter and Chemical Abundances Pipeline (ASPCAP)

The ultimate goal of APOGEE is to determine the effective temperature, surface gravity, overall metallicity, and detailed chemical abundances for a large sample of stars in the Milky Way. Stellar parameters and chemical abundances are extracted from the continuum-normalized co-added APOGEE spectra by comparing with synthetic spectra calculated using state-of-the-art model photospheres (Meszaros et al. 2012) and atomic and molecular line opacities (Shetrone et al., in preparation).

Analysis of high-resolution spectra is traditionally done by hand. However, given the sheer size of APOGEE's spectral database, automatic analysis methods must be implemented. For that purpose, ASPCAP searches for the best fitting spectrum through 2 minimization within a pre-computed multi-dimensional grid of synthetic spectra, allowing for interpolation within the grid. The output parameters of the analysis are effective temperature (Teff ), surface gravity (log g), metallicity ([M/H]), and the relative abundances of elements ([Alpha/M]) 101, carbon ([C/M]), and nitrogen ([N/M]). The micro-turbulence quoted in the DR10 results is not an independent quantity, but is instead calculated directly from the value of log g. Figure 10 shows an example ASPCAP t to an APOGEE spectrum of a typical star. ASPCAP will be fully described in an upcoming paper (A. Garca Perez et al., 2014, in preparation).

Figure 10. (upper lines) An example ASPCAP fit (red) to a typical APOGEE co-added stellar spectrum (black). (lower lines)

Residual of the ASPCAP model t compared to the data (oset from zero by +0.4 units for clarity of presentation). (inset) Zoom on a region showing the high resolution of the actual data. The H-band spectrum contains a wealth of information about the elemental abundances and stellar parameters of the star. The high resolution and high S/N of APOGEE spectra allow these atmospheric properties to be measured for the entire APOGEE sample.

DR10PaperFigure10.png

Chemical composition parameters are dened as follows. The abundance of a given element X is defined relative to solar values in the standard way:

[X=H] = log10(nX=nH)star - log10(nX=nH) ; (1)

where nX and nH are respectively the numbers of atoms of element X and hydrogen, per unit volume, in the stellar photosphere. The parameter [M/H] is dened as an overall metallicity scaling, assuming the solar abundance pattern. The deviation of the abundance of element X from that pattern is given by

[X=M] = [X=H] - [M=H] : (2)

The Alpha elements considered in the APOGEE spectral libraries are O, Ne, Mg, Si, S, Ca, and Ti, and [Alpha/H] is defined as an overall scaling of the abundances of those elements, where they are assumed to vary together while keeping their relative abundances xed at solar ratios. For DR10, we allow four chemical composition parameters to vary: the overall metallicity, and the abundances of elements, carbon, and nitrogen. Carbon, nitrogen, and oxygen contribute significantly to the opacity in APOGEE spectra of cool giants, particularly in the form of molecular lines due to OH, CO, and CN.

3.7.1. Parameter Accuracies

Meszaros et al. (2013) have compared the outputs of ASPCAP to stellar parameters in the literature for stars targeted by APOGEE in open and globular clusters spanning a wide range in metallicity. These comparisons uncovered small systematic differences between ASPCAP and literature results, which are mostly based on high-resolution optical spectroscopy. These differences are not entirely understood yet, and we hope they will be corrected in future data releases. In the meantime, calibrations have been derived to bring APOGEE and literature values into agreement. With these osets in place, the APOGEE metallicities are accurate to within 0.1 dex for stars of S=N > 100 per half-resolution element that lie within a strict range of Te , log g, and [M/H]. Based on observed scatter in the ASPCAP calibration clusters, we estimate that the internal precision of the APOGEE measurements is 0.2 dex for log g, 150 K for Teff , and 0.1 dex for [Alpha/M] (see Meszaros et al. 2013, for details).

Because most of the observed cluster stars are giants, the applied calibration osets only apply to giants. The parameters of dwarfs are generally accurate enough to determine that they are indeed higher surface gravity stars, but otherwise their parameters are likely to be more uncertain: one reason for this is that rotation is likely to be important for a larger fraction of these stars, and the effects of rotation are not currently included in our model spectral libraries.

APOGEE mean values per cluster of [/M] are in good agreement with those in the literature. However, there are systematic correlations between [/M] and both [M/H] and Teff for stars outside the range - 0:5 [M/H] 0:1. Moreover, important systematic effects may be present in [Alpha/M] for stars cooler than Teff 4200 K. We therefore discourage use of [Alpha/M] for stars with Teff < 4200 K or with [M/H]< - 0:5 or [M/H]> +0:1.

Figure 15 in Meszaros et al. (2013) shows the root-mean square scatter in [Alpha/M] for red giants in open and globular clusters, as a measure of the uncertainty in this parameter. However, given the trends in [Alpha/M] with other stellar parameters, care should be taken when estimating the accuracy of [Alpha/M].

Comparison with literature values for carbon and nitrogen abundances shows large scatter and signicant systematic differences. In view of the relative paucity and uncertainty of literature data for these elements, more work is needed to understand these systematic and random differences before APOGEE abundances for carbon and nitrogen can be confidently adopted in science applications.

3.7.2. ASPCAP Outputs

In DR10, we provide calibrated values of effective temperature, surface gravity, overall metallicity, and [Alpha/M] for giants. In addition, we provide the raw ASPCAP results (uncalibrated, and thus, to some extent, unvalidated) for all six parameters for all stars with survey-quality data. Since commissioning data have lower resolution, different spectral libraries are needed to derive stellar parameters from them, and therefore ASPCAP results are not provided for these spectra at this time. For all stars with ASPCAP results, we also provide information about the quality of the fit (X2) and several bitmasks (APOGEE ASPCAPFLAG and APOGEE PARAMFLAG) that ag several conditions that may cause the results to be less reliable. Among these conditions are abnormally high X2 in the fit, best-fit parameters near the edges of the searched range, evidence in the spectrum of significant stellar rotation, and so on. Users should check the values of these bitmasks before using the ASPCAP parameters.

Figure 11 shows the distribution of stellar properties derived by ASPCAP for stars included in DR10. The ASPCAP spectral libraries are currently only calibrated in the range 3610 < Teff < 5255 K. Thus the reliable ASPCAP Teff reported values lie only in this range, with a peak at about 4800 K. The surface gravity distribution peaks at log g ~ 2:5, corresponding to red clump stars, and is strongly correlated with surface temperature. The ASPCAP models are calibrated in the range - 0:5 < log g < 3:6, which is reflected in the range shown. Because of the strong concentration of targeted elds to the Galactic plane (Figure 1), the metallicity distribution peaks just below solar levels, with a tail extending from [M=H] ~ - 0:5 to below - 2:3. The [Alpha/M] abundance distribution has both Alpha-rich and Alpha-poor stars, which reflects the variety of populations explored by APOGEE.

Figure 11. The one-dimensional and two-dimensional distributions of APOGEE stellar parameters

-- temperatures, surface gravities, metallity, and [Alpha/M] | for all 29,438 APOGEE stars in DR10 which have reliable ASPCAP ts. The [Alpha/M] values are only shown for the 16,066 star subset with Teff > 4200 K and - 0:5 <[M/H]< +0:1, which is the range for which [Alpha/M] values are reliable (limits are indicated by red dashed lines; see Section 3.7 for details). These distributions show what APOGEE has observed and ASPCAP has analyzed. They do not represent a fair sample of the underlying Galactic populations.

DR10PaperFigure11.png

Figure 12 shows the excellent agreement of the ASPCAP log g, Teff , and [M/H] values with the isochrone models of Bressan et al. (2012).

Figure 12. ASPCAP log g vs. Teff with the points color-coded by [M/H]

Overplotted are isochrones for a 4 Gyr population of RGB stars with [Alpha/Fe]= 0 from Bressan et al. (2012) on the same color-coded metallicity scale. The isochrones are for [M/H] = - 1:9; - 1:0; - 0:58, and +0.14 from left to right.

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3.8. APOGEE Data Products

The APOGEE data as presented in DR10 are available as the individual 500-second spectra taken on a per-exposure basis (organized both by object and by plate+MJD+fiber), as combined co-added spectra on a per-object basis, and as continuum-normalized spectra used by the APOGEE pipeline (ASPCAP) when it computes stellar properties (Section 3.7). The individual raw exposure les, processed spectra, and combined summary les of stellar parameters are provided as FITS 102 files (Wells et al. 1981) through the DR10 Science Archive Server (SAS). The DR10 Catalog Archive Server (CAS) provides the basic stellar parameters (including the radial velocity) from the APOGEE spectra on a per-visit (SQL table apogeeVisit) and a co-added star basis (SQL table apogeeStar). The ASPCAP results are provided in the SQL table aspcapStar; the covariances between these parameters are given in a separate table, aspcapStarCovar.

To allow one to recreate the sample selection, all of the parameters used in selecting APOGEE targets are provided in DR10 in the SQL table apogeeObject.

Example queries for APOGEE data using the CAS are provided as part of the DR10 web documentation 103.

4. THE BARYON OSCILLATION SPECTROSCOPIC SURVEY (BOSS)

An overview of the BOSS survey is presented in detail in Dawson et al. (2013), and the instrument is described in Smee et al. (2013). BOSS is obtaining spectra of 1.5 million galaxies (Ahn et al. 2012), and 150,000 quasars with redshifts between 2.15 and 3.5 (Ross et al. 2012), selected from 10,000 deg2 of SDSS imaging data. The large-scale distribution of galaxies and the structure in the quasar Lyman alpha forest, allow measurements of the baryon oscillation signature as a function of redshift (Anderson et al. 2012, 2013; Busca et al. 2013). In addition, about 5% of the fibers are devoted to a series of ancillary programs with a broad range of science goals (see the Appendix of Dawson et al. 2013).

DR9 included about 830,000 BOSS spectra over 3275 deg2 from 1.5 years of observation; DR10 adds an additional 679,000 spectroscopic observations over 3100 deg2 from an additional year of observation that featured unusually good weather at APO. The quality of the data is essentially unchanged from DR9. The spectra cover the wavelength range 3650-10,400 A0, with a resolution of roughly R ~ 1800. The S/N is of course a strong function of magnitude, but at a model magnitude of i = 19:9, the magnitude limit of the CMASS galaxy sample (see Dawson et al. 2013; Ahn et al. 2012), the typical median S/N per pixel across the spectra is about 2. The majority of these spectra are of adequate quality for classification and measurement of a redshift; 6% of the galaxy target spectra and 12% of the quasar target spectra are agged by the spectroscopic pipeline (Bolton et al. 2012) as having uncertain classification. These numbers are significantly higher than they were for SDSS-I/II, as the targets are quite a bit fainter, but they remain small enough for quantitative analysis of the samples (especially with visual inspections of the quasar targets; see Paris et al. 2012).

Figure 13 shows the sky coverage of the BOSS spectroscopic survey in more detail than in Figure 2. The tiling of the individual circular plates is visible in this completeness map of the CMASS galaxy sample. Because of the finite extent of the cladding around fibers, no two fibers can be placed closer than 62', meaning that spectroscopy will be only about 94% complete in regions covered by only a single plate.

Figure 13. BOSS DR10 spectroscopic sky coverage in the Northern Galactic Cap (top) and Southern Galactic Cap (bottom)

The grey region is the coverage goal for the final survey, totaling 10,000 deg2. The color coding indicates the fraction of CMASS galaxy targets that receive a fiber; the fact that no two fibers can be placed closer than 62 on a given plate reduces the average completeness to 94%. Note the higher completeness on the Equator in the Southern Galactic Cap (Stripe 82) where the plates are tiled with more overlapping area to recover collided galaxies.

DR10PaperFigure13.png

Figure 14 shows the distribution of DR10 BOSS spectroscopy as a function of lookback time, or equivalently redshift. The galaxy distribution peaks at a redshift of 0.5 (about 5.5 Gyr ago), with very few galaxies above redshift 0.7. By design, the majority of quasars lie between redshifts 2.15 and 3.5, as this is the range in which the Lyman forest enters the BOSS spectral coverage.

Figure 14. The distribution of BOSS DR10 spectroscopic objects versus lookback time

for the 144,968 unique stars; 859,322 unique galaxies; and 166,300 unique quasars. Lookback time is based on the observed redshift under the assumption of a CDM cosmology (Komatsu et al. 2011). This figure is nearly identical to the equivalent for DR9 (Figure 3 of Ahn et al. (2012)), scaled by a factor of 1.8.

DR10PaperFigure14.png

These distributions are shown in more detail in Figure 15, which compares the redshift distributions of galaxies and quasars to those from the SDSS-I/II Legacy survey. The SDSS-I/II galaxy survey includes a magnitude-limited sample with median redshift z ~ 0:10 (Strauss et al. 2002) and a magnitude- and color-selected sample of luminous red galaxies extending to beyond z = 0:4 (Eisenstein et al. 2001). The SDSS-I/II quasar survey (Richards et al. 2002; Schneider et al. 2010) selects quasars at all redshifts and is flux-limited at magnitudes significantly brighter than BOSS; the bulk of the resulting quasar sample lies below z = 2. The BOSS DR10 galaxy sample is roughly the same size as the full DR7 Legacy galaxy sample (at almost five times the median redshift) and the BOSS DR10 quasar sample is significantly larger than its Legacy counterpart. DR10 includes about 60% of the full BOSS footprint, so DR12, the final SDSS-III data release, will be roughly 50% larger.

Figure 15. N(z) of SDSS-III BOSS spectra in DR10 compared to that of the SDSS-I/II Legacy spectra for galaxies (top) and quasars (bottom).

DR10PaperFigure15.png

In what follows, Section 4.1 describes a new quasar target class for quasars selected using WISE data, Section 4.2 describes minor updates to the BOSS spectroscopic pipeline in DR10, and Section 4.3 discusses additions to measurements of parameters from galaxy spectra.

4.1. A New Quasar Target Class in DR10

Ross et al. (2012) describe the quasar target selection used in BOSS. DR10 includes one new quasar target class, BOSS WISE SUPP, which uses photometry from SDSS and WISE to select z > 2 quasars that the standard BOSS quasar target selection may have missed, and to explore the properties of quasars selected in the infrared.

These objects were required to have detections in the 3.6 um, 4.5 um, and 12 um bands, and to be point sources in SDSS imaging. They were selected with the following color cuts:

(u - g) > 0:4 and (g - r) < 1:3: (3)

The requirement of a 12 um detection removes essentially all stellar contamination, without any WISE color cuts.

There are 5,007 spectra from this sample in DR10, with a density of ~ 1:5/deg2 over the ~ 3; 100 deg2 of new area added by BOSS in DR10. Almost 3000 of these objects are spectroscopically confirmed to be quasars, with redshifts up to z = 3:8. Nine-hundred ninety-nine of these objects have z > 2:15.

Given the use of WISE photometry in target selection, we have imported the WISE All-Sky Release catalog (Cutri et al. 2012) into the SDSS Catalog Archive Server (CAS), and performed an astrometric cross-match with 4' matching radius with the SDSS catalog objects. We find no systematic shift between the WISE and SDSS astrometric systems; 4' extends well into the tail of the match distance distribution. The results of this matching are also available as individual les in the Science Archive Server (SAS).

4.2. Updates to BOSS Data Processing

We have become aware of transient hot columns on the spectrograph CCDs. Because ber traces lie approximately along columns, a bad column can adversely affect a large swath of a given spectrum. With this in mind, unusual-looking spectra associated with bers 40, 556, and 834 and bers immediately adjacent should be treated with suspicion; these objects are often erroneously classied as z > 5 quasars. We will improve the masking of these bad columns in future data releases.

We have identied 2748 objects with spectra whose astrometry is unreliable in the SDSS imaging due to tracking or focus problems of the SDSS telescope while scanning. As a consequence, the bers may be somewhat oset from the true position of the object, often missing it entirely (and thus having a spectrum with no signal). The redshift determination of each object is accompanied by a warning ag, ZWARNING, which indicates that the results are not reliable (Table 2 of Dawson et al. 2013). Objects with bad astrometry are assigned bit 8, BAD TARGET in ZWARNING.

4.3. Updates to BOSS Galaxy Stellar Population Parameters

Estimating stellar population properties for galaxies from SDSS spectra continues to be an active field with different valid approaches. DR9 included various estimates of stellar population parameters, including:

  • "Portsmouth" stellar masses derived from spectroscopic redshifts plus the SDSS imaging ugriz (Maraston et al. 2013);
  • "Portsmouth" measurements of stellar kinematics and emission-line uxes combined with model spectral ts to the full spectra (Thomas et al. 2013), and
  • "Wisconsin" principal component analysis (PCA) of the stellar populations using ts to the wavelength range Lambda = 3700-5500 A0 (Chen et al. 2012).

The latter two spectral ts include estimates of stellar velocity dispersions. These measurements agree with each other and the pipeline estimates of Bolton et al. (2012) within their measurement errors, but slight systematic osets remain. For a detailed comparison we refer the reader to Thomas et al. (2013).

All stellar population calculations use the WMAP7 CDM cosmology with H0 = 70 km/s/Mpc, OmegaM = 0:274, and OmegaA = 0:726 (Komatsu et al. 2011).

In DR9, these models were calculated just for BOSS spectra; in DR10 they are extended to the ~ 930; 000 galaxy spectra from SDSS-I/II. The Portsmouth code results in DR10 now also include the full stellar mass probability distribution function for each spectrum. The Wisconsin PCA code in DR9 used the stellar population model of Bruzual & Charlot (2003). In DR10, we have added the stellar population synthesis model of Maraston & Stromback (2011). In addition, the covariance matrix in the ux density in neighboring pixels due to errors in spectrophotometry has been updated by using all of the repeat galaxy observations in DR10, rather than the 5,000 randomly selected repeat galaxy observations used in DR9. This covariance is important in fitting stellar population models to the spectra.

In DR9 we also provided measurements of emission-line fuxes and equivalent widths as well as gas kinematics (Thomas et al. 2013). However, the continuum fluxes as listed in the Portsmouth DR9 catalog needed to be corrected to rest-frame by multiplication by 1+z. Consequently, the equivalent widths needed to be divided by the same factor 1+z to be translated into the rest frame. In DR10, the continuum fluxes and equivalent widths have these correction factors applied, and are presented in the rest-frame.

In DR10, we also include results from the Granada Stellar Mass code (A. Montero-Dorta et al., 2014, in preparation) based on the publicly available "Flexible Stellar Population Synthesis" code of Conroy et al. (2009). The Granada FSPS product follows a similar spectrophotometric SED tting approach as that of the Portsmouth galaxy product, but using different stellar population synthesis models, with varying star formation history (based on simple tau-models), metallicity and dust attenuation. The Granada FSPS galaxy product provides spectrophotometric stellar masses, ages, specific star formation rates, and other stellar population properties, along with corresponding errors, for eight different models, which are generated by applying simple, physically motivated priors to the parent grid. These eight models are based on three binary choices: (1) including or not including dust; (2) using the Kroupa (2001) vs. the Salpeter (1955) stellar initial mass function; and (3) two different configurations for the galaxy formation time: either the galaxy formed within the rst 2 Gyr following the Big Bang (z ~ 3:25), or the galaxy formed between the time of the Big Bang and two Gyr before the observed redshift of the galaxy.

5. DATA DISTRIBUTION

All Data Release 10 data are available through data access tools linked from the DR10 web site. 104 The data are stored both as individual les in the Science Archive Server (SAS) and as a searchable database in the Catalog Archive Server (CAS). Both of these data servers have front-end web interfaces, called the "SAS Webapp" 105 and "SkyServer" 106, respectively. A number of different interfaces are available, each designed to accomplish a specific task.

  • Color images of regions of the sky in JPEG format (based on the g, r and i images; see Lupton et al. 2004) can be viewed in a web browser with the SkyServer Navigate tool. These are presented at higher resolution, and with greater delity, than in previous releases. With DR10 we also include JPEG images of the 2MASS data to complement the APOGEE spectra.
  • FITS images can be searched for, viewed, and downloaded through the SAS Webapp.
  • Complete catalog information (astrometry, photometry, etc.) of any imaging object can be viewed through the SkyServer Explore tool.
  • Individual spectra, both optical and infrared, can be searched for, viewed, and downloaded through the SAS Webapp.
  • Catalog search tools are available through the Sky- Server interface to the CAS, each of which returns catalog data for objects that match supplied criteria. For more advanced queries, a powerful and exible catalog search website called "CasJobs" allows users to create their own personalized data sets and then to modify or graph their data.

Links to all of these methods are provided at http://www.sdss3.org/dr10/dataaccess/.

The DR10 web site also features data access tutorials, a glossary of SDSS terms, and detailed documentation about algorithms used to process the imaging and spectroscopic data and select spectroscopic targets.

Imaging and spectroscopic data from all prior data releases are also available through DR10 data access tools, with the sole caveat that the 303 imaging runs covering the equatorial stripe in the Fall sky ("Stripe 82") are only fully provided in DR7 107 - only the good quality images are included from Stripe 82 in DR8 and subsequent releases.

6. FUTURE

The SDSS-III project will present two more public data releases: DR11 and DR12, both to be released in December 2014. DR11 will include data taken through the summer of 2013. DR12 will be the nal SDSS-III data release and will include the nal data through Summer 2014 from all observations with APOGEE, BOSS, MARVELS, and SEGUE-2.

In 2014 July, operation of the 2.5-m Sloan Foundation Telescope will be taken over by the next generation of SDSS, currently known as SDSS-IV, which plans to operate for six years. SDSS-IV consists of three surveys mapping the Milky Way Galaxy, the nearby galaxy population, and the distant universe. APOGEE-2 will continue the current APOGEE program of targeting Milky Way stars to study Galactic archaeology and stellar astrophysics. It will include a southern component, observing from the 2.5-m du Pont Telescope at Las Campanas Observatory, Chile, allowing a full-sky view of the structure of the Milky Way. Mapping Nearby Galaxies at APO (MaNGA) will use the BOSS spectrograph in a new mode, bundling bers into integral eld units to observe 10,000 nearby galaxies with spatially resolved spectroscopy. MaNGA has already observed a small number of targets using BOSS time to test its planned hardware conguration. Finally, the Extended Baryon Oscillation Spectroscopic Survey (eBOSS) will create the largest volume three-dimensional map of the universe to date, to measure baryon acoustic oscillations and constrain cosmological parameters in the critical and largely unexplored redshift range 0:6 < z < 2:1. eBOSS will also obtain spectra of X-ray sources detected by the eROSITA satellite (Predehl et al. 2010), as well as of variable stars and quasars to understand their physical nature. The SDSS-IV collaboration will continue the production and distribution of cutting-edge and diverse data sets through the end of the decade.

SDSS-III Data Release 10 makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation.

SDSS-III Data Release 10 makes use of data products from the Wide-eld Infrared Survey Explorer, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration.

Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the U.S. Department of Energy Oce of Science. The SDSS-III web site is http://www.sdss3.org/.

SDSS-III is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS-III Collaboration including the University of Arizona, the Brazilian Participation Group, Brookhaven National Laboratory, Carnegie Mellon University, University of Florida, the French Participation Group, the German Participation Group, Harvard University, the Instituto de Astrosica de Canarias, the Michigan State/Notre Dame/JINA Participation Group, Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington, and Yale University.

Footnotes

1

Department of Physics and Astronomy, University of Utah, Salt Lake City, UT 84112, USA.

2

Center for Astrophysical Sciences, Department of Physics and Astronomy, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA.

3

Instituto de Astrofsica de Canarias (IAC), C/Va Lactea, s/n, E-38200, La Laguna, Tenerife, Spain.

4

Departamento de Astrofsica, Universidad de La Laguna, E-38206, La Laguna, Tenerife, Spain.

5

Leibniz-Institut fur Astrophysik Potsdam (AIP), An der Sternwarte 16, D-14482 Potsdam, Germany.

6

Technische Universitat Dresden (TUD), Institut fur Kernund Teilchenphysik, D-01062 Dresden, Germany.

7

Department of Astronomy, University of Washington, Box 351580, Seattle, WA 98195, USA.

8

Department of Astronomy, Ohio State University, 140 West 18th Avenue, Columbus, OH 43210, USA.

9

APC, University of Paris Diderot, CNRS/IN2P3, CEA/IRFU, Observatoire de Paris, Sorbonne Paris Cite, F-75205 Paris, France.

10

Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA.

11

Department of Physics and Astronomy, Vanderbilt University, VU Station 1807, Nashville, TN 37235, USA.

12

National Optical Astronomy Observatory, 950 North Cherry Avenue, Tucson, AZ, 85719, USA.

13

Department of Physics and Astronomy and JINA: Joint Institute for Nuclear Astrophysics, Michigan State University, East Lansing, MI 48824, USA.

14

Max-Planck-Institut fur Extraterrestrische Physik, Giessenbachstrae, D-85748 Garching, Germany.

15

Department of Astronomy and Astrophysics, 525 Davey Laboratory, The Pennsylvania State University, University Park, PA 16802, USA.

16

Center for Exoplanets and Habitable Worlds, 525 Davey Laboratory, Pennsylvania State University, University Park, PA 16802, USA.

17

Apache Point Observatory, P.O. Box 59, Sunspot, NM 88349, USA.

18

Department of Astronomy, MSC 4500, New Mexico State University, P.O. Box 30001, Las Cruces, NM 88003, USA.

19

University of Pennsylvania, Department of Physics and Astronomy, 219 S. 33rd St., Philadelphia, PA 19104.

20

Center for Cosmology and Particle Physics, Department of Physics, New York University, 4 Washington Place, New York, NY 10003, USA.

21

Department of Physics and Astronomy, University of California, Irvine, CA 92697, USA.

22

Haverford College, Department of Physics and Astronomy, 370 Lancaster Avenue, Haverford, PA, 19041, USA.

23

CEA, Centre de Saclay, Irfu/SPP, F-91191 Gif-sur-Yvette, France.

24

Institute for Advanced Study, Einstein Drive, Princeton, NJ 08540, USA.

25

Hubble fellow.

26

Institute for Gravitation and the Cosmos, The Pennsylvania State University, University Park, PA 16802, USA.

27

Department of Terrestrial Magnetism, Carnegie Institution of Washington, 5241 Broad Branch Road, NW, Washington DC 20015, USA.

28

Observatorio Nacional, Rua Gal. Jose Cristino 77, Rio de Janeiro, RJ - 20921-400, Brazil.

29

Laboratorio Interinstitucional de e-Astronomia, - LIneA, Rua Gal.Jose Cristino 77, Rio de Janeiro, RJ - 20921-400, Brazil.

30

Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA.

31

Department of Astronomy, University of Virginia, P.O.Box 400325, Charlottesville, VA 22904-4325, USA.

32

Instituto de Fsica Teorica, (UAM/CSIC), Universidad Autonoma de Madrid, Cantoblanco, E-28049 Madrid, Spain.

33

Laboratoire d'Astrophysique de Marseille, CNRSUniversit e de Provence, 38 rue F. Joliot-Curie, F-13388 Marseille cedex 13, France.

34

Department of Physics, 225 Nieuwland Science Hall, Notre Dame, IN, 46556, USA.

35

INAF, Osservatorio Astronomico di Trieste, Via G. B. Tiepolo 11, I-34131 Trieste, Italy.

36

INFN/National Institute for Nuclear Physics, Via Valerio 2, I-34127 Trieste, Italy.

37

Bruce and Astrid McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA.

38

Yale Center for Astronomy and Astrophysics, Yale University, New Haven, CT, 06520, USA.

39

Steward Observatory, 933 North Cherry Avenue, Tucson, AZ 85721, USA.

40

Department of Physical Sciences, Embry-Riddle Aeronautical University, 600 South Clyde Morris Blvd., Daytona Beach, FL 32114, USA.

41

Centre de Physique des Particules de Marseille, Aix- Marseille Universite, CNRS/IN2P3, E-13288 Marseille, France.

42

Institute of Cosmology and Gravitation, Dennis Sciama Building, University of Portsmouth, Portsmouth, PO1 3FX, UK.

43

Harvard-Smithsonian Center for Astrophysics, Harvard University, 60 Garden Street, Cambridge MA 02138, USA.

44

Faculty of Sciences, Department of Astronomy and Space Sciences, Erciyes University, 38039 Kayseri, Turkey.

45

UPMC-CNRS, UMR7095, Institut d'Astrophysique de Paris, 98bis Boulevard Arago, F-75014, Paris, France.

46

Institute of Theoretical Physics, University of Zurich, 8057 Zurich, Switzerland.

47

Department of Physics and Astronomy, Texas Christian University, 2800 South University Drive, Fort Worth, TX 76129, USA.

48

Department of Astronomy, University of Florida, Bryant Space Science Center, Gainesville, FL 32611-2055, USA.

49

INAF, Osservatorio Astronomico di Padova, Vicolo dell'Osservatorio 5, I-35122 Padova, Italy.

50

Department of Astronomy, Case Western Reserve University, Cleveland, OH 44106, USA.

51

Department of Physics, Ohio State University, Columbus, OH 43210, USA.

52

Center for Cosmology and Astro-Particle Physics, Ohio State University, Columbus, OH 43210, USA.

53

Department of Physics, University of Texas-Dallas, Dallas, TX 75080, USA.

54

Key Laboratory for Research in Galaxies and Cosmology, University of Science and Technology of China, Chinese Academy of Sciences, Hefei, Anhui, 230026, China.

55

Laboratoire d'Astrophysique, Ecole Polytechnique Federale de Lausanne (EPFL), Observatoire de Sauverny, 1290, Versoix, Switzerland.

56

Texas Advanced Computer Center, University of Texas, 10100 Burnet Road (R8700), Austin, Texas 78758-4497, USA.

57

Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU, WPI), Todai Institutes for Advanced Study, The University of Tokyo, Kashiwa, 277-8583, Japan.

58

Max-Planck-Institut fur Astronomie, Konigstuhl 17, D-69117 Heidelberg, Germany.

59

Australian Astronomical Observatory, PO Box 915, North Ryde NSW 1670, Australia.

60

Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK.

61

Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK.

62

Alfred P. Sloan fellow.

63

Institucio Catalana de Recerca i Estudis Avancats, Barcelona E-08010, Spain.

64

Institut de Ciencies del Cosmos, Universitat de Barcelona/IEEC, Barcelona E-08028, Spain.

65

US Naval Observatory, Flagsta Station, 10391 West Naval Observatory Road, Flagsta, AZ 86001-8521, USA.

66

Department of Physics and Astronomy, University of Wyoming, Laramie, WY 82071, USA.

67

Dunlap Institute for Astronomy and Astrophysics, University of Toronto, Toronto, ON, M5S 3H4, Canada.

68

Dept. of Astronomy, University of Michigan, Ann Arbor, MI, 48104, USA.

69

Departamento de Astronoma, Universidad de Chile, Casilla 36-D, Santiago, Chile.

70

Department of Physics, Lehigh University, 16 Memorial Drive East, Bethlehem, PA 18015, USA.

71

Departament d'Astronomia i Meteorologia, Facultat de Fsica, Universitat de Barcelona, E-08028 Barcelona, Spain.

72

Federal do Rio de Janeiro, Observatorio do Valongo, Ladeira do Pedro Ant^onio 43, 20080-090 Rio de Janeiro, Brazil.

73

Campus of International Excellence UAM+CSIC, Cantoblanco, E-28049 Madrid, Spain.

74

Instituto de Astrofsica de Andaluca (CSIC), Glorieta de la Astronoma, E-18080 Granada, Spain.

75

Department of Astronomy, Columbia University, New York, NY 10027, USA.

76

Consejo Superior Investigaciones Cientcas, 28006 Madrid, Spain.

77

Instituto de Fsica, UFRGS, Caixa Postal 15051, Porto Alegre, RS - 91501-970, Brazil.

78

Universite de Franche-Comte, Institut Utinam, UMR CNRS 6213, OSU Theta, Besancon, F-25010, France.

79

UCO/Lick Observatory, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA.

80

School of Physics, Korea Institute for Advanced Study, 85 Hoegiro, Dongdaemun-gu, Seoul 130-722, Republic of Korea.

81

Astrophysics Research Institute, Liverpool John Moores University, IC2, Liverpool Science Park 146 Brownlow Hill Liverpool L3 5RF United Kingdom.

82

Research School of Astronomy and Astrophysics, Australian National University, Weston Creek, ACT, 2611, Australia.

83

Observatories of the Carnegie Institution of Washington, 813 Santa Barbara Street, Pasadena, CA 91101, USA.

84

University of Texas, Hobby-Eberly Telescope, 32 Fowlkes Rd, McDonald Observatory, TX 79734-3005, USA.

85

Brookhaven National Laboratory, Bldg 510, Upton, NY 11973, USA.

86

Department of Astronomy and Astrophysics and JINA, University of Chicago, Chicago, IL 60637, USA.

87

Department of Physics, Fisk University, 1000 17th Avenue North, Nashville, TN 37208, USA.

88

Corresponding authors.

89

Department of Astronomy, University of Wisconsin- Madison, 475 North Charter Street, Madison WI 53703, USA.

90

Department of Physics, University of California, Berkeley, CA 94720, USA.

91

Max-Planck Institute for Astrophysics, Karl- SchwarzschildStr 1, D-85748 Garching, Germany.

92

H.L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman, OK 73019, USA.

93

PITT PACC, Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15260, USA.

94

Department of Astronomy and Astrophysics and the Enrico Fermi Institute, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637, USA.

95

National Astronomy Observatories, Chinese Academy of Science, Beijing, 100012, China.

98

This is a renement from the less stringent goal of S/N> 100 per full-resolution element given in Eisenstein et al. (2011).

101

The relative -element abundance is labeled ALPHAFE in the DR10 tables and les, but it is more accurately the ratio of the elements to the overall metallicity, [/M].

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