The third generation of the Sloan Digital Sky Survey SDSSIII took data from 2008 to 2014 using the original SDSS widefield imager the original and an upgraded multiobject fiberfed optical spectrograph a new nearinfrared highresolution spectrograph and a novel optical interferometer. All of the data from SDSSIII are now made public. In particular this paper describes Data Release 11 DR11 including all data acquired through 2013 July and Data Release 12 DR12 adding data acquired through 2014 July including all data included in previous data releases marking the end of SDSSIII observing. Relative to our previous public release DR10 DR12 adds one million new spectra of galaxies and quasars from the Baryon Oscillation Spectroscopic Survey BOSS over an additional 3000 degSUP2SUP of sky more than triples the number of Hband spectra of stars as part of the Apache Point Observatory APO Galactic Evolution Experiment APOGEE and includes repeated accurate radial velocity measurements of 5500 stars from the Multiobject APO Radial Velocity Exoplanet Largearea Survey MARVELS. The APOGEE outputs now include the measured abundances of 15 different elements for each star. In total SDSSIII added 5200 degSUP2SUP of ugriz imaging 155520 spectra of 138099 stars as part of the Sloan Exploration of Galactic Understanding and Evolution 2 SEGUE2 survey 2497484 BOSS spectra of 1372737 galaxies 294512 quasars and 247216 stars over 9376 degSUP2SUP 618080 APOGEE spectra of 156593 stars and 197040 MARVELS spectra of 5513 stars. Since its first light in 1998 SDSS has imaged over 13 of the Celestial sphere in five bands and obtained over five million astronomical spectra.
['atlases', 'catalogs', 'surveys', 'Astrophysics - Instrumentation and Methods for Astrophysics', 'Astrophysics - Astrophysics of Galaxies', 'Astrophysics - Solar and Stellar Astrophysics']
The Eleventh and Twelfth Data Releases of the Sloan Digital Sky Survey Final Data from SDSSIII
{'THE ELEVENTH AND TWELFTH DATA RELEASES OF THE SLOAN DIGITAL SKY SURVEY: FINAL DATA FROM SDSS-III': "Shadab Alam 1 , Franco D. Albareti 2 , Carlos Allende Prieto 3,4 , F. Anders 5 , Scott F. Anderson 6 , Timothy Anderton 7 , Brett H. Andrews 8,9 , Eric Armengaud 10 , ' Eric Aubourg 11 , Stephen Bailey 12 , Sarbani Basu 13 , Julian E. Bautista 11 , Rachael L. Beaton 14,15 , Timothy C. Beers 16 , Chad F. Bender 17,18 Andreas A. Berlind 19 , Florian Beutler 12 , Vaishali Bhardwaj 6,12 , Jonathan C. Bird 19 , Dmitry Bizyaev 20,21,22 , Cullen H. Blake 23 , Michael R. Blanton 24 , Michael Blomqvist 25 , John J. Bochanski 6,26 , Adam S. Bolton 7 , Jo Bovy 27,28 , A. Shelden Bradley 20 , W. N. Brandt 17,29,30 , D. E. Brauer 5 , J. Brinkmann 20 , Peter J. Brown 31 , Joel R. Brownstein 7 , Angela Burden 32 , Etienne Burtin 10 , Nicol'as G. Busca 33,34,11 , Zheng Cai 35 , Diego Capozzi 32 , Aurelio Carnero Rosell 33,34 , Michael A. Carr 36 , Ricardo Carrera 3,4 , K. C. Chambers 37 , William James Chaplin 38,39 , Yen-Chi Chen 40 , Cristina Chiappini 5,34 , S. Drew Chojnowski 21 , Chia-Hsun Chuang 2 , Nicolas Clerc 41 , Johan Comparat 2 , Kevin Covey 42,43 , Rupert A.C. Croft 1 , Antonio J. Cuesta 44,45 , Katia Cunha 33,35 , Luiz N. da Costa 33,34 , Nicola Da Rio 46 , James R. A. Davenport 6 , Kyle S. Dawson 7 , Nathan De Lee 47 , Timoth'ee Delubac 48 , Rohit Deshpande 17,18 , Saurav Dhital 49 , Let'ıcia Dutra-Ferreira 50,34,51 , Tom Dwelly 41 , Anne Ealet 52 , Garrett L. Ebelke 14 , Edward M. Edmondson 32 , Daniel J. Eisenstein 53 , Tristan Ellsworth 7 , Yvonne Elsworth 38,39 , Courtney R. Epstein 8 , Michael Eracleous 17,29,54,6 , Stephanie Escoffier 52 , Massimiliano Esposito 3,4 , Michael L. Evans 6 , Xiaohui Fan 35 , Emma Fern'andez-Alvar 3,4 , Diane Feuillet 21 , Nurten Filiz Ak 17,29,55 , Hayley Finley 56 , Alexis Finoguenov 57 , Kevin Flaherty 58 , Scott W. Fleming 59,60 , Andreu Font-Ribera 12 , Jonathan Foster 45 , Peter M. Frinchaboy 61 , J. G. Galbraith-Frew 7 , Rafael A. Garc'ıa 62 , D. A. Garc'ıa-Hern'andez 3,4 , Ana E. Garc'ıa P'erez 14,3,4 , Patrick Gaulme 20 , Jian Ge 46 , R. G'enova-Santos 3,4 , A. Georgakakis 41 , Luan Ghezzi 33,53 , Bruce A. Gillespie 63 , L'eo Girardi 64,34 , Daniel Goddard 32 , Satya Gontcho A Gontcho 44 , Jonay I. Gonz'alez Hern'andez 3,4 , Eva K. Grebel 65 , Paul J. Green 53 , Jan Niklas Grieb 41 , Nolan Grieves 46 , James E. Gunn 36 , Hong Guo 7 , Paul Harding 66 , Sten Hasselquist 21 , Suzanne L. Hawley 6 , Michael Hayden 21 , Fred R. Hearty 17 , Saskia Hekker 67,39 , Shirley Ho 1 , David W. Hogg 24 , Kelly Holley-Bockelmann 19 , Jon A. Holtzman 21 , Klaus Honscheid 68,69 , Daniel Huber 70,71,39 , Joseph Huehnerhoff 20 , Inese I. Ivans 7 , Linhua Jiang 72 , Jennifer A. Johnson 8,69 , Karen Kinemuchi 20,21 , David Kirkby 25 , Francisco Kitaura 5 , Mark A. Klaene 20 , Gillian R. Knapp 36 , Jean-Paul Kneib 48,73 , Xavier P. Koenig 13 , Charles R. Lam 14 Ting-Wen Lan 63 , Dustin Lang 1 , Pierre Laurent 10 , Jean-Marc Le Goff 10 , Alexie Leauthaud 74 , Khee-Gan Lee 75 , Young Sun Lee 76 , Timothy C. Licquia 9 , Jian Liu 46 , Daniel C. Long 20,21 , Mart'ın L'opez-Corredoira 3,4 , Diego Lorenzo-Oliveira 50,34 , Sara Lucatello 64 , Britt Lundgren 77 , Robert H. Lupton 36 , Claude E. Mack III 19,5 , Suvrath Mahadevan 17,18 , Marcio A. G. Maia 33,34 , Steven R. Majewski 14 , Elena Malanushenko 20,21 , Viktor Malanushenko 20,21 , A. Manchado 3,4 , Marc Manera 32,78 , Qingqing Mao 19 , Claudia Maraston 32 , Robert C. Marchwinski 17,18 , Daniel Margala 25 , Sarah L. Martell 79 , Marie Martig 75 , Karen L. Masters 32 , Savita Mathur 80 , Cameron K. McBride 53 , Peregrine M. McGehee 81 , Ian D. McGreer 35 , Richard G. McMahon 82,83 , Brice M'enard 63,74,84 , Marie-Luise Menzel 41 , Andrea Merloni 41 , Szabolcs M'esz'aros 85 , Adam A. Miller 86,87,88 , Jordi Miralda-Escud'e 89,44 Hironao Miyatake 36,74 , Antonio D. Montero-Dorta 7 , Surhud More 74 , Eric Morganson 53 , Xan Morice-Atkinson 32 , Heather L. Morrison 66 , Benoˆıt Mosser 90 , Demitri Muna 8 , Adam D. Myers 91 , Kirpal Nandra 41 , Jeffrey A. Newman 9 , Mark Neyrinck 63 , Duy Cuong Nguyen 92 , Robert C. Nichol 32 , David L. Nidever 93 , Pasquier Noterdaeme 56 , Sebasti'an E. Nuza 5 , Julia E. O'Connell 61 , Robert W. O'Connell 14 , Ross O'Connell 1 , Ricardo L. C. Ogando 33,34 , Matthew D. Olmstead 7,94 , Audrey E. Oravetz 20,21 , Daniel J. Oravetz 20 , Keisuke Osumi 1 , Russell Owen 6 , Deborah L. Padgett 95 , Nikhil Padmanabhan 45 , Martin Paegert 19 , Nathalie Palanque-Delabrouille 10 , Kaike Pan 20 , John K. Parejko 96 , Isabelle Pˆaris 97 , Changbom Park 98 , Petchara Pattarakijwanich 36 , M. Pellejero-Ibanez 3,4 , Joshua Pepper 99,19 , Will J. Percival 32 , Ismael P'erez-Fournon 3,4 , Ignasi P'erez-R'afols 44,100 , Patrick Petitjean 56 , Matthew M. Pieri 101,32 , Marc H. Pinsonneault 8 , Gustavo F. Porto de Mello 50,34 , Francisco Prada 2,102,103 , Abhishek Prakash 9 , Adrian M. Price-Whelan 104 , Pavlos Protopapas 105 , M. Jordan Raddick 63 , Mubdi Rahman 63 , Beth A. Reid 106,12 , James Rich 10 , Hans-Walter Rix 75 , Annie C. Robin 107 , Constance M. Rockosi 108 , Tha'ıse S. Rodrigues 64,109,34 , Sergio Rodr'ıguez-Torres 2,102 , Natalie A. Roe 12 , Ashley J. Ross 32,69 , Nicholas P. Ross 110 , Graziano Rossi 111,10 , John J. Ruan 6 , J. A. Rubi˜no-Mart'ın 3,4 , Eli S. Rykoff 112 , Salvador Salazar-Albornoz 113,41 , Mara Salvato 41,114 , Lado Samushia 115,116 , Ariel G. S'anchez 41 , Bas'ılio Santiago 117,34 , Conor Sayres 6 , Ricardo P. Schiavon 118,119 , David J. Schlegel 12 , Sarah J. Schmidt 8 , Donald P. Schneider 17,29 , Mathias Schultheis 120 , Axel D. Schwope 5 , C. G. Sc'occola 3,4 , Caroline Scott 53 , Kris Sellgren 8 , Hee-Jong Seo 121 , Aldo Serenelli 122 , Neville Shane 14 , Yue Shen 15,72 , Matthew Shetrone 123 , Yiping Shu 7 , V. Silva Aguirre 39 , Thirupathi Sivarani 124 , M. F. Skrutskie 14 , Anˇze Slosar 125 , Verne V. Smith 126 , Fl'avia Sobreira 34,127 , Diogo Souto 33 , Keivan G. Stassun 19,128 , Matthias Steinmetz 5 , Dennis Stello 39,70 , Michael A. Strauss 36,129 , Alina Streblyanska 3,4 , Nao Suzuki 74 , Molly E. C. Swanson 53 , Jonathan C. Tan 46 , Jamie Tayar 8 , Ryan C. Terrien 17,18,130 , Aniruddha R. Thakar 63 , Daniel Thomas 32,131 , Neil Thomas 46 , Benjamin A. Thompson 61 , Jeremy L. Tinker 24 , Rita Tojeiro 132 , Nicholas W. Troup 14 , Mariana Vargas-Maga˜na 1 , Jose A. Vazquez 125 , Licia Verde 89,44,133 , Matteo Viel 97,134 , Nicole P. Vogt 21 , David A. Wake 77,135 , Ji Wang 13 , Benjamin A. Weaver 24 , David H. Weinberg 8 , Benjamin J. Weiner 35 , Martin White 12,106 , John C. Wilson 14 , John P. Wisniewski 136 , W. M. Wood-Vasey 9,129 , Christophe Y'eche 10 , Donald G. York 137 , Nadia L. Zakamska 63 , O. Zamora 3,4 , Gail Zasowski 63 , Idit Zehavi 66 , Gong-Bo Zhao 138,32 , Zheng Zheng 7 , Xu Zhou ( 周 旭 ) 139 ,  \nDraft version August 7, 2016", 'ABSTRACT': "The third generation of the Sloan Digital Sky Survey (SDSS-III) took data from 2008 to 2014 using the original SDSS wide-field imager, the original and an upgraded multi-object fiber-fed optical spectrograph, a new near-infrared high-resolution spectrograph, and a novel optical interferometer. All the data from SDSS-III are now made public. In particular, this paper describes Data Release 11 (DR11) including all data acquired through 2013 July, and Data Release 12 (DR12) adding data acquired through 2014 July (including all data included in previous data releases), marking the end of SDSS-III observing. Relative to our previous public release (DR10), DR12 adds one million new spectra of galaxies and quasars from the Baryon Oscillation Spectroscopic Survey (BOSS) over an additional 3000 deg 2 of sky, more than triples the number of H -band spectra of stars as part of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE), and includes repeated accurate radial velocity measurements of 5500 stars from the Multi-Object APO Radial Velocity Exoplanet Large-area Survey (MARVELS). The APOGEE outputs now include measured abundances of 15 different elements for each star. In total, SDSS-III added 2350 deg 2 of ugriz imaging; 155,520 spectra of 138,099 stars as part of the Sloan Exploration of Galactic Understanding and Evolution 2 (SEGUE-2) survey; 2,497,484 BOSS spectra of 1,372,737 galaxies, 294,512 quasars, and 247,216 stars over 9376 deg 2 ; 618,080 APOGEE spectra of 156,593 stars; and 197,040 MARVELS spectra of 5,513 stars. Since its first light in 1998, SDSS has imaged over 1/3 of the Celestial sphere in five bands and obtained over five million astronomical spectra.  \nKeywords: Atlases-Catalogs-Surveys  \n- 1 McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA\n- 2 Instituto de F'ısica Te'orica, (UAM/CSIC), Universidad Aut'onoma de Madrid, Cantoblanco, E-28049 Madrid, Spain\n- 3 Instituto de Astrof'ısica de Canarias (IAC), C/V'ıa L'actea, s/n, E-38200, La Laguna, Tenerife, Spain\n- 4 Departamento de Astrof'ısica, Universidad de La Laguna, E-38206, La Laguna, Tenerife, Spain\n- 5 Leibniz-Institut fur Astrophysik Potsdam (AIP), An der Sternwarte 16, D-14482 Potsdam, Germany\n- 6 Department of Astronomy, University of Washington, Box 351580, Seattle, WA 98195, USA\n- 7 Department of Physics and Astronomy, University of Utah, Salt Lake City, UT 84112, USA\n- 8 Department of Astronomy, Ohio State University, 140 West 18th Avenue, Columbus, OH 43210, USA\n- 9 PITT PACC, Department of Physics and Astronomy, University of Pittsburgh, 3941 O'Hara Street, Pittsburgh, PA 15260, USA\n- 10 CEA, Centre de Saclay, Irfu/SPP, F-91191 Gif-sur-Yvette, France\n- 11 APC, University of Paris Diderot, CNRS/IN2P3, CEA/IRFU, Observatoire de Paris, Sorbonne Paris Cit'e, F-75205 Paris, France\n- 12 Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA\n- 13 Department of Astronomy, Yale University, P.O. Box 208101, New Haven, CT 06520-8101, USA\n- 14 Department of Astronomy, University of Virginia, P.O. Box 400325, Charlottesville, VA 22904-4325, USA\n- 15 Observatories of the Carnegie Institution of Washington, 813 Santa Barbara Street, Pasadena, CA 91101, USA\n- 16 Department of Physics and JINA Center for the Evolution of the Elements, University of Notre Dame, Notre Dame, IN 46556 USA\n- 17 Department of Astronomy and Astrophysics, 525 Davey Laboratory, The Pennsylvania State University, University Park, PA 16802, USA\n- 18 Center for Exoplanets and Habitable Worlds, 525 Davey Laboratory, Pennsylvania State University, University Park, PA 16802, USA\n- 19 Department of Physics and Astronomy, Vanderbilt University, VU Station 1807, Nashville, TN 37235, USA\n- 20 Apache Point Observatory, P.O. Box 59, Sunspot, NM 88349, USA\n- 21 Department of Astronomy, MSC 4500, New Mexico State\n- University, P.O. Box 30001, Las Cruces, NM 88003, USA\n- 22 Sternberg Astronomical Institute, Moscow State University, Universitetskij Prosp. 13, Moscow 119992, Russia\n- 23 University of Pennsylvania, Department of Physics and Astronomy, 219 S. 33rd St., Philadelphia, PA 19104, USA\n- 24 Center for Cosmology and Particle Physics, Department of Physics, New York University, 4 Washington Place, New York, NY 10003, USA\n- 25 Department of Physics and Astronomy, University of California, Irvine, CA 92697, USA\n- 26 Rider University, 2083 Lawrenceville Road, Lawrenceville, NJ 08648, USA\n- 27 Institute for Advanced Study, Einstein Drive, Princeton, NJ 08540, USA\n- 28 John Bahcall fellow.\n- 29 Institute for Gravitation and the Cosmos, The Pennsylvania State University, University Park, PA 16802, USA\n- 30 Department of Physics, The Pennsylvania State University, University Park, PA 16802, USA\n- 31 George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy, Texas A. and M. University, Department of Physics and Astronomy, 4242 TAMU, College Station, TX 77843, USA\n- 32 Institute of Cosmology and Gravitation, Dennis Sciama Building, University of Portsmouth, Portsmouth, PO1 3FX, UK 33 Observat'orio Nacional, Rua Gal. Jos'e Cristino 77, Rio de Janeiro, RJ - 20921-400, Brazil\n- 34\n- Laborat'orio Interinstitucional de e-Astronomia, - LIneA, Rua Gal.Jos'e Cristino 77, Rio de Janeiro, RJ - 20921-400, Brazil 35 Steward Observatory, 933 North Cherry Avenue, Tucson, AZ 85721, USA\n- 36 Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA\n- 37 Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USA\n- 38 School of Physics and Astronomy, University of Birmingham, Birmingham B15 2TT, UK\n- 39 Stellar Astrophysics Centre (SAC), Department of Physics and Astronomy, Aarhus University, Ny Munkegade 120, DK8000 Aarhus C, Denmark\n- 40 Department of Statistics, Bruce and Astrid McWilliams Center for Cosmology, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA\n- 41 Max-Planck-Institut fur Extraterrestrische Physik, Postfach 1312, Giessenbachstr. D-85741 Garching, Germany\n- 42 Lowell Observatory, 1400 W. Mars Hill Road, Flagstaff AZ 86001, USA  \n- 43 Western Washington University, Department of Physics & Astronomy, 516 High Street, Bellingham WA 98225\n- 44 Institut de Ci'encies del Cosmos, Universitat de Barcelona/IEEC, Barcelona E-08028, Spain\n- 45 Yale Center for Astronomy and Astrophysics, Yale University, New Haven, CT, 06520, USA\n- 46 Department of Astronomy, University of Florida, Bryant Space Science Center, Gainesville, FL 32611-2055, USA\n- 47 Department of Physics and Geology, Northern Kentucky University, Highland Heights, KY 41099, USA\n- 48 Laboratoire d'Astrophysique, ' Ecole Polytechnique F'ed'erale de Lausanne (EPFL), Observatoire de Sauverny, 1290, Versoix, Switzerland.\n- 49 Department of Physical Sciences, Embry-Riddle Aeronautical University, 600 South Clyde Morris Blvd., Daytona Beach, FL 32114, USA\n- 50 Universidade Federal do Rio de Janeiro, Observat'orio do Valongo, Ladeira do Pedro Antˆonio 43, 20080-090 Rio de Janeiro, Brazil\n- 51 Departamento de F'ısica, Universidade Federal do Rio Grande do Norte, 59072-970, Natal, RN, Brazil.\n- 52 Centre de Physique des Particules de Marseille, AixMarseille Universit'e, CNRS/IN2P3, F-13288 Marseille, France\n- 53 Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge MA 02138, USA\n- 54 Center for Relativistic Astrophysics, Georgia Institute of Technology, Atlanta, GA 30332, USA\n- 55 Faculty of Sciences, Department of Astronomy and Space Sciences, Erciyes University, 38039 Kayseri, Turkey.\n- 56 Institut d'Astrophysique de Paris, UPMC-CNRS, UMR7095, 98 bis Boulevard Arago, F-75014, Paris, France\n- 57 Department of Physics, University of Helsinki, Gustaf Hallstromin katu 2, Helsinki FI-00140, Finland\n- 58 Department of Astronomy, Van Vleck Observatory, Wesleyan University, Middletown, CT 06459, USA\n- 59 Space Telescope Science Institute, 3700 San Martin Dr, Baltimore, MD 21218, USA\n- 60 Computer Sciences Corporation, 3700 San Martin Dr, Baltimore, MD 21218, USA\n- 61 Department of Physics and Astronomy, Texas Christian University, 2800 South University Drive, Fort Worth, TX 76129, USA\n- 62 Laboratoire AIM, CEA/DSM - CNRS - Univ. Paris Diderot - IRFU/SAp, Centre de Saclay, F-91191 Gif-sur-Yvette Cedex, France\n- 63 Center for Astrophysical Sciences, Department of Physics and Astronomy, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA\n- 64 INAF, Osservatorio Astronomico di Padova, Vicolo dell'Osservatorio 5, I-35122 Padova, Italy.\n- 65 Astronomisches Rechen-Institut, Zentrum fur Astronomie der Universitat Heidelberg, Monchhofstr. 12-14, D-69120 Heidelberg, Germany\n- 66 Department of Astronomy, Case Western Reserve University, Cleveland, OH 44106, USA\n- 67 Max-Planck-Institut fur Sonnensystemforschung, Justusvon-Liebig-Weg 3, D-37077 Gottingen, Germany\n- 68 Department of Physics, Ohio State University, Columbus, OH 43210, USA\n- 69 Center for Cosmology and Astro-Particle Physics, Ohio State University, Columbus, OH 43210, USA\n- 70 Sydney Institute for Astronomy (SIfA), School of Physics, University of Sydney, Sydney, NSW 2006, Australia\n- 71 SETI Institute, 189 Bernardo Avenue, Mountain View, CA 94043, USA\n- 72 Kavli Institute for Astronomy and Astrophysics, Peking University, Beijing 100871, China\n- 73 Laboratoire d'Astrophysique de Marseille, CNRS-  \n- Universit'e de Provence, 38 rue F. Joliot-Curie, F-13388 Marseille cedex 13, France\n- 74 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.\n- 75 Max-Planck-Institut fur Astronomie, Konigstuhl 17, D-69117 Heidelberg, Germany\n- 76 Department of Astronomy and Space Science Chungnam National University Daejeon 305-764, Repulic of Korea.\n- 77 Department of Astronomy, University of WisconsinMadison, 475 North Charter Street, Madison WI 53703, USA\n- 78 University College London, Gower Street, London, WC1E 6BT, UK\n- 79 School of Physics, University of New South Wales, Sydney, NSW 2052, Australia\n- 80 Space Science Institute, 4750 Walnut street, Suite 205, Boulder, CO 80301, USA\n- 81 IPAC, MS 220-6, California Institute of Technology, Pasadena, CA 91125, USA\n- 82 Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK.\n- 83 Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK.\n- 84 Alfred P. Sloan fellow.\n- 85 ELTE Gothard Astrophysical Observatory, H-9704 Szombathely, Szent Imre herceg st. 112, Hungary\n- 86 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA\n- 87 Department of Astronomy, California Institute of Technology, Pasadena, CA 91125, USA 88  \nHubble fellow.  \n- 89 Instituci'o Catalana de Recerca i Estudis Avan¸cats, Barcelona E-08010, Spain\n- 90 LESIA, UMR 8109, Universit'e Pierre et Marie Curie, Universit'e Denis Diderot, Observatoire de Paris, F-92195 Meudon Cedex, France\n- 91 Department of Physics and Astronomy, University of Wyoming, Laramie, WY 82071, USA\n- 92 Dunlap Institute for Astronomy and Astrophysics, University of Toronto, Toronto, ON, M5S 3H4, Canada.\n- 93 Dept. of Astronomy, University of Michigan, Ann Arbor, MI, 48104, USA\n- 94 Department of Chemistry and Physics, King's College, Wilkes-Barre, PA 18711, USA\n- 95 NASA/GSFC, Code 665, Greenbelt, MC 20770, USA\n- 96 Department of Physics, Yale University, 260 Whitney Ave, New Haven, CT, 06520, USA\n- 97 INAF, Osservatorio Astronomico di Trieste, Via G. B. Tiepolo 11, I-34131 Trieste, Italy.\n- 98 School of Physics, Korea Institute for Advanced Study, 85 Hoegiro, Dongdaemun-gu, Seoul 130-722, Republic of Korea\n- 99 Department of Physics, Lehigh University, 16 Memorial Drive East, Bethlehem, PA 18015, USA\n- 100 Departament d'Astronomia i Meteorologia, Facultat de F'ısica, Universitat de Barcelona, E-08028 Barcelona, Spain\n- 101 A*MIDEX, Aix Marseille Universit'e, CNRS, LAM (Laboratoire d'Astrophysique de Marseille) UMR 7326, F-13388 Marseille cedex 13, France\n- 102 Campus of International Excellence UAM+CSIC, Cantoblanco, E-28049 Madrid, Spain\n- 103 Instituto de Astrof'ısica de Andaluc'ıa (CSIC), Glorieta de la Astronom'ıa, E-18080 Granada, Spain\n- 104 Department of Astronomy, Columbia University, New York, NY 10027, USA\n- 105 Institute for Applied Computational Science, SEAS, Harvard University, 52 Oxford Street, Cambridge, MA 02138, USA\n- 106 Department of Physics, University of California, Berkeley,", '1. INTRODUCTION': "Comprehensive wide-field imaging and spectroscopic surveys of the sky have played a key role in astronomy, leading to fundamental new breakthroughs in our understanding of the Solar System; our Milky Way Galaxy and its constituent stars and gas; the nature, properties, and evolution of galaxies; and the Universe as a whole. The Sloan Digital Sky Survey (SDSS), which started routine  \nCA 94720, USA  \n- 107 Universit'e de Franche-Comt'e, Institut Utinam, UMR CNRS 6213, OSU Theta, Besan¸con, F-25010, France\n- 108 Department of Astronomy and Astrophysics, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA\n- 109 Dipartimento di Fisica e Astronomia, Universit'a di Padova, Vicolo dell'Osservatorio 2, I-35122 Padova, Italy\n- 110 Department of Physics, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA\n- 111 Department of Astronomy and Space Science, Sejong University, Seoul, 143-747, Korea\n- 112 SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA\n- 113 Universitats-Sternwarte Munchen, Scheinerstrasse 1, D-81679 Munich, Germany\n- 114 Cluster of Excellence, Boltzmannstraße 2, D-85748 Garching, Germany\n- 115 Department of Physics, Kansas State University, 116 Cardwell Hall, Manhattan, KS 66506, USA\n- 116 National Abastumani Astrophysical Observatory, Ilia State University, 2A Kazbegi Ave., GE-1060 Tbilisi, Georgia\n- 117 Instituto de F'ısica, UFRGS, Caixa Postal 15051, Porto Alegre, RS - 91501-970, Brazil\n- 118 Gemini Observatory, 670 N. A'Ohoku Place, Hilo, HI 96720, USA\n- 119 Astrophysics Research Institute, Liverpool John Moores University, IC2, Liverpool Science Park, 146 Brownlow Hill, Liverpool L3 5RF, UK\n- 120 Universit'e de Nice Sophia-Antipolis, CNRS, Observatoire de Cˆote d'Azur, Laboratoire Lagrange, BP 4229, F-06304 Nice Cedex 4, France\n- 121 Department of Physics and Astronomy, Ohio University, 251B Clippinger Labs, Athens, OH 45701, USA\n- 122\n- Instituto de Ciencias del Espacio (CSIC-IEEC), Facultad de Ciencias, Campus UAB, E-08193, Bellaterra, Spain\n- 123 University of Texas at Austin, Hobby-Eberly Telescope, 32 Fowlkes Rd, McDonald Observatory, TX 79734-3005, USA\n- 124 Indian Institute of Astrophysics, II Block, Koramangala, Bangalore 560 034, India\n- 125 Brookhaven National Laboratory, Bldg 510, Upton, NY 11973, USA\n- 126 National Optical Astronomy Observatory, 950 North Cherry Avenue, Tucson, AZ, 85719, USA\n- 127 Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510, USA\n- 128 Department of Physics, Fisk University, 1000 17th Avenue North, Nashville, TN 37208, USA 129  \nCorresponding authors.  \n- 130 The Penn State Astrobiology Research Center, Pennsylvania State University, University Park, PA 16802, USA\n- 131 SEPnet, South East Physics Network, UK\n- 132 School of Physics and Astronomy, University of St Andrews, St Andrews, Fife, KY16 9SS, UK\n- 133 Institute of Theoretical Astrophysics, University of Oslo, NO-0315 Oslo, Norway\n- 134 INFN/National Institute for Nuclear Physics, Via Valerio 2, I-34127 Trieste, Italy.\n- 135 Department of Physical Sciences, The Open University, Milton Keynes MK7 6AA, UK.\n- 136 H.L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman, OK 73019, USA\n- 137 Department of Astronomy and Astrophysics and the Enrico Fermi Institute, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637, USA\n- 138 National Astronomical Observatories, Chinese Academy of Sciences, Beijing, 100012, China\n- 139 Key Laboratory of Optical Astronomy, National Astro-  \noperations in 2000 April, has carried out imaging and spectroscopy over roughly 1/3 of the Celestial Sphere. The SDSS uses a dedicated 2.5-meter wide-field telescope (Gunn et al. 2006), instrumented with a sequence of sophisticated imagers and spectrographs. The SDSS has gone through a series of stages. SDSS-I (York et al. 2000), which was in operation through 2005, focused on a 'Legacy' survey of five-band imaging (using what was at the time the largest camera ever used in optical astronomy; Gunn et al. 1998) and spectroscopy of well-defined samples of galaxies (Strauss et al. 2002; Eisenstein et al. 2001) and quasars (Richards et al. 2002), using a 640fiber pair of spectrographs (Smee et al. 2013). SDSS-II operated from 2005 to 2008, and finished the Legacy survey. It also carried out a repeated imaging survey of the Celestial Equator in the Fall sky to search for supernovae (Frieman et al. 2008), as well as a spectroscopic survey of stars to study the structure of the Milky Way (Yanny et al. 2009).  \nSDSS-III (Eisenstein et al. 2011) started operations in Fall 2008, completing in Summer 2014. SDSS-III consisted of four interlocking surveys:  \n- · The Sloan Exploration of Galactic Understanding and Evolution 2 (SEGUE-2; C. Rockosi et al. 2015, in preparation) used the SDSS-I/II spectrographs to obtain R ∼ 2000 spectra of stars at high and low Galactic latitudes to study Galactic structure, dynamics, and stellar populations. SEGUE-2 gathered data during the 2008-2009 season.\n- · The Baryon Oscillation Spectroscopic Survey (BOSS; Dawson et al. 2013) used the SDSS imager to increase the footprint of the SDSS imaging in the Southern Galactic Cap in the 2008-2009 season. The SDSS spectrographs were then completely rebuilt, with new fibers (2 '' entrance aperture rather than 3 '' , 1000 fibers per exposure), as well as new gratings, CCDs, and optics. Galaxies (B. Reid et al. 2015, in preparation) and quasars (Ross et al. 2012) were selected from the SDSS imaging data, and are used to study the baryon oscillation feature in the clustering of galaxies (Anderson et al. 2014c,a) and Lymanα absorption along the line of sight to distant quasars (Busca et al. 2013; Slosar et al. 2013; Font-Ribera et al. 2014; Delubac et al. 2015). BOSS collected spectroscopic data from 2009 December to 2014 July.\n- · The Apache Point Observatory Galaxy Evolution Experiment (APOGEE; S. Majewski et al. 2015, in preparation) used a separate 300-fiber high-resolution ( R ∼ 22 , 500) H -band spectrograph to investigate the composition and dynamics of stars in the Galaxy. The target stars were selected from the database of the Two Micron AllSky Survey (2MASS; Skrutskie et al. 2006); the resulting spectra give highly accurate stellar surface temperatures, gravities, and detailed abundance measurements. APOGEE gathered data from 2011 May to 2014 July.  \n- · The Multi-Object APO Radial Velocity Exoplanet Large-area Survey (MARVELS; J. Ge et al. 2015, in preparation) used a 60-fiber interferometric spectrograph to measure high-precision radial velocities of stars to search for extra-solar planets and brown dwarfs orbiting them. MARVELS gathered data from 2008 October to 2012 July.  \nThe SDSS data have been made available to the scientific community and the public in a roughly annual cumulative series of data releases. These data have been distributed (Thakar 2008b) in the form of direct access to raw and processed imaging and spectral files and also through a relational database (the 'Catalog Archive Server', or 'CAS'), presenting the derived catalog information. As of DR12 these catalogs present information on a total of ∼ 470 million objects in the imaging survey, and 5.3 million spectra.  \nThe Early Data Release (EDR; Stoughton et al. 2002), and Data Releases 1-5 (DR1; Abazajian et al. 2003, DR2; Abazajian et al. 2004, DR3; Abazajian et al. 2005, DR4; Adelman-McCarthy et al. 2006, and DR5; AdelmanMcCarthy et al. 2007) included data from SDSS-I. DR6 and DR7 (Adelman-McCarthy et al. 2008; Abazajian et al. 2009) covered the data in SDSS-II. The data from SDSS-III have appeared in three releases thus far. DR8 (Aihara et al. 2011) included the final data from the SDSS imaging camera, as well as all the SEGUE-2 data. DR9 (Ahn et al. 2012) included the first spectroscopic data from BOSS. DR10 (Ahn et al. 2014) roughly doubled the amount of BOSS data made public, and included the first release of APOGEE data.  \nThe SDSS-III collaboration has found it useful to internally define a data set associated with the data taken through 2013 Summer, which we designate as 'DR11'. The SDSS-III completed data-taking in 2014 July, and the present paper describes both DR11 and Data Release 12 (DR12). Like previous data releases, DR12 is cumulative; it includes all data taken by SDSS to date. DR12 includes almost 2.5 million BOSS spectra of quasars, galaxies, and stars over 9,376 square degrees: 155,000 SEGUE-2 spectra of 138,000 stars (as released in DR8), and 618,000 APOGEE spectra of 156,000 stars. It also includes the first release of MARVELS data, presenting 197,000 spectra of 5,500 stars (3,300 stars with > 16 observations each). Because some BOSS, APOGEE, and MARVELS scientific papers have been based on the DR11 sample, this paper describes the distinction between DR11 and DR12 and the processing software for the two data sets, and how to understand this distinction in the database.  \nThe data release itself may be accessed from the SDSSIII website 140 or the DR12 page of the new pan-SDSS website. 141 DR11 is similarly available through the same interfaces. The outline of this paper is as follows. We summarize the full contents of DR11 and DR12 in Section 2, emphasizing the quantity of spectra and the solid angle covered by each of the surveys. Details for each component of SDSS-III are described in Section 3 (MARVELS), Section 4 (BOSS) and Section 5 (APOGEE).  \nThere have been no updates to SEGUE-2 since DR9 and we do not discuss it further in this paper. We describe the distribution of the data in Section 6, and conclude, with a view to the future, in Section 7.", '2. SUMMARY OF COVERAGE': "DR12 presents all data gathered by SDSS-III, which extended from 2008 August to 2014 June, plus a small amount of data gathered with the BOSS and APOGEE instruments in the first two weeks of 2014 July under the auspices of the next phase of the Sloan Digital Sky Survey, SDSS-IV (see Section 7). The contents of the data release are summarized in Table 1, and are described in detail in the sections that follow for each component survey of the SDSS-III.  \nAs described in Section 4, the BOSS spectroscopy is now complete in two large contiguous regions in the Northern and Southern Galactic caps. DR12 represents a ∼ 40% increment over the previous data release (DR10). The first public release of APOGEE data (Section 5) was in DR10; DR12 represents more than a three-fold increase in the number of spectra, and six times as many stars with 12 or more visits. In addition, DR12 includes the first release of data from MARVELS. MARVELS was in operation for four years (2008-2012); all resulting data are included in the release. The MARVELS data (Section 3) include ∼ 5,500 unique stars, most of which have 20-40 observations (and thus radial velocity measurements) per star. DR11 and DR12 represent different pipeline processing of the same observed MARVELS data. The MARVELS fields were selected to have > 90 FGK stars with V < 12 and 30 giant stars with V < 11 in the SDSS telescope 3 · diameter field of view. A set of pre-selection spectra of these fields to distinguish giants and dwarfs and thus refine the MARVELS target list was taken by the SDSS spectrograph in 2008. The raw data from these observations were released as part of DR9. In DR12, we provide the outputs from custom reductions of these data.  \nWhile SDSS-III formally ended data collection at the end of the night of 2014 June 30, the annual summer maintenance shutdown at APO occurred 2014 July 14. The SDSS-III BOSS and APOGEE targeting programs were continued during these two weeks and are included in the DR12 release.  \nIn addition, prototype and commissioning data were obtained during SDSS-III for the SDSS-IV Mapping Nearby Galaxies at APO (MaNGA) project (Bundy et al. 2015), which uses the BOSS spectrographs to measure spatially resolved spectra across galaxies. The raw data from these observations are included in DR12, but reduced data products (including kinematic and stellar population measurements) will be released only with the first SDSS-IV data release.  \nWe also made a single fiber connection from the APOGEE instrument to the nearby New Mexico State University (NMSU) 1-m telescope at APO for observations when the APOGEE instrument was not being fed photons from the 2.5-m telescope. These observations, of a single star at a time, were taken to extend the range of the APOGEE-observed stars to brighter limits, giving improved calibration with existing observations of these stars (see Holtzman et al. 2015, for details). These data and the reductions are included in the standard SDSS-III  \nAPOGEE DR12 products and can be identified by the denoted source.  \nTable 1 Contents of DR11 and DR12  \nTable 1 -Continued  \n- a Removing all duplicates, overlaps, and repeat visits from the 'Total' column.\n- b These numbers are unchanged since DR8.\n- c Number of plate observations that were successfully processed through the respective pipelines.\n- d Each MARVELS observation of a star generates two spectra. Unique is number of unique stars.\n- e 2,155 stars were observed during both the commissioning period and the main survey. Because commissioning and survey spectra are kept separate in the data processing, these objects are counted twice in the Unique column.\n- f The statistics in the following indented lines include only those observations which met the requirements of being survey quality.\n- g Signal-to-noise ratio per half resolution element > 100, summed over all observations of a given star.\n- h Kepler stars were originally targeted by APOGEE under an ancillary program, but eventually became part of the main target selection.\n- i Repeated observations of plates in BOSS are from the Reverberation Mapping program (Shen et al. 2015b, including 30 observations of a single set of targets to study variability), several other ancillary programs, and several calibration programs.\n- j This count excludes the small fraction ( ∼ 0 . 5%) of the observations through fibers that are broken or that fell out of their holes after plugging. There were attempted observations of 2,512,000 BOSS spectra.\n- k '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 targeted in the redshift range 0 . 15 < z < 0 . 4, and CMASS galaxies in the range 0 . 4 < z < 0 . 8.\n- l 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: see Section A, Dawson et al. (2013), and Pˆaris et al. (2014).\n- m Quasars with redshifts in the range 2 . 15 < z < 3 . 5 provide the most signal in the BOSS spectra of the Lymanα forest.\n- n Non-sky spectra for which the automated redshift/classification pipeline (Bolton et al. 2012) gave no reliable classification, as indicated by the ZWARNING flag.\n- o Includes spectra from SDSS-I/II (DR7; Abazajian et al. 2009). Although the MARVELS interference spectra are in the optical range (5000 ˚ A < λ < 5700 ˚ A), for convenience of labeling we here differentiate between the MARVELS data as 'interferometric' and the original SDSS or BOSS spectrograph data as 'optical.'\n- p Spectra on good or marginal plates.", '3. MARVELS': "The MARVELS survey (J. Ge et al. 2015, in preparation) was designed to obtain a uniform census of radialvelocity-selected planets around a magnitude-limited sample of F, G, and K main sequence, subgiant, and giant stars. It aimed to determine the distribution of gas giant planets ( M > 0 . 5 M Jupiter ) in orbits of periods < 2 years and to explore the 'brown dwarf desert' over the mass range 13 < M < 80 M Jupiter (Grether & Lineweaver 2006). Measuring these distributions requires a target sample with well-understood selection and temporal sampling. These science goals translated to observational plans to monitor 8400 stars over 2-4 years with radial velocity accuracies of 10-50 m s -1 for 9 < V < 12 mag for each of 24 epochs per star. These radial velocity accuracy predictions were estimated as 2 times the theoretical photon-noise limit.  \nThe MARVELS instrument (Ge et al. 2009), the W. M. Keck Exoplanet Tracker, uses an innovative dispersed fixed-delay interferometer (DFDI) to measure stellar radial velocities, by observing the movements of stellar lines across the fringe pattern created by the interferometer. The wavelength coverage of the interferometer is 5000 ˚ A < λ < 5700 ˚ A and it simultaneously observes 60 science fibers.  \nMARVELS radial velocities (RVs) are differential measurements, based on the shift of a star's fringing spectrum at the current epoch relative to one from the template epoch. For more details on the MARVELS program and DFDI instruments see Eisenstein et al. (2011); Erskine & Ge (2000); Ge (2002); Ge et al. (2002, 2009); van Eyken et al. (2010) and J. Ge et al. (2015, in preparation).  \nAs described in Eisenstein et al. (2011), the original plan was to build two MARVELS spectrographs so as  \nto capture 120 stars per exposure and a total sample of 11,000 stars. However, due to lack of funding, the second spectrograph was not built, meaning that the total number of stars observed was about 5500. We unfortunately encountered significant challenges in calibrating the RV stability of the MARVELS instrument. These difficulties led us to end the MARVELS observing as of the summer shutdown in 2012 July, so as to focus on our data reduction efforts. For a detailed accounting and presentation of the observations see Table 1 and Figures 1 and 2. The typical RMS scatter of the radial velocity measurements in the data processing we have achieved to date has been 3-5 times greater than the photon noise limit. This increased RMS has significantly limited the ability to discover planets in the MARVELS data. However, the distribution of RMS values extends to near the photon noise limits and has led to cautious optimism that further improvements in processing and calibration may yield improved sensitivity to giant planets.  \nThe original data processing pipeline was based on software from earlier DFDI prototype instruments (e.g., Ge et al. 2006). This pipeline used the full 2-D phase information but the resulting radial velocity measurements were limited by systematic instrumental variations to an RMS of 100-200 m s -1 . As described in detail below, the two radial velocity estimates from this pipeline are presented in DR11 as the 'cross-correlation function' (CCF) and 'differential fixed-delay interferometry' (DFDI) reductions, the latter explicitly incorporating the phase information from the interferometric fringes. These reductions revealed instrumental calibration variations that required a redesign of the analysis approach.  \nA subsequent reworked processing pipeline only analyzes the collapsed one-dimensional (1-D) spectrum, without using the fringing information, but determines  \nthe calibration of the spectrograph dispersion on a more frequent basis (N. Thomas et al. 2015, in preparation). The results from this pipeline are presented in DR12 as the 'University of Florida One Dimensional' (UF1D) reductions.", '3.1. Scope and status': 'MARVELS data collection began in 2008 October and ended in 2012 July. The majority of MARVELS stars were observed 20-40 times (Figure 1), with a typical exposure time of 50-60 min. These exposure times were designed to reach a signal-to-noise ratio (SNR) sufficient to allow per-epoch RV precisions of tens of m s -1 on stars of 7 . 6 < V < 12 mag. The total number of observations was designed to enable the determination of orbital parameters of companions with periods between one day and two years without the need for follow-up RV measurements using additional telescopes. However, the problems in radial velocity calibration, the shortened MARVELS observing period, and the fact that the second MARVELS spectrograph was never built meant that this ideal was not met for all targets. The observing was split into two 2-year campaigns: Years 1+2: 2008 October - 2010 December; and Years 3+4: 2011 January - 2012 July. For any particular star, the time baseline between the first and last observation was thus typically 1.5-2 years.  \nDuring its four years of operation MARVELS obtained 1565 observations of 95 fields collecting multi-epoch data for 5700 stars, with observations of 60 stars per target field.  \nWhile we provide all raw data and intermediate data products in this release, the CCF and DFDI results are limited to the 3533 stars with more than 10 RV measurements. The UF1D analysis results include 5513 stars from the 92 fields that pass the basic quality requirements of the pipeline. Restricting to stars with ≥ 16 observed epochs, which might be considered a reasonable threshold for searching for companions in the MARVELS data, yields 3293 stars in DR11 and 3233 stars in DR12 (a small number because of somewhat tighter quality constraints).', '3.2. A Brief Guide to MARVELS Data': "Each spectrographic plate has two sets of 60 fiber holes, corresponding to two different fields to be observed in sequence. Both sets of fibers were plugged at the same time. In between observations of the two fields, the 'gang' connector that joins the fibers from the cartridges to the long fibers that run to the MARVELS instruments was switched between the two sets of fibers.  \nA MARVELS exposure is the result of light from each of 60 fibers passing through a two-beam interferometer with one slanted mirror and then dispersed in wavelength before being recorded on a 4k × 4k CCD. Thus each MARVELS image contains 120 individual spectra as the beamsplitter produces two interference patterns for each star, one from each beam. The RVs for each star can then be calculated from a comparison of the fringing spectrum observations at different epochs.  \nIn this data release we provide the two-dimensional (2D) raw images, the 2-D slices of extracted spectra, the 1D collapsed spectra, and the calculated stellar velocities  \nand associated observational metadata for each spectrum of each star and field.", '3.3. Target selection': "Target selection for MARVELS is described in full in Paegert et al. (2015). We here summarize the key aspects of the MARVELS target selection in each two-year phase of the survey.  \nMARVELS aimed to have a target sample in the range of 8 < V < 12 with a balance of 90% dwarf and subgiant stars with T eff < 6250 K, and ∼ 10% giant stars with 4300 < T eff < 5100 K (spectral types K2-G5). In the first two years of MARVELS, target selection was based on short 'pre-selection' observations obtained with the SDSS spectrographs during the first year of SDSS-III to determine stellar surface temperatures and surface gravities. Because these observations used much shorter exposure times than standard SDSS observations, they were not automatically processed with the standard SDSS pipeline. Instead, the SDSS pipeline was used with some custom modifications to provide stellar spectra suitable for processing with the SEGUE Spectroscopic Processing Pipeline (SSPP; Lee et al. 2008). The raw data for these spectra were released as part of DR9. In DR12 we release these custom spectroscopic images, extracted spectra, and derived SSPP parameters as flat files, but due to their specialized and non-standard nature these have not been loaded into the CAS.  \nUnfortunately, the derived log g values - needed to discriminate giants from dwarfs - from these moderateresolution spectra ( R ∼ 2000) were not reliable and the first two years of MARVELS targets resulted in a 35% giant fraction instead of the goal of 10%.  \nWe thus employed a new method for giant-dwarf selection in Years 3+4. For this second phase of the MARVELS survey, temperature estimates were derived based on V -K and J -K colors following the infrared flux method of Casagrande et al. (2010), and giants were rejected based on a requirement of a minimum reduced proper motion (Collier Cameron et al. 2007) based on the measured 2MASS J -band proper motion together with the J -band magnitude and J -H color.  \nFrom 2011 January onward all MARVELS observations were carried out simultaneously with APOGEE, using plug plates drilled with holes for both sets of targets. The spectroscopic cartridges were adapted to allow connection of both the APOGEE and MARVELS fibers to the long fibers that run to the stabilized rooms that house the respective instruments. This joint observation mode yielded significant overall observational efficiencies, but imposed the restriction that both surveys observe the same fields with the same cadence. This shifted the MARVELS target fields much farther south than originally planned as APOGEE pursued observations toward the center of the Milky Way.  \nThe sky distribution of all observed MARVELS fields is shown in Figure 2.", '3.4. MARVELS Data Analysis': "The MARVELS instrument is designed to be sensitive to wavelength shifts (and thus radial-velocity changes) in stellar spectra. It splits each input stellar spectrum into two beams, and then projects a slanted interference  \nFigure 1. Distribution of the number of observations made of each MARVELS star that was processed by the CCF+DFDI (black solid) and the UF1D (red dashed) pipelines and met the respective quality cuts.  \n<!-- image -->  \npattern of the recombined beams through a spectrograph (see Figure 3).  \nThe dispersed slanted interference pattern effectively magnifies the resolution of a moderate-resolution spectrograph ( R ∼ 11 , 000) by translating wavelength shifts in the dispersion (' x ') direction to much larger shifts in the ' y ' position. This slope is ∼ 5 pixel pixel -1 for MARVELS. The design goal of the MARVELS analysis is to measure the shift of the interferometric sinusoid in the y direction to determine the wavelength offset due to a radial velocity change.  \nThe key challenges in the processing of MARVELS data are the calibration of the wavelength solution on the detector, identification and extraction of each spectrum, and the measurement of the slant of the interferometric comb and of the resulting interference pattern of the absorption-line features.  \nOur approach to analyzing the MARVELS data will be described in detail in N. Thomas et al. (2015, in preparation), which specifically describes the UF1D pipeline. The CCF+DFDI and UF1D pipelines follow many of the same steps, but differ in choices of calibration reference sources and complexity of model for instrumental variations. We here outline the important differences in the CCF+DFDI and UF1D processing.", '3.4.1. Extraction of Spectra from the 2-D Images': "A key part of spectroscopic processing is determining the 'trace', i.e., where the light from a given fiber target falls on the CCD. In an idealized instrument, the trace would lie horizontally along the CCD (constant y ), and the light at a given wavelength would be distributed perpendicular to the trace (constant x ), In practice, this is not true, and we correct for these two according through a 'trace correction' and 'deslant correction'.  \nThe CCF+DFDI pipeline uses available Tungsten lamp continuum exposures with a diffuser to determine the trace of the spectrum on the CCD, and ThoriumArgon arc spectra to determine the deslant correction. The UF1D pipeline uses the Tungsten lamp exposures taken through an iodine cell to determine the trace, and the absorption lines in the observed stellar spectra to determine the deslant correction. The pipelines extract and correct 2-D arrays for each spectrum based on their respective trace and deslant corrections.", '3.4.2. Compression to One-Dimensional Spectra': "The CCF+DFDI pipeline takes the 2-D rectified spectrum and fits a sinusoid to the interference pattern along the y (slit) direction. The spectrum is then collapsed along y , and the resulting 1-D spectrum plus sinusoidal fit parameters are stored. The combination of the collapsed spectrum and the sinusoidal fits is denoted a 'whirl' in the provided CCF+DFDI data products.  \nThe UF1D pipeline focuses on improvements to the instrumental calibration without adding complications from the details of the phase extraction. It simply collapses the 2-D rectified spectra along the y direction to create 1-D spectra, removing the information contained in the fringes. The UF1D pipeline was implemented as a step toward a new pipeline still in development that will include the more detailed calibration model used in the UF1D pipeline (see below) and will also make use of the phase information from the 2-D spectra.", '3.4.3. Characterizing the Instrumental Wavelength Drift': 'Determining the instrumental wavelength drift over time is critical in deriving reliable radial-velocity measurements. The instrumental drift is measured from calibration lamp exposures taken before and after each science frame. The calibration exposures are from a Tungsten lamp shining through a temperature-stabilized Iodine gas cell (TIO). This extracted spectrum is compared to that of the calibration lamp exposures taken on either side of the reference epoch chosen as the baseline for that star.  \nFor the CCF+DFDI pipeline, the shift for each star was determined by comparing the extracted TIO spectrum to a single reference lamp spectrum taken on MJD 55165 (2009 November 29), and the measured radial velocity for the star in question was corrected by the resulting offset. This correction attempts to express all changes in the instrument by a single parameter per fiber. The large variance in the resulting radial velocities has shown that this approach does not fully capture the complex nature of the calibration changes across the detector.  \nIn an effort to capture the fact that the velocity offset may be a function of wavelength, the UF1D pipeline calculates a separate shift value for each 100-pixel chunk of each spectrum, corresponding to ∼ 17 ˚ A. The reference TIO pair for each field is chosen to be the one that brackets the observation with the highest stellar flux observations. These instrumental shift values are then used as corrections to each chunk of the spectrum before the stellar radial velocity shifts are determined.', '3.4.4. Measuring the Stellar Radial Velocity Shifts': 'In CCF+DFDI, the stellar radial velocity is measured by comparing the extracted stellar spectrum from a given stellar exposure to the spectrum at the template epoch. The template epoch is selected as the highest SNR observation available for the selected star. We first calculate the barycentric correction (due to the orbit of the Earth around the Sun) as part of the comparison with the template epoch, and then use cross-correlation to measure the radial velocity offset of the 1-D spectrum. This raw stellar radial velocity shift is corrected for the instrumental drift determination from the previous step and labeled as the CCF measurement. The fringe shifts as a function of wavelength are then used to refine these velocity offsets to generate the final DFDI measurements.  \nFigure 2. MARVELS sky coverage in equatorial coordinates. Each plate is plotted with a color-coding giving the number of epochs the plate was observed.  \n<!-- image -->  \n<!-- image -->  \nFigure 3. (left) Conceptual illustration of a portion of the spectrum of one star from MARVELS dispersed fixed-delay interferometry. For simplicity, we show only five absorption lines in this sample wavelength region; the full MARVELS wavelength range features thousands of absorption lines - most of these are blended at the MARVELS dispersion of R ∼ 11 , 000. The diagonal pattern of constructive and destructive interference is not sharp as in this simple diagram, but rather varies sinusoidally with y . The phase of the best-fitting sinusoid to each column of the data determines the corresponding wavelength shift, given the slope of the interference comb. (right) Illustration of some of the real-world effects of variable projection of spectra onto the focal plane, spectrograph alignment, point spread function, and the variable slope of the interference comb. Note the additional blending in each set of closely-separated absorption lines. There are 120 of these spectra (each roughly 4096 pixels by 34 pixels) per MARVELS exposure.  \n<!-- image -->  \nThese two successive calculations are reported in separate tables in DR11 with CCF and DFDI suffixes in the name of the respective tables.  \nIn principle, the DFDI radial velocities should be more precise. However, given the challenges in measuring stable radial velocities from the processing, we find it useful to compare the results with (DFDI) and without (CCF) the fringe corrections.  \nIn UF1D, the pixel shift of each stellar spectrum with respect to that from the template date is determined for the same 100-pixel chunk based on a least-squares solution that minimizes the difference in values in each pixel, and then corrected for the calibration drifts measured from the TIO measurements. The resulting calibrated shifts are converted into a radial-velocity measurement by using a wavelength solution from each 100-pixel chunk to covert from pixel shift to wavelength shift to velocity shift. The outlier-rejected mean velocity shift across all 100-pixel chunks is then taken as the velocity shift for that spectrum for that epoch.  \nThese radial velocity shifts are then corrected for the barycentric motion of each observation. Because the radial velocity measurements are all relative, the zero point of the radial velocities is meaningless, so the mean of all measurements for a given star is set to zero.  \nBecause of the two-beam nature of the DFDI instrument, each star observation results in two spectra. These computations are done separately for each of these two spectra. The published data tables present RVs separately for each beam. To estimate the RV for the star on a given epoch one would in principle simply average the radial velocities from the two measurements. Because of the noticeable number of outliers in individual beam measurements, the use of an outlier rejection scheme is recommended.', '3.5. Current Status and Remaining Challenges': "As Figure 4 shows, the current data processing results in stellar radial velocity variations of 50 m s -1 or larger even at high SNR, a value several times greater than that  \n<!-- image -->  \nFigure 4. Distribution of RMS of radial velocity measurements of MARVELS stars (grey points) for the DFDI (left), and UF1D (right) analyses, as a function of apparent magnitude. The mode of the RMS in each 0.5 mag bin (blue circles and line) highlights the significant number of stars with RMS near 50-100 m s -1 . However, a comparison with the theoretical photon limit (red dashed line) illustrates that the bulk of the RMS values are many times higher than the limit. Despite this, there are stars whose radial velocity repeatability approaches the theoretical limit, suggesting that the large scatter for many of the observations is due to calibration problems, which might be improved with further development of the pipeline.  \n<!-- image -->  \n<!-- image -->  \nFigure 5. MARVELS observations of the radial velocities of the stars (left) HD68988 compared to the exoplanet model of Butler et al. (2006); and (right) HIP-14810 compared to the model of Wright et al. (2009). The phased data are shown over two periods for ease of visualization.  \n<!-- image -->  \nexpected from photon statistics. This is mostly due to systematic uncalibrated wavelength shifts on timescales longer than a month; repeat observations of stars within the same lunation show much smaller radial velocity variations. However, the figures show that some stars show RMS radial velocity variations which approach the photon noise limit, suggesting that with proper calibration, the overall scatter should drop significantly. One possibility currently under investigation is that these stars represent specific fibers that are more stable, while the beams from other stars experienced greater hardware variation across repeated pluggings and fiber connections. Work continues on improving the analysis of the MARVELS data and our understanding of the long-term systematic effects.  \nDespite these challenges, the MARVELS DR11 reductions have been used to study low mass and sub-stellar companions (Wisniewski et al. 2012; Fleming et al. 2012; Ma et al. 2013), brown dwarfs in the 'desert' (Lee et al. 2011), and exotic orbital systems (Mack, III et al. 2013). Figure 5 shows MARVELS RV measurements of two stars with known exoplanets, showing that MARVELS data are in good agreement with existing orbital models for these systems.  \nHowever, in general the MARVELS data and analysis  \nto date have not achieved the survey requirements for radial velocity necessary to discover and characterize a fiducial 0.5M Jupiter planet in a 100-day orbit. Figure 4 shows the achieved radial velocity RMS for the current pipelines as a function of stellar magnitude. The upper band of objects with RMS from 1-10 km s -1 is predominantly true astrophysical variation from binary star systems. The distribution of objects with RMS values in the range of 100 m s -1 is bounded near the photon limit, but the bulk lies several times above these limits.", '4.1. Scope and Summary': "The BOSS main survey of galaxies and quasars over two large contiguous regions of sky in the Northern and Southern Galactic Caps was completed in Spring 2014. The majority of the galaxies were uniformly targeted for large-scale structure studies in a sample focused on relatively low redshifts ('LOWZ', with z < 0 . 4) and a sample with 0 . 4 < z < 0 . 7 designed to give a sample approximately volume-limited in stellar mass ('CMASS'; B. Reid et al. 2015, in preparation). The total footprint is about 10,400 deg 2 (Figure 6); the value of 9376 deg 2 in Table 1 excludes masked regions due to bright stars  \nand data that do not meet our survey requirements.  \nThe main BOSS survey was completed in 2014 February. The additional dark time available through the 2014 summer shutdown was devoted to a portfolio of additional science programs designed to maximize the science return while taking advantage of the unique abilities of the SDSS system. Two of the largest such programs were a variability study of 849 quasars, designed to measure time delays between continuum and emission line variations ('Reverberation Mapping'; Shen et al. 2015b), and an early start on the planned cosmological studies with SDSS-IV (the Sloan Extended QUasar, ELG and LRG Survey, hereafter 'SEQUELS', where 'ELG' stands for 'Emission Line Galaxy' and 'LRG' stands for 'Luminous Red Galaxy'), together with an exploratory set of plates to investigate the requirements for studies of highredshift ELGs and other aspects of SDSS-IV. These and other BOSS ancillary programs executed since the DR10 release are described in Appendix A.", '4.2. Highlights from BOSS DR11': "The DR11 and DR12 releases of BOSS data constitute increments of 35% and 47% in the number of spectra over DR10, respectively, processed using very similar pipelines. These increases were significant enough to warrant a new set of BOSS cosmological analyses for each of these releases. These key papers were one of the motivations for tagging a DR11 data set for later public release along with DR12. The cosmology analyses based on DR11 data include studies of isotropic galaxy clustering (Guo et al. 2015), anisotropic galaxy clustering (Song et al. 2014; Samushia et al. 2014; S'anchez et al. 2014; Gil-Mar'ın et al. 2014b,a; Reid et al. 2014; Beutler et al. 2014b), galaxy clustering in the LOWZ sample (Tojeiro et al. 2014), the baryon oscillations (BAO) in the clustering of the Lymanα forest of distant quasars (Bautista et al. 2014; Delubac et al. 2015), the first detection of BAO in the cross-correlation between the Lymanα forest and the quasars (Font-Ribera et al. 2014), an updated upper bound to the sum of neutrino masses (Beutler et al. 2014a), a summary BAO galaxy clustering analysis paper (Anderson et al. 2014b), and a joint cosmology analysis paper incorporating all of the BOSS cosmology constraints as well as those from Type Ia supernovae and anisotropies in the cosmic microwave background (Aubourg et al. 2014). The BOSS team plans a similar set of papers based on the full DR12 analyses.", '4.3. Data Reduction Changes for DR12': "The pipeline software for reduction of BOSS spectroscopic data was largely unchanged between DR10 and  \nDR11. The classification and redshift-measurement aspects of this software are described in Bolton et al. (2012).  \nThere were, however, some significant improvements to the spectrophotometric flux calibration routine for DR12. These improvements were made to mitigate low-level imprinting of (primarily) Balmer-series features from standard-star spectra onto science target spectra. This imprinting was first documented in Busca et al. (2013) in observed-frame stacks of quasar continuum spectra. Although this effect is generally undetectable in any single-spectrum analysis, it has a small but nonnegligible effect on the analysis of the Lymanα forest across many thousands of quasar spectra. The change implemented for DR12 consists of a simple masking and linear interpolation of the flux-calibration vectors over the observed-frame wavelength ranges shown in Table 2. A more flexible flux-calibration vector model is retained at other wavelengths to accommodate real small-scale features in the spectrograph throughput. This more flexible model was necessary for the original SDSS spectrographs due to time variation in the dichroic filters, although it is likely unnecessary for the improved optical coatings on those surfaces in BOSS (see Smee et al. 2013).  \nIn addition, we updated the pixel-response flats used to pre-process the spectrograph frames, improved the biassubtraction code to catch and correct electronic artifacts that appear in a small number of frames, and updated the CCD bad-pixel and bad-column masks to reduce the incidence of corrupted but previously unflagged spectra. These changes reduce the number of corrupted spectra, and more accurately flag those that remain.  \nTable 3 gives the full history of significant changes to the BOSS spectrograph detectors and the calibration software to process its data since the BOSS survey began. See also Table 2 of Ahn et al. (2012) for additional changes to the hardware.  \nTable 2  \nWavelength Ranges Masked During BOSS Spectrophotometric Calibration  \nTable 3  \nNote . -Observed-frame vacuum wavelength ranges that were masked and linearly interpolated during determination of spectrophotometric calibration vectors.  \nSignificant changes to the BOSS spectrographs and the data reduction pipeline  \nTable 3 -Continued  \nNote . - There are two BOSS spectrographs, each with a red and blue camera. Thus R2 refers to the red camera on the second spectrograph, which accepts light from fibers 501-1000. The August dates in the table above refer to the summer shutdowns.  \nAs in previous BOSS data releases, a unique tag of the idlspec2d spectroscopic pipeline software is associated with each unique sample of publicly released data. 142 Three tagged reductions of three separate samples are being released at the time of DR12. One ( v5 6 5 ) is the 'DR11' version that defines a homogeneous sample of BOSS data taken through Summer 2013; this is the version used in the cosmological analyses described in Section 4.2 above. A second label ( v5 7 0 ) defines the main DR12 BOSS cosmological survey at its point of  \ncompletion. A third tag ( v5 7 2 ) is associated with the several extra observing programs undertaken with the BOSS spectrographs in Spring 2014 following the completion of the main BOSS survey program (Section 4.1, Appendix A). These data-release software versions are summarized in Table 4.  \nMany of the pipeline changes for the ancillary programs involved bookkeeping and special cases for plates drilled with either fewer or more flux calibration stars. In addition the SEQUELS plates targeted ELGs at high redshift, so the upper redshift limit of the galaxy template fitting (Bolton et al. 2012) was extended from z = 1 to z = 2. Thus DR12 includes several thousand SDSS galaxy spectra with tabulated redshifts above z = 1.  \nTable 4  \nSpectroscopic pipeline versions associated with each BOSS data release.", '5. APOGEE': "In this paper, we release both DR11 and DR12 versions of the APOGEE outputs, with considerably more stars (see Table 1) in the latter. The APOGEE release is described in detail in Holtzman et al. (2015). The DR11 parameters and abundances use the same version of the APOGEE Stellar Parameters and Chemical Abundances Pipeline (ASPCAP; A. E. Garc'ıa P'erez et al. 2015, in preparation) as in DR10. The DR12 version of ASPCAP is a major upgrade, in which abundances are determined for 15 individual elements. In addition, the DR12 ASPCAP code incorporated a number of technical improvements: multiple searches to avoid local minima in parameter space, new model atmospheres with updated solar reference abundances and non-solar Carbonand α -element-to-Iron abundance ratios (M'esz'aros et al. 2012), the use of a Gauss-Hermite function instead of a Gaussian to represent the instrumental point-spread function, and upgrades to the atomic and molecular line lists. These improvements do not change the derived fundamental stellar parameters systematically, but do improve their accuracy.", '5.1. Scope and Summary': "142 SDSS data processing software is publicly available at http: //www.sdss.org/dr12/software/products/  \nThe APOGEE DR11 data include twice as many stars and spectra as DR10 (53,000 more stars and 200,000 more spectra), analyzed with the same pipeline. The APOGEE DR11 data have been used in several papers, including a determination of distances to and chemical abundances of red-clump stars (Bovy et al. 2014; Nidever et al. 2014), mapping of the Galactic interstellar medium using diffuse interstellar bands measured along the line of sight to APOGEE stars (Zasowski et al. 2015), and an identification of new Be stars and their H -band line profiles (Chojnowski et al. 2015).  \nAPOGEE DR12 represents a further year of data and thus includes another 46,000 stars and 240,000 spectra over DR11. It also uses the updated analysis pipeline described above. The sky coverage of the final APOGEE DR12, covering the bulge, disk, and halo of our Galaxy is shown in Figure 7. The additional observations of stars that already appeared in DR10 improve the SNR of these stars and also provide opportunities for studies of radial velocity and other variations in the observed stellar spectra. Figure 8 demonstrates that we achieved our goal of SNR > 100 per half-resolution element for the APOGEE sample. Figure 9 shows the distribution of time baselines and the number of observations of each star.  \nA succinct overview of the APOGEE survey was presented in Eisenstein et al. (2011) and a full summary will be given by S. Majewski et al. (2015, in preparation). The APOGEE spectroscopic data processing is  \nFigure 6. BOSS DR11 (left) and DR12 (right) spectroscopic sky coverage in the Northern Galactic Cap (top) and Southern Galactic Cap (bottom). The grey region (visible most clearly in the DR11 map) was the coverage goal for the final survey. The DR12 coverage map shows that we exceeded our original goals with a final total of 10,400 deg 2 . The color coding indicates the fraction of CMASS galaxy targets that receive a fiber. The average completeness is 94% due to the limitation that no two fibers can be placed closer than 62 '' on a given plate.  \n<!-- image -->  \nFigure 7. Sky coverage of APOGEE DR12 observations in Galactic coordinates. The number of visits to each field is denoted by the color coding from 1 visit (blue) through 12 or more visits (magenta).  \n<!-- image -->  \ndescribed in Nidever et al. (2015). The pipeline for deriving atmospheric parameters and abundances from the spectra will be described by A. E. Garc'ıa P'erez (2015, in preparation). The spectra, stellar parameters, and abundances for DR11 and DR12 are described in Holtzman et al. (2015).  \nFigure 10 shows the observed distribution of the key stellar parameters and abundances for APOGEE DR12. Obtaining robust and calibrated values of T eff , log g , and [M/H] along with individual abundances for 15 elements  \nhas required development of new stellar libraries (Zamora et al. 2015) and H -band spectral line lists (Shetrone et al. 2015). After describing these fits, we discuss a valueadded catalog of red clump stars, then describe specific target classes of APOGEE stars that are new since DR10.", '5.2. Abundances of 15 Elements in APOGEE DR12': 'In DR12, we provide the best fitting values of the global stellar parameters, as well as individual elemental abundances for C, N, O, Na, Mg, Al, Si, S, K, Ca, Ti, V, Mn,  \n<!-- image -->  \nFigure 8. Distribution of SNR of APOGEE stars in DR12. With 1.5 pixels per effective half-resolution element, the science requirements goal of SNR ≥ 100/half-resolution element is achieved with SNR ≥ 82/pixel (dashed green line). (left) 2-D histogram of SNR vs. 2MASS H magnitude. The red dash-dot lines denote the magnitude limits for the different bins of target brightness. The number of planned visits to APOGEE main targets was (1, 3, 6, 12, 24) visits for H < (11 . 0 , 12 . 2 , 12 . 8 , 13 . 3 , 13 . 8) mag. (right) 1-D histogram of SNR. The systematic floor in the effective SNR is ∼ 200 (red dash-dot line).  \n<!-- image -->  \n<!-- image -->  \nFigure 9. (left) Distribution of time intervals between observations of a given APOGEE target in DR12. (right) Distribution of number of visits for individual APOGEE targets in DR12.  \n<!-- image -->  \nFe, and Ni.  \nThe spectra are fit to models based on spectral libraries from astronomical observations combined with laboratory and theoretical transition probabilities and damping constants for individual species. The final measurements and associated uncertainties are calibrated to observations of stellar clusters, whose abundance patterns are assumed to be uniform.  \nThe abundances are most reliable for stars with effective surface temperatures in the range 3800 K ≤ T eff ≤ 5250 K. For cooler atmospheres ( T eff < 3800 K), the strengths of molecular transitions are increasingly sensitive to temperature, surface gravity, molecular equilibrium, and other physical details, giving rise to a greater uncertainty in the inferred abundances. Stars with warmer atmospheres ( T eff > 5250 K) or at low metallicity ([Fe/H] glyph[lessorsimilar] -1) have weaker lines, making it more difficult to measure abundances.', '5.3. Red Clump Stars in APOGEE': 'This APOGEE data release also contains the DR11 and DR12 versions of the APOGEE red-clump (APOGEE-RC) catalog. Red clump stars, helium coreburning stars in metal-rich populations, are good standard candles, and thus can be used as a spatial tracer of the structure of the disk and the bulge. RC stars are selected using the log g , [Fe/H], and near-infrared colors available for each APOGEE star. The construction of the DR11 APOGEE-RC catalog and the derivation of the distances to individual stars were described in detail by Bovy et al. (2014). The DR11 catalog contains 10,341 stars with distances accurate to about 5%, with a contamination estimated to be glyph[lessorsimilar] 7%.  \nThe DR12 RC catalog applies the same selection criteria to the full DR12 APOGEE sample, but re-calibrates the surface gravities to a scale appropriate for RC stars; the standard DR12 surface-gravity calibration is not appropriate for RC stars. The calibration starting from the uncalibrated outputs of ASPCAP for surface gravity, log g uncal . DR12 is  \nlog g RC = 1 . 03 log g uncal . DR12 -0 . 370 ,  \nfor 1 < log g uncal . DR12 < 3 . 8 (outside of this range the log g RC -log g uncal . DR12 correction is fixed to that at the edges of this range). The DR12 APOGEE-RC catalog contains 19,937 stars with an estimated contamination glyph[lessorsimilar] 3 . 5% (estimated in the same manner as for the DR11 catalog, see Bovy et al. 2014).', '5.4. Additional Target Classes in APOGEE DR12': "Target selection for APOGEE was described in Zasowski et al. (2013). As with BOSS, the targets for APOGEE are dominated by uniformly selected samples designed to meet the key APOGEE science goals, but also feature additional ancillary programs to take advantage of smaller-scale unique science opportunities presented by the APOGEE instrument. The final distribution of 2MASS magnitudes and colors for all APOGEE targets are presented in Figure 11, both as observed, and corrected for Galactic extinction. Because many of the APOGEE target fields are at quite low Galactic latitudes, the extinction corrections can be quite substantial, even in the infrared.  \nSome of the additional dark time from the early completion of the BOSS main survey was used for the existing  \nFigure 10. Key stellar parameters ( T eff , log g ) and key metallicity indicators ([M/H], [C/M], [N/M], [ α /M]) for stars with APOGEE observations in DR12. These distributions are strongly affected by the selection of stars targeted for APOGEE spectroscopy. The grey scale is logarithmic in number of stars.  \n<!-- image -->  \n-  \n-  \nFigure 11. Near-infrared colors and H magnitudes of APOGEE targets as observed (left panels) and corrected for Galactic dust extinction (right panels). The vertical dashed line in the lowerright panel at ( J -K s ) 0 = 0 . 5 mag indicates the selection cutoff for the main APOGEE red giant sample. Objects bluer than this line are from observations of telluric calibration stars, commissioning data, or ancillary program targets. The grey scale is logarithmic in number of stars.  \n<!-- image -->  \nAPOGEE main program, and allowed the addition and expansion of several ancillary science programs. DR12 adds four additional ancillary target classes to those described in Zasowski et al. (2013) and extends two previous ancillary programs. We briefly describe these additions here:  \nRadial Velocity Monitoring of Stars in IC 348: The 'Infrared Spectroscopy of Young Nebulous Clusters' (IN-SYNC) ancillary program originally observed the Perseus sub-cluster IC 348. Subsequent to those observations a set of stars was targeted for further follow-up to (1) search for sub-stellar companions in bright field stars of all spectral types; (2) search for stellar and sub-stellar companions around low-mass M stars; (3) search for premain-sequence spectroscopic binaries in IC 348; (4) study a newly identified Herbig Be object (HD 23478/BD+31 649) and (5) enhance the completeness of the IC 348 sample with 40 additional targets. These 122 stars are labeled with APOGEE TARGET2 bit set to 18.  \nProbing Binarity, Elemental Abundances, and False Positives Among the Kepler Planet Hosts: This ancillary project observed 159 Kepler Objects of Interest (KOI; e.g., Burke et al. 2014), 23 M dwarfs, and 25 eclipsing binaries, at high cadence ( ∼ 21 observations), over a period of 8 months to study binarity, abundances, and false positives in the planet host sample. This project aims to detect stellar and brown dwarf companions of Kepler host stars, provide detailed abundances for several elements, and understand planet formation in binary systems. KOI targets were selected from the KOI catalog with H V ega < 14; 'eclipsing binary' targets were selected with H < 13, periods > 5 days, and classified as having a 'detached morphology' as listed in the catalogs of Prˇsa et al. (2011) and Slawson et al. (2011), plus two systems from Gaulme et al. (2013); and 'M dwarf' targets were drawn from the catalog of Dressing & Charbonneau (2013) with T eff < 3500 K and H < 14. These  \n208 stars are labeled with APOGEE TARGET2 bit set to 19.  \nCalibration of the Gaia-ESO Spectroscopic Survey Program: A sample of 41 stars was observed to provide improved calibration of stellar parameters in conjunction with the Gaia-ESO Survey 143 (Pancino & GaiaESO Survey consortium 2012). These observations are labeled with the setting of APOGEE TARGET2 bit 20.  \nRe-Observation of Commissioning Bulge Stars to Verify Radial Velocity Accuracy: A set of 48 stars in the bulge of the Milky Way that had originally been observed during the early commissioning phase of the APOGEE instrument was re-observed to provide a verification of the APOGEE radial velocity estimates. These observations are labeled with the setting of APOGEE TARGET2 bit 21.  \nIn addition, two previous ancillary programs were expanded in DR12. The IN-SYNC ancillary program ( APOGEE TARGET2 =13) to study young stellar objects in the Perseus molecular cloud (see Cottaar et al. 2014 and Foster et al. 2015 for more details) was expanded in DR12 to observe 2,634 stars in the Orion A molecular cloud. The APOGEE ancillary program to observe Kepler stars for asteroseismology and stellar parameter calibration ( APOGEE TARGET1 =27) proved extraordinarily useful (e.g., Epstein et al. 2014) and was folded into the main APOGEE target selection for DR12.", '6. DATA DISTRIBUTION': "The data for DR11 and DR12 are distributed through the same mechanisms available in DR10, with some URL modifications to accommodate the ongoing transition to SDSS-IV and an associated unification of the SDSS web presence under the sdss.org domain. Raw and processed image and spectroscopic data are available through the Science Archive Server 144 (Neilsen 2008) and through an interactive web application. 145 The catalogs of photometric, spectroscopic, and derived quantities are available through the Catalog Archive Server 146 (Thakar et al. 2008; Thakar 2008a). More advanced and extensive querying capabilities are available through 'CasJobs', which allows time-consuming queries to be run in the background 147 (Li & Thakar 2008). GUI-driven queries of the database are also available through SkyServer. 148 Links to all of these methods are provided at http://www.sdss.org/dr12/data\\_ access . The data processing software for APOGEE, BOSS, and SEGUE are publicly available at http:// www.sdss.org/dr12/software/products . A set of tutorial examples for accessing SDSS data is provided at http://www.sdss.org/dr12/tutorials .", '7. THE FUTURE: SDSS-IV': "SDSS-IV began in 2014 July, as SDSS-III completed its observations. It will continue the legacy of SDSS with three programs on the 2.5-m Sloan Foundation Telescope to further our understanding of our Galaxy, nearby galaxies, and the distant Universe.  \n- 146 http://skyserver.sdss.org/dr12  \n-  \nThe extended Baryon Oscillation Spectroscopic Survey (eBOSS; K. Dawson et al. 2015, in preparation) is obtaining spectra of LRGs over the redshift range 0 . 6 < z < 1 . 0 and quasars in the range 0 . 9 < z < 3 . 5 over 7500 deg 2 , and ELGs from 0 . 6 < z < 1 . 0 over 1500 deg 2 , with an aim to measure the BAO peak to an accuracy of < 2% in four redshift bins. eBOSS also includes a spectroscopic survey (TDSS) of variable stars and quasars (the Time Domain Spectroscopic Survey; TDSS; E. Morganson et al. 2015, in preparation), along with a program to obtain optical spectra of X-ray selected sources (The SPectroscopic IDentification of ERosita Sources; SPIDERS). Many of the BOSS ancillary programs described in Appendix A are exploratory or pilot studies to test aspects of eBOSS target selection.  \nSDSS-I/II established our understanding of galaxies in the z ∼ 0 . 1 Universe. The SDSS-IV Mapping Nearby Galaxies at APO (MaNGA) program (Bundy et al. 2015) will revisit 10,000 of these galaxies in far greater detail using integral-field fiber bundles to study spatiallyresolved galaxy properties, star formation, and evolution.  \nAs Figure 7 makes clear, APOGEE has sampled only a fraction of the Milky Way, and has missed the Southern skies completely. The APOGEE exploration of the Milky Way will continue with SDSS-IV. APOGEE-2 will use the existing spectrograph on the 2.5-m Sloan Foundation Telescope. In addition, a second APOGEE instrument will be built and installed on the 2.5-m du Pont Telescope at Las Campanas Observatory, Chile, providing an allsky view of the Galaxy.  \nSDSS-IV will continue the sequence of SDSS public data releases, starting with a first release of spectroscopic data in 2016.  \nData Release 12 marks the final data release of the SDSS-III project, which began development in 2006 and conducted six years of fully-dedicated operations at APO. In total, SDSS-III collected 2350 deg 2 of ugriz imaging and about 3.4 million spectra. The total SDSS data set now contains over 5 million spectra, with connections to nearly all areas of astrophysics. The median extra-galactic redshift is now 0.5. We thank the full SDSS-III collaboration and partner institutions for their tremendous efforts toward the realization of the ambitious goals of the project, and we look forward to the many public uses of this vast legacy data set.  \nSDSS-III Data Release 12 has made 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.  \nSDSS-III Data Release 12 based APOGEE targeting decisions in part on data collected by the Kepler mission. Funding for the Kepler mission is provided by the NASA Science Mission directorate.  \nSDSS-III Data Release 12 based MARVELS targeting decisions in part on the Guide Star Catalog 2.3. The Guide Star Catalogue-II is a joint project of the Space Telescope Science Institute and the Osservatorio Astronomico di Torino. Space Telescope Science Institute is operated by the Association of Universities for Research  \nin Astronomy, for the National Aeronautics and Space Administration under contract NAS5-26555. The participation of the Osservatorio Astronomico di Torino is supported by the Italian Council for Research in Astronomy. Additional support is provided by European Southern Observatory, Space Telescope European Coordinating Facility, the International GEMINI project and the European Space Agency Astrophysics Division.  \nSDSS-III Data Release 12 selected a significant number of BOSS ancillary targets based on data products from the Wide-field 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.  \nSDSS-III Data Release 12 made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013).  \nSDSS-III Data Release 12 made use of the Exoplanet Orbit Database and the Exoplanet Data Explorer at exoplanets.org.  \nSDSS-III Data Release 12 made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.  \nSDSS-III Data Release 12 made use of data from PanSTARRS1. The Pan-STARRS1 Surveys (PS1) have been made possible through contributions by the Institute for Astronomy, the University of Hawaii, the Pan-STARRS Project Office, the Max-Planck Society and its participating institutes, the Max Planck Institute for Astronomy, Heidelberg and the Max Planck Institute for Extraterrestrial Physics, Garching, The Johns Hopkins University, Durham University, the University of Edinburgh, the Queen's University Belfast, the Harvard-Smithsonian Center for Astrophysics, the Las Cumbres Observatory Global Telescope Network Incorporated, the National Central University of Taiwan, the Space Telescope Science Institute, and the National Aeronautics and Space Administration under Grant No. NNX08AR22G issued through the Planetary Science Division of the NASA Science Mission Directorate, the National Science Foundation Grant No. AST-1238877, the University of Maryland, Eotvos Lor'and University (ELTE), and the Los Alamos National Laboratory.  \nFunding 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 Office of Science. The SDSS-III web site is http://www.sdss3.org/ .  \nSDSS-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 Astrofisica 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,  \nPennsylvania 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.", 'REFERENCES': "- Abazajian, K., Adelman-McCarthy, J. 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T., et al. 2014, ApJ, 785, 104 Sadibekova, T., Pierrre, M., Clerc, N., et al. 2014, A&A, 571, A87 Samushia, L., Reid, B. A., White, M., et al. 2014, MNRAS, 439, 3504  \n- S'anchez, A. G., Montesano, F., Kazin, E. A., et al. 2014, MNRAS, 440, 2692\n- Schlafly, E. F., Finkbeiner, D. P., Juri'c, M., et al. 2012, ApJ, 756, 158\n- Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, ApJ, 500, 525\n- Schneider, D. P., Richards, G. T., Hall, P. B., et al. 2010, AJ, 139, 2360\n- Shen, Y., Greene, J. E., Ho, L. C., et al. 2015a, ApJ,  \narXiv:1502.01034, arXiv:1502.01034  \n- Shen, Y., Brandt, W. N., Dawson, K. S., et al. 2015b, ApJS, 216,  \n4", 'A. TARGET SELECTION AND SCIENTIFIC MOTIVATION FOR BOSS ANCILLARY SCIENCE PROGRAMS': "As described in Eisenstein et al. (2011) and Dawson et al. (2013), up to 10% of the BOSS targets were reserved for ancillary programs, i.e., those with scientific aims that went beyond those of the core quasar and galaxy samples. Ancillary programs observed in the 2009-2010 and 2010-2011 seasons are documented in Dawson et al. (2013), and those observed in the 2011-2012 season were documented in Ahn et al. (2014). There were additional categories of ancillary programs included in the 2012-2014 observing seasons, which are released for the first time with DR12, and which we document here. In particular, BOSS completed observations of its uniform galaxy and quasar samples over the full footprint (Figure 6) several months before the end of SDSS-III observing, allowing a number of focused programs to be carried out.  \nAll BOSS ancillary programs initiated after 2012 can be identified by having a non-zero ANCILLARY TARGET2 bitmask. We present in this Appendix the scientific motivation for each program, the number of fibers assigned, and a description of the target selection algorithms. The labels for each target bit name appear in bold font in what follows. The new programs fall into three categories: those that are dispersed throughout the remainder of the BOSS footprint  \n- Shetrone, M., Bizyaev, D., Lawler, J., et al. 2015, ApJS, arXiv:1502.04080, submitted , arXiv:1502.04080\n- Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163\n- Slawson, R. W., Prˇsa, A., Welsh, W. F., et al. 2011, AJ, 142, 160 Slosar, A., Irˇsiˇc, V., Kirkby, D., et al. 2013, J. Cosmology Astropart. Phys., 4, 26\n- Smee, S. A., Gunn, J. E., Uomoto, A., et al. 2013, AJ, 146, 32 Song, Y.-S., Sabiu, C. G., Okumura, T., Oh, M., & Linder, E. V. 2014, J. Cosmology Astropart. Phys., 12, 5\n- Stassun, K. G., Mathieu, R. D., Cargile, P. A., et al. 2008, Nature, 453, 1079\n- Steinmetz, M., Zwitter, T., Siebert, A., et al. 2006, AJ, 132, 1645 Stoughton, C., Lupton, R. H., Bernardi, M., et al. 2002, AJ, 123, 485\n- Strateva, I. V., Brandt, W. N., Eracleous, M., Schneider, D. P., & Chartas, G. 2006, ApJ, 651, 749\n- Strauss, M. A., Weinberg, D. H., Lupton, R. H., et al. 2002, AJ, 124, 1810\n- Tasse, C., Le Borgne, D., Rottgering, H., et al. 2008, A&A, 490, 879\n- Taylor, A. R., Bhatnagar, S., Condon, J., et al. 2014, arXiv:1405.0117, arXiv:1405.0117\n- Thakar, A. R. 2008a, AIP Computing in Science & Engineering, 10, 65\n- -. 2008b, AIP Computing in Science & Engineering, 10, 9\n- Thakar, A. R., Szalay, A., Fekete, G., & Gray, J. 2008, AIP Computing in Science & Engineering, 10, 30\n- Tojeiro, R., Ross, A. J., Burden, A., et al. 2014, MNRAS, 440, 2222\n- van Eyken, J. C., Ge, J., & Mahadevan, S. 2010, ApJS, 189, 156 van Haarlem, M. P., Wise, M. W., Gunst, A. W., et al. 2013, A&A, 556, A2\n- V'eron-Cetty, M.-P., & V'eron, P. 2010, A&A, 518, A10  \nVoges, W., Aschenbach, B., Boller, T., et al. 1999, A&A, 349, 389  \n- -. 2000a, VizieR Online Data Catalog, 9029, 0\n- -. 2000b, IAU Circ., 7432, 1\n- Wisniewski, J. P., Ge, J., Crepp, J. R., et al. 2012, AJ, 143, 107 Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868\n- Wright, J. T., Fischer, D. A., Ford, E. B., et al. 2009, ApJ, 699, L97\n- Yanny, B., Rockosi, C., Newberg, H. J., et al. 2009, AJ, 137, 4377 Y'eche, C., Petitjean, P., Rich, J., et al. 2010, A&A, 523, A14 York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120, 1579\n- Zamora, O., Garcia-Hernandez, D. A., Allende Prieto, C., et al.\n- 2015, AJ, arXiv:1502.05237, accepted , arXiv:1502.05237 Zasowski, G., Johnson, J. A., Frinchaboy, P. M., et al. 2013, AJ, 146, 81\n- Zasowski, G., M'enard, B., Bizyaev, D., et al. 2015, ApJ, 798, 35 Zhu, G., Zaw, I., Blanton, M. R., & Greenhill, L. J. 2011, ApJ, 742, 73  \nat low density ('parallel ancillary programs', Section A.1, Table 5), those that were located in small regions of sky at high density ('dedicated ancillary programs', Section A.2, Table 6), and those associated with a pilot survey in advance of eBOSS ('SEQUELS programs', Section A.3, Table 7). Most of the latter two categories were observed in the last six months of SDSS-III observations, after the main survey had been completed. Some of these programs are self-contained science projects in themselves, some represent calibrations or refinements of SDSS or BOSS spectroscopic programs, and some, like the SEQUELS programs, are preparatory for future surveys, especially eBOSS. While few of these programs have generated published results at this writing, a significant number of papers are in preparation which use these data. Note that there is often scientific or algorithmic overlap between many of the programs, reflecting the multiple calls for ancillary programs within the SDSS collaboration.  \nThe selection algorithms in these different programs typically use PSF, model (for galaxy magnitudes), or cmodel (for galaxy colors; Abazajian et al. 2004) SDSS photometry, all corrected for Galactic extinction following Schlegel et al. (1998). Occasionally, fiber magnitudes are also used. The selection for many programs also uses photometry from the Wide-field Infrared Survey Explorer (WISE, Wright et al. 2010). WISE carried out a full-sky survey in four bands, centered at 3.6, 4.5, 12, and 22 µ m; the resulting photometry (which is reported on a Vega system, unlike the AB system of SDSS) is referred to as W1, W2, W3, and W4, respectively, in what follows. We include the WISE catalog photometry of SDSS objects in both the DR11 and DR12 contexts in the CAS database. A number of programs use a reprocessing of the WISE data (Lang 2014) or forced photometry of WISE images at positions from SDSS (Lang et al. 2014).", 'A.1. BOSS Parallel Ancillary Programs': "All new parallel ancillary target classes found in DR12 were given a priority lower than both the primary galaxy and quasar targets and previously approved ancillary programs. The targeted samples for these parallel ancillary programs are therefore not complete. We list these programs roughly in the order of the distance to the targets; in Table 5, we list them in bit order.  \nCharacterizing Low-mass M Dwarfs Using Wide Binaries: M dwarf stars make up ∼ 70% of the stars in the Galaxy by number and have lifetimes longer than the age of the Universe. They are thus valuable tracers of the chemical and dynamical evolution of the Milky Way, but their complex spectra dominated by molecular bands make it difficult to determine their ages and metallicities. This program targets earlier-type binary companions to known M dwarf stars; these companions should share the same metallicity and age as the M dwarf but have atmospheres that are easier to interpret. These systems can be used to refine relations between M dwarf properties and spectral signatures (e.g., Stassun et al. 2008; Dhital et al. 2012).  \nFibers denoted by the SPOKE2 target flag were assigned to candidate binary companions of spectroscopically confirmed low-mass stars in the Sloan Low-mass Wide Pairs of Kinematically Equivalent Stars (SLoWPoKES; Dhital et al. 2010, 2015) project. A previous ancillary program, the Low-Mass Binary Stars program (Dawson et al. 2013) consisted of systems with angular separations 65-180 '' . SPOKE2 extends that target sample to late-M spectral types, identifying binaries with separations between 3 and 20 arcsec. No proper motion requirement is imposed (Dhital et al. 2015). Targets have magnitudes in the range 17 < i PSF < 21 . 3.  \nA Census of Nearby Galaxies: We do not yet have a complete catalog of galaxies within 200 Mpc (Kasliwal 2011), hampering studies of nearby transients and the fine detail of the large-scale distribution of galaxies. The Palomar Transient Factory (PTF; Law et al. 2009) is performing a narrow-band survey in two filters, centered at 656 nm and 663 nm, to complete the catalog of galaxies in the local universe out to 200 Mpc. A sample of galaxies denoted by the PTF GAL target flag was selected for spectroscopic confirmation. Galaxies without known redshift were observed if they had an SDSS counterpart, a color m 656 -m 663 > 0 . 7 mag, and relatively blue broadband color as measured by SDSS ( g model -i model < 1 . 3 mag). Images of all candidate galaxies were first visually inspected to avoid spurious detections.  \nQuasar Spectrophotometric Calibration: As described in Dawson et al. (2013) and Pˆaris et al. (2014), the fibers assigned to BOSS CORE and BONUS quasar targets (Ross et al. 2012) were offset in the focal plane to optimize throughput in the blue part of the spectrum, to better observe the Lymanα forest. Because the standard stars are not observed with this same offset, the spectrophotometric calibration of these quasar targets is systematically incorrect. The QSO STD flag denotes an additional sample of spectrophotometric standard stars, from 10 to 25 per plate spread evenly across the focal plane, that were drilled to follow the same offsets in the focal plane as the BOSS quasar targets. These objects are chosen using the same algorithm as for normal spectroscopic standard stars in BOSS, as explained in Dawson et al. (2013). Improved calibration gives improved measurements of quasar spectral energy distributions, important both for constraining quasar emission models, and for interpreting optical depth data in the Lymanα forest (Lee et al. 2015).  \nSpectra of H 2 O Maser Galaxies: One current route to the absolute calibration of the luminosities of Type Ia supernovae (SNeIa) as standard candles uses the 3%-accurate distance to NGC 4258 afforded by the well-studied H 2 O maser in its center (Humphreys et al. 2013). Further improvements, by identifying other maser galaxies with supernovae, could decrease the uncertainty on local measurements of the Hubble Constant (Riess et al. 2011). There is an apparent correlation between maser activity and host galaxy properties (Zhu et al. 2011); this correlation will  \nbe tested with spectroscopy of known maser host galaxies, and spectroscopy of SN Ia host galaxies will be used to identify plausible maser candidates. Targets, identified with the IAMASERS flag, were selected with no previous SDSS spectra and i model < 20 mag. Objects targeted in the Bright Galaxies ancillary program (Dawson et al. 2013) were also removed from the IAMASERS target list.  \nSpectroscopy of Massive Galaxy Cluster Members: This program aims to obtain redshifts of candidate member galaxies of X-ray selected clusters. The sources are optical counterparts to X-ray clusters selected as faint sources in the ROSAT All-Sky Survey (Voges et al. 1999, 2000b) identified by applying the redMaPPer (Rykoff et al. 2014) cluster finding algorithm to the position of an X-ray source. The X-ray magnitude limit corresponds roughly to the brightest 30% of clusters that the X-ray satellite eROSITA will find within the BOSS area.  \nObjects denoted by the target flag CLUSTER MEMBER are selected from the redMaPPer catalog with i cmodel < 19 . 9 mag and i fib2 < 21 . 5 mag. Roughly 1000 candidate clusters were observed.  \nRepeated Spectroscopy of Candidate Close Binary Massive Black Holes: Second-epoch spectroscopy was obtained for SDSS I/II quasars that are candidate massive black hole binaries with separations less than one parsec. The quasars were selected from the DR7 quasar catalog (Schneider et al. 2010) based on having double-peaked broad Balmer lines or significant velocity offsets between broad and narrow line centroids. The SDSS-III spectrum, identified by the DISKEMITTER REPEAT target class, is separated from the first SDSS-I/II epoch by multiple years and provides a test of binarity by observing changes in the emission line properties. These data should allow new constraints on the close massive black hole binary population in SDSS quasars and will provide a better understanding of the nature of these peculiar broad line profiles.  \nSpectroscopy of Hard X-ray Identified AGN: This sample, identified with the XMMSDSS target class, was designed to spectroscopically confirm hard (2-10 keV) X-ray selected AGN identified in the serendipitous XMM survey of SDSS (Georgakakis & Nandra 2011). These objects tend to lie at relatively low redshift, z < 0 . 8. Objects identified by the XMMSDSS target class were selected with f X (2-10 keV) > 4 × 10 -14 erg cm -2 s -1 and SDSS r model < 22 mag. There was of order one target per square degree. The cross-correlation measurement of those AGN with the SDSS and BOSS galaxy samples will constrain the dark matter halo masses of X-ray AGN as a function of redshift and luminosity.  \nWISE BOSS: BOSS spectra of Mid-IR bright AGN: Photometry from the WISE All-Sky Data Release catalog was used in combination with SDSS photometry to select a 12 µ m-flux-limited sample of quasars that goes beyond the main BOSS quasar sample (Ross et al. 2012). This allows studies of the completeness of the main quasar sample and an exploration of dust obscuration of quasars. The WISE BOSS QSO target class was selected as having i PSF < 20 . 2, W1 -W2 < 0 . 30, W1 < 2 . 0 + 0 . 667 g PSF , r PSF -W2 > 2 . 0, W2 < 18 . 5, W3 > 12 . 5, and, for extended objects, W3 > 10 . 3. The resulting sample peaks at redshift z ≈ 1 . 4.  \nQuasar Target Selection with WISE: This was a second sample of WISE-selected quasars, focused on the redshift range z > 2 . 15. Candidate quasars, identified with the QSO WISE FULL SKY target class, were identified from SDSS photometry using an artificial neural network as described in Y'eche et al. (2010). Point sources are assigned a photometric redshift estimate and a likelihood ( NN ) ranging from zero (stellar) to one (quasar). Objects with NN > 0 . 3 were considered targets if they were matched within 1 . 5 '' of a WISE source, had color i PSF -W1 > 2 . 0 + 0 . 8( g PSF -i PSF ) and i PSF -W2 > 3 . 0, and were brighter than g PSF = 21 . 5 mag. These color cuts were designed to identify high-redshift quasars, and indeed almost 3/4 of the candidates have redshifts above 2. Objects satisfying this cut were assigned the QSO WISE FULL SKY flag whether or not they were also targeted by the main BOSS quasar selection.  \nQuasar Pairs: Candidate quasar pairs separated by angles corresponding to less than a few hundred kpc were identified for spectroscopic confirmation. When combined with spectroscopy from other programs (e.g., Hennawi et al. 2006; Myers et al. 2008; Hennawi et al. 2010), this sample will provide a large statistical sample of quasar pairs necessary for small-scale clustering measurements. The target list consists of pairs of quasar targets selected using either the Kernel Density Estimation (KDE) method (Richards et al. 2009) or the XDQSOz method (Bovy et al. 2011). There are both low- and mid-redshift selection samples, both identified by the QSO XD KDE PAIR target flag.  \nThe low-redshift selection includes targets with g PSF < 20 . 85 mag and a matching target from the same selection within an angular separation, θ , of 1 '' < θ < 30 '' . Objects are selected based on being in the XDQSOz low-redshift selection range (0 < z < 2 . 2) with probability being a quasar, PQSO > 0 . 8; or in the KDE catalog with flags indicating that the object is at low redshift and/or has an ultraviolet excess ( lowzts = 1 or uvxts = 1 , as described in Table 2 of Richards et al. 2009).  \nThe mid-redshift selection includes XDQSOz targets with PQSO > 0 . 2 that have a pair (from the same mid-z selection) within 1 '' < θ < 20 '' . These targets are further culled to only retain pairs for which the product of the two XDQSOz probabilities for the pair integrated over 2 . 0 < z < 5 . 5 is PQSO 1 × PQSO 2 > 0 . 16.  \nFor both low- and mid-z selection, the following algorithm is implemented to clean the sample: (1) target all pairs where one or both of the objects in the pair are in a BOSS tiling overlap region; (2) for pairs where both objects are outside overlap regions, target the object with no existing spectrum; (3) for pairs where both objects are outside  \noverlap regions and neither have existing spectra, target the fainter object; (4) discard all pairs where both objects are outside overlap regions and one of the pair is already a BOSS target; (5) discard all pairs where either object is a spectroscopically confirmed star or is obviously an artifact on visual inspection of the image; and (6) discard all targets (not pairs) that have an existing spectroscopic confirmation.  \nTable 5 Parallel BOSS Ancillary Programs", 'A.2. Ancillary Programs with Dedicated Plates': "Because BOSS observations were proceeding ahead of schedule in 2012, a series of plates were added to the SDSS-III program to observe ancillary science programs. These plates do not have primary BOSS galaxy and quasar targets and instead consist entirely of ancillary science targets. The completeness of each dedicated sample is therefore typically higher than the completeness of the samples in the parallel ancillary programs. We describe each of these programs here, again sorted roughly by the distance of the targets. Table 6 summarizes the target categories, listed in order of ANCILLARY TARGET2 bit. Note that a number of the programs include multiple target classes, each indicated by a separate bit.  \nStar Formation in the Orion and Taurus Molecular Clouds: This program obtained spectra of candidate young stellar objects (YSO) in the Orion and Taurus molecular clouds. The data provide a census of YSO into the brown dwarf regime, a measurement of the initial mass function at low masses, and a characterization of circumstellar disks as a function of stellar mass, extending previous studies to fainter magnitudes, to be sensitive to very low luminosity, low mass objects. Objects were selected mostly from WISE photometry, as well as the Two Micron All Sky Survey (2MASS; Skrutskie et al. 2006) and Spitzer photometry (matching to SDSS imaging where available; Finkbeiner et al. 2004) in the Orion and Taurus regions. Objects were included to the detection limit of the WISE catalog, but those with W1 > 7 were removed to reduce contamination from luminous, very red, asymptotic giant branch stars. There are several target classes within this program, as detailed in Table 6.  \nThe five plates in this program were designed in a heterogeneous manner due to the different availability of SDSS imaging in each field and the variation in the relative number of IR-excess sources. The latter is primarily related to the age of each star formation complex, as the circumstellar disk fraction decreases with stellar age. When limited SDSS photometry is available in a field, gri magnitudes are derived from the PPMXL/USNO-B1 catalog following the inverse of the transformations tabulated in Monet et al. (2003).  \nThe 25 Ori spectroscopic plate targets WISE-detected stars within 1.5 degrees of the B3 star 25 Ori. It focuses on members of the young 25 Ori group and surrounding pre-main sequence stars in Orion and defines the target classes 25ORI WISE and 25ORI WISE W3 .  \nObjects were selected from the WISE catalog with detections in W1 and W3, with a magnitude limit of W3 < 11 . 65, and are assigned a target class of 25ORI WISE W3 . Sources were required to be fainter than 15 in g, r and i , and brighter than g = 22 and i = 21.  \nThe remaining three Orion plates covering the Kappa Ori, NGC 2023, and NGC 2068 star formation regions were created in an identical manner and define the target classes KOEKAP STAR , KOEKAPBSTAR , KOE2023 STAR , KOE2023BSTAR , KOE2068 STAR , and KOE2068BSTAR . For all three plates, objects in the * STAR class are infrared excess sources selected by W 1 -W 2 > 0 and a SNR in W1 greater than 10. The *BSTAR objects are other WISE detections within the field.  \nThe Taurus spectroscopic plate targets objects with Spitzer mid-infrared 8 and/or 24 micron excess within 1.5 degrees of the center of the Taurus Heiles 2 molecular cloud. Our sample for Taurus focuses on very low mass substellar objects with disks and edge-on disks which may have been mistaken for galaxies. The selection used IRAC1 -IRAC4 > 1 . 5 and/or IRAC1 -MIPS24 > 1 . 5 mag with SNR > 10 for IRAC1 and SNR > 7 for IRAC4 or MIPS24. Here IRAC1, IRAC4, and MIPS24 refer to Vega magnitudes measured through filters centered at 3.5, 8.0, and 24 microns on Spitzer . All science objects on the Taurus plate have a target class of TAU STAR .  \nStars Across the SDSS: This project aims to cross-calibrate the large spectral surveys which are giving us a detailed map of the different stellar populations in the Milky Way. Dedicated stellar spectroscopic surveys such as SEGUE (Yanny et al. 2009), the RAdial Velocity Experiment (RAVE; Steinmetz et al. 2006), APOGEE, the Gaia/European Southern Observatory Survey (GES; Gilmore et al. 2012) and the massive Gaia survey itself (de Bruijne 2012) provide kinematic information and chemical diagnostics for large samples of stars. In addition, there are over 250,000 BOSS spectra of stars (Table 1), mostly targeted as quasar candidates. Derived stellar parameters, such as effective temperatures, surface graft's, and metallicities must be robust and consistent between surveys to use them jointly to build a coherent picture of our Galaxy. Because each survey targets a particular magnitude range, one must be careful to minimize systematic errors in stellar parameters as a function of distance.  \nThis program obtained BOSS spectra of stars observed by the SEGUE-1 and SEGUE-2 surveys on eight plates (target classes SEGUE1 , SEGUE2 ), GES targets in eight plates (target class GES ), and one plate dedicated to stars from the COnvection, ROtation, and planetary Transit mission (CoRoT; Baglin et al. 2002) also observed by GES and APOGEE (target classes COROTGES , COROTGETAPOG ). As many CoRoT and GES stars were given fibers as possible, restricted only with the bright magnitude limit of i > 14 to avoid saturation in the spectrographs. There were not enough targets to fill all the fibers on the BOSS plates, particularly when the GES fields did not overlap with SEGUE-1 or SEGUE-2 plates, so the eight GES plates also targeted stars selected from the SDSS photometry ( SDSSFILLER ) with the following selection cuts to ensure good SNR and to avoid very cool stars for which it is more difficult to obtain accurate stellar parameters with the SSPP: 0 < g -r < 1 . 25 , g < 19 , i > 15 , r > 15. The CoRoT plate had targets chosen from APOGEE (target class APOGEE ) and 2MASS ( 2MASSFILL ) as well. Stars were targeted to sample the full parameter space of effective temperature, metallicity, and log g , as much as possible. The GES project (Milky Way survey) targeted stars with 0 < J -K < 0 . 7 , 12 . 5 < J < 17 . 5, with near-infrared photometry from the Visible and Infrared Survey Telescope for Astronomy (VISTA; Emerson et al. 2004). In total, the eight GES plates gave spectra with high enough SNR for acceptable SSPP parameters for 296 stars with -0 . 25 < g -r < 1 . 5 and 14 < g < 19.  \nA Galaxy Sample Free of Fiber Collisions: The finite size of the BOSS fiber ferrules means that no two fibers can be placed closer than 62 '' apart on a given plate. These 'fiber collisions' affect measurements of the small-scale clustering of galaxies from the CMASS and LOWZ samples. CMASS and LOWZ galaxies that were not observed in the main BOSS survey due to fiber collisions with other primary targets were added to ancillary target plates 6373-6398 (North Galactic Cap), 6780-6782 (on Stripe 82), 6369 and 6717. Fibers were also assigned to CMASS and LOWZ targets that suffered redshift failures ( ZWARNING NOQSO > 0; Bolton et al. 2012) in previous observations in the data reduction pipeline. These objects are identified with the CMASS or LOWZ target flags in the database; unlike all other objects discussed in this Appendix, they are not assigned a target class in ANCILLARY TARGET2 . This program significantly increases the completeness of these galaxy samples in the region covered by these plates and provides a useful dataset for testing the fiber-collision correction methods that are currently used in BOSS clustering analyses (e.g., Guo et al. 2012). A total of 1282 targets were included in this program. These data have been used in an analysis of velocity bias in close pairs of galaxies by Guo et al. (2015).  \nQuantifying BOSS Galaxy Incompleteness with a WISE-Selected Sample: The CMASS sample is designed to select red galaxies of high stellar mass ( M stellar > 10 11 M glyph[circledot] ). This program (target class WISE COMPLETE ) aimed to explore a broader range of galaxy colors in the CMASS redshift range (0 . 45 < z < 0 . 7), using optical-IR cuts by combining SDSS and WISE. The sample criteria are 17 . 5 < i < 19 . 9, ( r -W1) > 4 . 165, and i fib2 < 21 . 7 (the latter uncorrected for Galactic extinction). Various quality flag cuts were imposed to limit spurious sources. Stars were eliminated using the SDSS morphological classifications for blue objects and a color-color cut in ( r -i , r -W1) space for red objects. A random subsample of 90% of these objects were selected as targets to meet the required target sky density.  \nExploring z > 0 . 6 LRGs from SDSS and WISE: WISE and SDSS photometry was used to identify a sample of z > 0 . 6 luminous red galaxies, taking advantage of the fact that the 1.6 µ m bump in old stellar populations (due to a local minimum in the opacity of the H -ion) is redshifted into the WISE W1 band. This spectroscopic sample will be used to calibrate photometric redshifts in this range and to test target selection techniques for eBOSS.  \nTargets for this program were divided into a higher priority sample denoted HIZ LRG and a lower priority sample denoted LRG ROUND3 . All objects were required to have  \n( i model < 20 . 0 || z model < 20 . 0) && ( z fib2 < 21 . 7 || i fib2 < 22 . 0) .  \nObjects in the HIZ LRG sample were selected to have  \n( r -i ) > 0 . 98 && ( r -W1) > 2( r -i ) -0 . 5 .  \nThe LRG ROUND3 sample used the same r -W1 cut, but the ( r -i ) color cut was bluer, ( r -i ) > 0 . 85, in order to explore a broader range of galaxy colors.  \nTests of eBOSS Target Selection in CFHTLS W3 Field: As a test of target selection algorithms to be used in eBOSS, six plates were dedicated to a selection of LRG and quasars at high density over a region of sky overlapping", 'SDSS DR12': 'A.3.5. Other Target Classes in SEQUELS  \nGalaxies from the main BOSS target selection, both LOWZ and CMASS, that were not assigned fibers due to fiber collisions were observed in SEQUELS and given the target class SEQUELS COLLIDED . Observing these galaxies in SEQUELS creates large contiguous areas that have 100% spectroscopic completeness in the final BOSS data sample. A similar sample was described in Section A.2.  \nVariable targets selected from the PTF survey are targeted with the SEQUELS PTF VAR target class in three classes: hosts of supernovae detected in the PTF supernova program, RR Lyrae stars, and additional sources whose light-curve built from PTF data show variations by 0.4 magnitude or more.  \nEmission-line galaxy candidates tend to have blue colors and thus are relatively bright in the u band. The South Galactic Cap U-band Sky Survey 154 (SCUSS) was carried out over the SEQUELS area using the 2.3-m Bok Telescope at Kitt Peak to obtain deeper data ( u ≈ 23 for 5 σ detections of point sources) than SDSS (X. Zhou et al. 2015, in preparation; H. Zou et al. 2015, in preparation). We used these data together with SDSS g, r, i photometry to select ELGs in the redshift range 0 . 4 < z < 1 . 6 in a region of the sky of 25.7 deg 2 around ( α, δ ) ∼ (23 · , 20 · ).  \nThe brightest and bluest galaxy population ( SEQUELS ELG ) is selected by  \n-0 . 5 < u -r < 0 . 7( g -i ) + 0 . 1 && 20 < u < 22 . 5 (A1)  \nTo fill the remaining fibers we also observed targets satisfying broader color cuts ( SEQUELS ELG LOWP ):  \n(20 < u < 22 . 7 && -0 . 9 < u -r ) && ( u -r < 0 . 7( g -i ) + 0 . 2 || u -r < 0 . 7) (A2)  \nTable 7 SEQUELS Targets', 'A.3. The Sloan Extended Quasar, ELG, and LRG Survey (SEQUELS)': "SEQUELS serves both as a pilot program for the eBOSS survey of SDSS-IV and as a stand-alone science program within SDSS-III. SEQUELS also encompasses two SDSS-IV sub-programs to obtain spectra of variability-selected objects and X-ray detected objects, which are pilot studies for the TDSS and SPIDERS programs within eBOSS described in Section 7.  \nThe main SEQUELS footprint lies in the North Galactic Cap. Targets were selected over the region covering 120 · < α < 210 · and 45 · < δ < 60 · within the nominal BOSS footprint, but only 300 deg 2 of this area were observed. The targets in the primary SEQUELS program have the SEQUELS TARGET bit set in the ANCILLARY TARGET2 bitmask. Plates that were drilled but not observed before DR12 will be observed as part of eBOSS.  \nSEQUELS targets fell into four broad categories, which we describe in detail below: (1) luminous red galaxies (LRG), designed to extend the BOSS CMASS redshift coverage, yielding a median redshift of ∼ 0 . 72; (2) quasars both as direct tracers of the cosmic density field at redshifts 0 . 9 < z < 2 . 2, and as probes of the Lymanα forest; (3) X-ray targets as a SPIDERS precursor, and (4) variability-selected targets as a TDSS precursor. Several other target classes don't fall neatly into any of these categories and are listed at the end.  \nIn addition, SEQUELS incorporated a pilot program to identify high-redshift ELGs. The ELG targets are listed with the ANCILLARY TARGET2 bitmask (Table 6). The bitmasks for all other SEQUELS programs are listed in Table 7 and are described in detail in what follows. Note that some of these bits (such as bit 0, DO NOT OBSERVE ) don't indicate programs per se, but rather give information about the target selection process.", 'A.3.1. LRGs in SEQUELS': 'Target selection of LRGs in SEQUELS was designed to target massive red galaxies at z glyph[greaterorsimilar] 0 . 6, using a combination of SDSS imaging and WISE photometry. The SDSS photometry (all model magnitudes corrected for Milky Way extinction) uses a new set of calibrations using a combination of PanSTARRS-1 (Kaiser et al. 2010) and SDSS stellar photometry (Finkbeiner et al. 2014). The residual systematics are reduced from 1% in griz (Padmanabhan et al. 2008) to 0.9, 0.7, 0.7 and 0.8% in the griz bands, respectively. In addition, some poorly-constrained zero-points with errors exceeding 3% in the DR9 data are now significantly improved. This new photometry will be included in a future data  \nrelease. The WISE photometry (now converted to the AB system) is forced photometry on SDSS positions (Lang et al. 2014).  \nThere are two target classes focused on LRG; roughly 1/3 of the LRG objects are targeted by both. Both classes are magnitude limited to z < 19 . 95 and i > 19 . 9. The bright limit ensures that there is no overlap with the BOSS CMASS selection. Objects flagged LRG IZW in the SEQUELS bitmask satisfy the color cuts ( i -z ) > 0 . 7 and ( i -W1) > 2 . 143( i -z ) -0 . 2. Objects flagged LRG RIW satisfy ( r -i ) > 0 . 98 , ( r -W1) > 2( r -i ), and ( i -z ) > 0 . 625; the latter cut pushes the sample to higher redshift.', 'A.3.2. Quasars in SEQUELS': "The main sample of SEQUELS quasars is assigned the QSO EBOSS CORE target class and is designed to meet the eBOSS sky density goal of ∼ 70 0 . 9 < z < 2 . 2 quasars deg -2 . The target selection makes no attempt to filter out higher-redshift quasars, so objects from this sample will also be useful for Lymanα forest studies. Quasars in the CORE are selected by a combination of XDQSOz (Bovy et al. 2012) in the optical and a WISE-optical color cut, as detailed in A. Myers et al. (2015, in preparation); see also the description of bit 1 and 2 of the QSO EBOSS W3 ADM target class above. This sample (and all the SEQUELS quasar candidates which follow, unless otherwise indicated) are restricted to objects classified as point sources, with faint-end magnitude cuts of g < 22 or r < 22.  \nWe also selected quasars via their variability as measured by the PTF; these are given the target class QSO PTF . This sample is less uniformly selected, given the availability of multi-epoch PTF imaging, but that is acceptable for Lymanα forest studies. These objects are limited in magnitude to r > 19 and g < 22 . 5.  \nTargets that have the QSO EBOSS KDE bit set in SEQUELS consisted of all objects from the KDE catalog of Richards et al. (2009) that had uvxts=1 set (indicating that they had a UV excess, and thus were likely to be at z glyph[lessorsimilar] 2 . 2) within that catalog. Only KDE objects that matched to a point source in the DR9 or the custom SDSS photometry used to select SEQUELS targets were included.  \nThe QSO EBOSS FIRST bit indicates quasars that are targeted in SEQUELS because there is an SDSS source within 1 '' of a source in the 2013 June 05 version 153 of the FIRST point source catalog (Becker et al. 1995).  \nAn object is flagged QSO BOSS TARGET if it has been previously observed by BOSS and does not have either LITTLE COVERAGE or UNPLUGGED set in the ZWARNING bitmask (see Table 3 of Bolton et al. 2012). Similarly, an object from SDSS DR8 is flagged QSO SDSS TARGET if it is included in the SDSS DR8 spectroscopic database, and similarly has neither of those flags set in ZWARNING .  \nWe separately flagged those quasars with QSO KNOWN whose spectra had been visually confirmed, as listed in the SDSS sample used to define known objects in BOSS (see Ross et al. 2012), and a preliminary version of the DR12 BOSS quasar catalog of I. Paris et. al. (2015, in preparation).  \nAs part of SEQUELS, we also re-observed a number of high-redshift ( z > 2 . 15) quasars that had low SNR spectroscopy in SDSS DR7 or BOSS, to improve the measurement of the Ly α forest.  \nObjects flagged QSO REOBS had 0 . 75 ≤ SNR / pixel < 3 in BOSS. This target class also included objects which have a high probability of being quasars based on their photometry, but had no signal in the BOSS spectra because of dropped fibers or other problems.  \nIn the same spirit, BOSS spectra of some objects are of low enough quality that their classification as quasars, or measurements of their redshifts, are uncertain upon visual inspection. Such objects are designated as QSO? or QSO Z? in the DR12 quasar catalog (I. Paris et. al. 2015, in preparation). Those objects in the SEQUELS footprint are reobserved, and given the QSO BAD BOSS target class. A preliminary, but close-to-final version of the DR12 catalog was used to define this sample for SEQUELS.  \nWe set a flag bit, DO NOT OBSERVE , to indicate which previously observed quasars should not be re-observed, even if they were selected by one of the SEQUELS algorithms. It is determined by the following combination of target flags:", '( QSO KNOWN || QSO BOSS TARGET || QSO SDSS TARGET ) && !( QSO BAD BOSS || QSO REOBS ) .': "SEQUELS targeted quasars were selected in both the DR9 imaging used for BOSS and an updated DR12 imaging calibration intended for use in eBOSS targeting. The DR9 CALIB TARGET bit signifies quasars that were selected for SEQUELS using the DR9 imaging calibrations instead of (or as well as) the updated DR12 imaging.  \nA.3.3. SPIDERS targets within the SEQUELS program  \nThe goal of the SPIDERS program within eBOSS is to obtain SDSS spectroscopy for large samples of X-ray selected AGN and member galaxies of X-ray selected clusters. Two SPIDERS pilot programs were executed within SEQUELS using pre-eROSITA X-ray survey data.  \nSPIDERS RASS AGN targets are candidate AGN detected in the ROSAT All Sky Survey (RASS). A parent sample of X-ray sources was formed from the concatenation of all Bright and Faint RASS catalogue (Voges et al. 1999, 2000a) detections lying within the SEQUELS footprint. Given the large RASS positional uncertainties, we determine the most probable optical counterpart for each RASS source using a novel Bayesian algorithm (M. Salvato et al, in preparation), an extension of the method introduced by Budav'ari & Szalay (2008) applied to all SDSS photometric  \nobjects with 17 < r < 22 within 1 ' of each RASS detection. The algorithm uses the positional offset between each possible association, the positional errors, and the colors of the sources, given priors from a sample of previously matched XMM-Newton sources (Georgakakis & Nandra 2011). Identified sources which already had SDSS/BOSS spectra, were associated with objects in the V'eron-Cetty & V'eron (2010) catalogue of known AGN, or were associated with bright stars from the Tycho-II catalogue (Høg et al. 2000), were removed.  \nObjects of type SPIDERS RASS CLUS are selected from the RedMapper catalogue (Rykoff et al. 2014) of cluster members with 17 . 0 < i fiber2 < 21 . 0 that lie in the SEQUELS footprint. A prioritization scheme penalizes lower richness clusters and favors highly-ranked members in the photometric red sequences. We also targeted 22 clusters selected in XMM-Newton observations by the XCLASS-RedMapper survey (Sadibekova et al. 2014; Clerc et al. 2012) with richness (i.e., number of candidate members) greater than 20. The high-quality XMM-Newton data allows more detailed characterization of the cluster mass once the spectroscopic redshift is known (via, e.g., derivation of intracluster gas temperatures). Moreover, the identification of these objects as clusters is unambiguous given their X-ray data, so no cut is made on optical richness.  \nA.3.4. TDSS targets within the SEQUELS program  \nThe TDSS program targeted variable objects matched between imaging in both Pan-STARRS1 and SDSS. There are two classes of TDSS targets: single-epoch spectroscopy (SES) and few-epoch spectroscopy (FES).  \nSingle-epoch spectroscopy : These targets comprise the main body of TDSS targets and are flagged with target class TDSS A .  \nWe match SDSS point sources with 16 < i psf < 21 to the PS1 'uberCal' database of 2013 September, restricting to objects with more than 10 detections across the PS1 griz bands. We also eliminate sources with a g < 22 neighbor within 5 '' or an i < 12 neighbor within 30 '' to avoid problems with deblending issues.  \nTo identify variables within this subsample, we use a three-dimensional Kernel Density Estimator. We train our algorithm on known variables, using the Stripe 82 variable catalog from Ivezi'c et al. (2007) and require that the amplitude of variation in the g , r and i bands be greater than 0.1. Our catalog of non-variables is taken from the Ivezi'c et al. (2007) standards catalog. We improve the purity of the latter catalog by requiring that our non-variables have at least eight SDSS observations in Stripe 82 and a reduced χ 2 relative to a model of no variability of less than 2 in the g , r and i bands. We require that variables, standards and candidates have SDSS and PS1 magnitude errors of less than 0.1 and at least two PS1 detections in three of the four bands in common between PS1 and SDSS bands ( g , r , i and z ).  \nAcross the 3-4 qualified bands (as described above), we use the median PS1-SDSS magnitude difference (corrected photometrically so that it is 0 for a typical star), median PS1-only variability (essentially the variance minus the average error squared) and median SDSS magnitude as the three dimensions of our KDE. We bin and convolve both our variable and standard population within this space and define 'efficiency' as the fraction of variables divided by the fraction of standards in every region of that space. We then use the PS1-SDSS difference, PS1 variability and median magnitude to assign an efficiency to every source in our sample.  \nWe limit ourselves to sources with SDSS i fib2 < 21, and fainter than 17 in u, g and r fiber magnitudes. This removes potentially saturated sources. We also remove targets that already have SDSS or BOSS spectroscopy.  \nFew-epoch spectroscopy : These target bits represent FES programs that explicitly seek repeat spectra for objects of interest in order to monitor spectroscopic variability. The TDSS FES program targets are:  \nTDSS FES DE : Quasar disk emitters. These targets are quasars with i < 18 . 9 and broad, double-peaked or asymmetric Balmer emission line profiles, such as those in Strateva et al. (2006) ( z < 0 . 33 for H α and H β ) and higher-redshift analogs from Luo et al. (2013) ( z ∼ 0 . 6 for H β and MgII). This program seeks to characterize the variability of the broad emission line profiles, especially changes in asymmetry and velocity profiles, for comparison to models of accretion disk emission in the presence of asymmetries and/or perturbations.  \nTDSS FES DWARFC : Dwarf carbon stars (dCs). Most targets were chosen from the compilation of Green (2013) from SDSS spectroscopy. Objects were required to have significant (more than 3 σ ) proper motion ( ≈ 15 mas/yr) between the Palomar Observatory Sky Survey and SDSS photometry, ensuring that they are nearby, and thus likely to be dwarf stars. Observations of radial velocity variations will identify binaries, thus testing the hypothesis that these stars became carbon-rich due to mass transfer from an asymptotic branch star via either wind accretion or Roche lobe overflow.  \nTDSS FES NQHISN : This program targets z < 0 . 8 DR7 quasars with high SNR spectra to study broad-line variability on multi-year timescales.  \nTDSS FES MGII : This program targets quasars that showed evidence for temporal velocity shifts in the MgII broad emission lines in previous repeat SDSS spectroscopy (Ju et al. 2013) in order to look for evidence of super-massive black hole binaries.  \nTDSS FES VARBAL : These objects are selected from the Gibson et al. (2008) broad absorption line quasar catalog, to look for variability in the absorption troughs. Further description of this program can be found in Filiz Ak et al. (2012, 2013)."}