Draft version September 26, 2017Typeset using LATEX style emulateapj v. 01/23/15

THE THIRTEENTH DATA RELEASE OF THE SLOAN DIGITAL SKY SURVEY: FIRST SPECTROSCOPICDATA FROM THE SDSS-IV SURVEY MAPPING NEARBY GALAXIES AT APACHE POINT OBSERVATORY

Franco D. Albareti1,2,3, Carlos Allende Prieto4,5, Andres Almeida6, Friedrich Anders7, Scott Anderson8,Brett H. Andrews9, Alfonso Aragón-Salamanca10, Maria Argudo-Fernández11,12, Eric Armengaud13,

Eric Aubourg14, Vladimir Avila-Reese15, Carles Badenes9, Stephen Bailey16, Beatriz Barbuy17,18,Kat Barger19, Jorge Barrera-Ballesteros20, Curtis Bartosz8, Sarbani Basu21, Dominic Bates22,

Giuseppina Battaglia4,5, Falk Baumgarten7,23, Julien Baur13, Julian Bautista24, Timothy C. Beers25,Francesco Belfiore26,27, Matthew Bershady28, Sara Bertran de Lis4,5, Jonathan C. Bird29,

Dmitry Bizyaev30,31,32, Guillermo A. Blanc33,34,35, Michael Blanton36, Michael Blomqvist37, Adam S. Bolton38,24,J. Borissova39,40, Jo Bovy41,42, William Nielsen Brandt43,44,45, Jonathan Brinkmann30, Joel R. Brownstein24,Kevin Bundy46, Etienne Burtin13, Nicolás G. Busca14, Hugo Orlando Camacho Chavez47,18, M. Cano D́ıaz15,

Michele Cappellari48, Ricardo Carrera4,5, Yanping Chen49, Brian Cherinka20, Edmond Cheung46,Cristina Chiappini7, Drew Chojnowski31, Chia-Hsun Chuang7, Haeun Chung50, Rafael Fernando Cirolini51,18,

Nicolas Clerc52, Roger E. Cohen53, Julia M. Comerford54, Johan Comparat1,2, Janaina Correa doNascimento55,18, Marie-Claude Cousinou56, Kevin Covey57, Jeffrey D. Crane33, Rupert Croft58, Katia Cunha59,

Jeremy Darling54, James W. Davidson Jr.60, Kyle Dawson24, Luiz Da Costa18,59, Gabriele Da Silva Ilha51,18,Alice Deconto Machado51,18, Timothée Delubac61, Nathan De Lee62,29, Axel De la Macorra15,

Sylvain De la Torre37, Aleksandar M. Diamond-Stanic28, John Donor19, Juan Jose Downes15,63, Niv Drory64,Cheng Du65, Hélion Du Mas des Bourboux13, Tom Dwelly52, Garrett Ebelke60, Arthur Eigenbrot28,

Daniel J. Eisenstein66, Yvonne P. Elsworth67,68, Eric Emsellem69,70, Michael Eracleous43,44,Stephanie Escoffier56, Michael L. Evans8, Jesús Falcón-Barroso4,5, Xiaohui Fan71, Ginevra Favole1,2,

Emma Fernandez-Alvar15, J. G. Fernandez-Trincado72, Diane Feuillet31, Scott W. Fleming73,74,Andreu Font-Ribera16,46, Gordon Freischlad30,31, Peter Frinchaboy19, Hai Fu75, Yang Gao (高扬)12,

Rafael A. Garcia76, R. Garcia-Dias4,5, D. A. Garcia-Hernández4,5, Ana E. Garcia Pérez4,5, Patrick Gaulme30,31,Junqiang Ge77, Douglas Geisler53, Bruce Gillespie20, Hector Gil Marin78,79, Léo Girardi80,18,

Daniel Goddard81, Yilen Gomez Maqueo Chew15, Violeta Gonzalez-Perez81, Kathleen Grabowski30,31,Paul Green66, Catherine J. Grier43,44, Thomas Grier19,82, Hong Guo12, Julien Guy83, Alex Hagen43,44,

Matt Hall60, Paul Harding84, R. E. Harley57, Sten Hasselquist31, Suzanne Hawley8, Christian R. Hayes60,Fred Hearty43, Saskia Hekker85, Hector Hernandez Toledo15, Shirley Ho 58,16, David W. Hogg36,

Kelly Holley-Bockelmann29, Jon A. Holtzman31, Parker H. Holzer24, Jian Hu (胡剑)64, Daniel Huber86,87,68,Timothy Alan Hutchinson24, Ho Seong Hwang50, Héctor J. Ibarra-Medel15, Inese I. Ivans24, KeShawn Ivory19,88,

Kurt Jaehnig28, Trey W. Jensen24, Jennifer A. Johnson89,90, Amy Jones91, Eric Jullo37, T. Kallinger92,Karen Kinemuchi30,31, David Kirkby93, Mark Klaene30, Jean-Paul Kneib61, Juna A. Kollmeier33, Ivan Lacerna94,

Richard R. Lane94, Dustin Lang42, Pierre Laurent13, David R. Law73, Alexie Leauthaud46,Jean-Marc Le Goff13, Chen Li12, Cheng Li12,65, Niu Li65, Ran Li77, Fu-Heng Liang (梁赋珩)64, Yu Liang65,Marcos Lima47,18, Lihwai Lin (林俐暉)94, Lin Lin (林琳)12, Yen-Ting Lin (林彥廷)94, Chao Liu77, Dan Long30,

Sara Lucatello80, Nicholas MacDonald8, Chelsea L. MacLeod66, J. Ted Mackereth96, Suvrath Mahadevan43,Marcio Antonio Geimba Maia18,59, Roberto Maiolino26,27, Steven R. Majewski60, Olena Malanushenko30,31,

Viktor Malanushenko30,31, Ńıcolas Dullius Mallmann55,18, Arturo Manchado4,5,97, Claudia Maraston81,Rui Marques-Chaves4,5, Inma Martinez Valpuesta4,5, Karen L. Masters81, Savita Mathur98, Ian D. McGreer71,

Andrea Merloni52, Michael R. Merrifield10, Szabolcs Meszáros99, Andres Meza100, Andrea Miglio67,Ivan Minchev7, Karan Molaverdikhani54,101, Antonio D. Montero-Dorta24, Benoit Mosser102, Demitri Muna89,90,

Adam Myers103, Preethi Nair104, Kirpal Nandra52, Melissa Ness101, Jeffrey A. Newman9, Robert C. Nichol81,David L. Nidever71, Christian Nitschelm11, Julia O’Connell19, Audrey Oravetz30,31, Daniel J. Oravetz30,31,

Zachary Pace28, Nelson Padilla94, Nathalie Palanque-Delabrouille13, Kaike Pan30,31, John Parejko8,Isabelle Paris37, Changbom Park50, John A. Peacock105, Sebastien Peirani106,46, Marcos Pellejero-Ibanez4,5,

Samantha Penny81, Will J. Percival81, Jeffrey W. Percival28, Ismael Perez-Fournon4,5, Patrick Petitjean106,Matthew Pieri37, Marc H. Pinsonneault89, Alice Pisani56,106, Francisco Prada1,2,107, Abhishek Prakash9,

Natalie Price-Jones42, M. Jordan Raddick20, Mubdi Rahman20, Anand Raichoor13,Sandro Barboza Rembold51,18, A. M. Reyna57, James Rich13, Hannah Richstein19, Jethro Ridl52,

Rogemar A. Riffel51,18, Rogério Riffel55,18, Hans-Walter Rix101, Annie C. Robin72, Constance M. Rockosi108,Sergio Rodŕıguez-Torres1, Tháıse S. Rodrigues80,109,18, Natalie Roe16, A. Roman Lopes110,

Carlos Román-Zúñiga111, Ashley J. Ross90, Graziano Rossi112, John Ruan8, Rossana Ruggeri81,Jessie C. Runnoe43,44, Salvador Salazar-Albornoz52, Mara Salvato52, Sebastian F. Sanchez15,

Ariel G. Sanchez52, José R. Sanchez-Gallego8, Baśılio Xavier Santiago55,18, Ricardo Schiavon96,Jaderson S. Schimoia55,18, Eddie Schlafly16,113, David J. Schlegel16, Donald P. Schneider43,44,

Ralph Schönrich48, Mathias Schultheis114, Axel Schwope7, Hee-Jong Seo115, Aldo Serenelli116,Branimir Sesar101, Zhengyi Shao12, Matthew Shetrone117, Michael Shull54, Victor Silva Aguirre68,

M. F. Skrutskie60, Anže Slosar118, Michael Smith28, Verne V. Smith119, Jennifer Sobeck60, Garrett Somers89,29,Diogo Souto59, David V. Stark46, Keivan G. Stassun29, Matthias Steinmetz7, Dennis Stello86,120,

Thaisa Storchi Bergmann55,18, Michael A. Strauss121, Alina Streblyanska4,5, Guy S. Stringfellow54,Genaro Suarez111, Jing Sun19, Manuchehr Taghizadeh-Popp20, Baitian Tang53, Charling Tao65,56, Jamie Tayar89,

Mita Tembe60, Daniel Thomas81, Jeremy Tinker36, Rita Tojeiro22, Christy Tremonti28, Nicholas Troup60,Jonathan R. Trump43,113, Eduardo Unda-Sanzana11, O. Valenzuela15, Remco Van den Bosch101,

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2 SDSS Collaboration

Mariana Vargas-Magaña122, Jose Alberto Vazquez118, Sandro Villanova53, M. Vivek24, Nicole Vogt31,David Wake123,124, Rene Walterbos31, Yuting Wang77,81, Enci Wang12, Benjamin Alan Weaver36,

Anne-Marie Weijmans22, David H. Weinberg89,90, Kyle B. Westfall81, David G. Whelan125, Eric Wilcots28,Vivienne Wild22, Rob A. Williams96, John Wilson60, W. M. Wood-Vasey9, Dominika Wylezalek20, Ting Xiao (肖

婷)12, Renbin Yan126, Meng Yang65, Jason E. Ybarra111,127, Christophe Yeche13, Fang-Ting Yuan12,Nadia Zakamska20, Olga Zamora4,5, Gail Zasowski20, Kai Zhang126, Cheng Zhao65, Gong-Bo Zhao77,81,

Zheng Zheng77, Zheng Zheng24, Zhi-Min Zhou77, Guangtun Zhu20,113, Joel C. Zinn89, Hu Zou77

Draft version September 26, 2017

Abstract

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) began observations in July 2014.It pursues three core programs: the Apache Point Observatory Galactic Evolution Experiment 2(APOGEE-2), Mapping Nearby Galaxies at APO (MaNGA), and the Extended Baryon OscillationSpectroscopic Survey (eBOSS). As well as its core program, eBOSS contains two major subprograms:the Time Domain Spectroscopic Survey (TDSS) and the SPectroscopic IDentification of ERositaSources (SPIDERS). This paper describes the first data release from SDSS-IV, Data Release 13(DR13). DR13 makes publicly available the first 1390 spatially resolved integral field unit obser-vations of nearby galaxies from MaNGA. It includes new observations from eBOSS, completing theSloan Extended QUasar, Emission-line galaxy, Luminous red galaxy Survey (SEQUELS), which alsotargeted variability-selected objects and X-ray selected objects. DR13 includes new reductions of theSDSS-III BOSS data, improving the spectrophotometric calibration and redshift classification, andnew reductions of the SDSS-III APOGEE-1 data, improving stellar parameters for dwarf stars andcooler stars. DR13 provides more robust and precise photometric calibrations. Value-added targetcatalogs relevant for eBOSS, TDSS, and SPIDERS and an updated red-clump catalog for APOGEEare also available. This paper describes the location and format of the data and provides referencesto important technical papers. The SDSS website, www.sdss.org, provides links to the data, tutori-als, examples of data access, and extensive documentation of the reduction and analysis procedures.DR13 is the first of a scheduled set that will contain new data and analyses from the planned ∼ 6-yearoperations of SDSS-IV.Subject headings: Atlases — Catalogs — Surveys

1 Instituto de F́ısica Teórica, (UAM/CSIC), UniversidadAutónoma de Madrid, Cantoblanco, E-28049 Madrid, Spain

2 Campus of International Excellence UAM+CSIC, Canto-blanco, E-28049 Madrid, Spain

3 la Caixa-Severo Ochoa Scholar4 Instituto de Astrof́ısica de Canarias, E-38205 La Laguna,

Tenerife, Spain5 Departamento de Astrof́ısica, Universidad de La Laguna

(ULL), E-38206 La Laguna, Tenerife, Spain6 Instituto de Investigación Multidisciplinario en Ciencia

y Tecnoloǵıa, Universidad de La Serena, Benavente 980, LaSerena, Chile

7 Leibniz-Institut fuer Astrophysik Potsdam (AIP), An derSternwarte 16, D-14482 Potsdam, Germany

8 Department of Astronomy, University of Washington, Box351580, Seattle, WA 98195, USA

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

10 School of Physics and Astronomy, University of Notting-ham, University Park, Nottingham, NG7 2RD, UK

11 Unidad de Astronomı́a, Universidad de Antofagasta,Avenida Angamos 601, Antofagasta 1270300, Chile

12 Shanghai Astronomical Observatory, Chinese Academy ofScience, 80 Nandan Road, Shanghai 200030, P. R. China

13 IRFU, CEA, Centre d’Etudes Saclay, 91191 Gif-Sur-YvetteCedex, France

14 APC, University of Paris Diderot, CNRS/IN2P3,CEA/IRFU, Observatoire de Paris, Sorbonne Paris Cite,France

15 Instituto de Astronomı́a, Universidad Nacional Autónomade México, Apartado Postal 70-264, México D.F., 04510 Mexico

16 Lawrence Berkeley National Laboratory, 1 Cyclotron Road,Berkeley, CA 94720, USA

17 Universidade de São Paulo, IAG, Rua do Matão 1226, SãoPaulo 05508-900, Brazil

18 Laboratório Interinstitucional de e-Astronomia - LIneA,Rua Gal. José Cristino 77, Rio de Janeiro, RJ - 20921-400,Brazil

19 Department of Physics & Astronomy, Texas Christian

University, Fort Worth, TX 76129, USA20 Center for Astrophysical Sciences, Department of Physics

and Astronomy, Johns Hopkins University, 3400 North CharlesStreet, Baltimore, MD 21218, USA

21 Department of Astronomy, Yale University, 52 HillhouseAvenue, New Haven, CT 06511, USA

22 School of Physics and Astronomy, University of St An-drews, North Haugh, St Andrews KY16 9SS, UK

23 Humboldt-Universitat zu Berlin, Institut für Physik,Newtonstrasse 15,D-12589, Berlin, Germany

24 Department of Physics and Astronomy, University of Utah,115 S. 1400 E., Salt Lake City, UT 84112, USA

25 Department of Physics and JINA Center for the Evolutionof the Elements, University of Notre Dame, Notre Dame, IN46556, USA

26 Cavendish Laboratory, University of Cambridge, 19 J. J.Thomson Avenue, Cambridge CB3 0HE, UK

27 University of Cambridge, Kavli Institute for Cosmology,Cambridge, CB3 0HE, UK

28 Department of Astronomy, University of Wisconsin-Madison, 475 N. Charter St., Madison WI 53706, USA

29 Department of Physics and Astronomy, Vanderbilt Univer-sity, 6301 Stevenson Center, Nashville, TN, 37235, USA

30 Apache Point Observatory, P.O. Box 59, Sunspot, NM88349, USA

31 Department of Astronomy, New Mexico State University,Las Cruces, NM 88003, USA

32 Sternberg Astronomical Institute, Moscow State Univer-sity, Moscow 119992, Russia

33 Observatories of the Carnegie Institution for Science, 813Santa Barbara St, Pasadena, CA, 91101, USA

34 Departamento de Astronomı́a, Universidad de Chile,Camino del Observatorio 1515, Las Condes, Santiago, Chile

35 Centro de Astrof́ısica y Tecnoloǵıas Afines (CATA),Camino del Observatorio 1515, Las Condes, Santiago, Chile

36 Center for Cosmology and Particle Physics, Department ofPhysics, New York University, New York, NY 10003, USA

37 Aix Marseille Université, CNRS, LAM (Laboratoire

Data Release 13 3

d’Astrophysique de Marseille) UMR 7326, F-13388, Marseille,France

38 National Optical Astronomy Observatory, 950 N CherryAve, Tucson, AZ 85719, USA

39 Instituto de F́ısica y Astronomı́a, Universidad de Val-paráıso, Av. Gran Bretaña 1111, Playa, Ancha, Casilla 5030,Chile

40 Millennium Institute of Astrophysics (MAS),MonseñorSotero Sanz 100, oficina 104, Providencia, Santiago, Chile

41 Department of Astronomy and Astrophysics, University ofToronto, 50 St. George Street, Toronto, ON, M55 3H4, Canada

42 Dunlap Institute for Astronomy & Astrophysics, Universityof Toronto, 50 St. George Street, Toronto, Ontario, M5S 3H4,Canada

43 Department of Astronomy and Astrophysics, Eberly Col-lege of Science, The Pennsylvania State University, 525 DaveyLaboratory, University Park, PA 16802, USA

44 Institute for Gravitation and the Cosmos, PennsylvaniaState University, University Park, PA 16802, USA

45 Department of Physics, The Pennsylvania State University,University Park, PA 16802, USA

46 Kavli IPMU (WPI), UTIAS, The University of Tokyo,Kashiwa, Chiba 277-8583, Japan

47 Departamento de F́ısica Matemática, Instituto de F́ısica,Universidade de São Paulo, CP 66318, CEP 05314-970, SãoPaulo, SP, Brazil

48 Sub-department of Astrophysics, Department of Physics,University of Oxford, Denys Wilkinson Building, Keble Road,Oxford OX1 3RH, UK

49 New York University Abu Dhabi, P.O. Box 129188, AbuDhabi, United Arab Emirates

50 Korea Institute for Advanced Study, 85 Hoegiro,Dongdaemun-gu, Seoul 02455, Republic of Korea

51 Departamento de F́ısica, Centro de Ciências Naturais eExatas, Universidade Federal de Santa Maria, 97105-900, SantaMaria, RS, Brazil

52 Max-Planck-Institut für extraterrestrische Physik, Postfach1312, Giessenbachstraße, 85741 Garching, Germany.

53 Departamento de Astronomı́a, Universidad de Concepción,Casilla 160-C, Concepción, Chile

54 Center for Astrophysics and Space Astronomy, Departmentof Astrophysical and Planetary Sciences, University of Colorado,389 UCB, Boulder, CO 80309, USA

55 Instituto de F́ısica, Universidade Federal do Rio Grande doSul, Campus do Vale, Porto Alegre, RS, , 91501-907, Brazil

56 Aix Marseille Université, CNRS/IN2P3, Centre dePhysique des Particules de Marseille, UMR 7346, 13288, Mar-seille, France

57 Department of Physics and Astronomy, Western Washing-ton University, 516 High Street, Bellingham, WA 98225, USA

58 Department of Physics and McWilliams Center for Cos-mology, Carnegie Mellon University, 5000 Forbes Avenue,Pittsburgh, PA 15213, USA

59 Observatório Nacional, 77 Rua General José Cristino, Riode Janeiro, 20921-400, Brazil

60 Department of Astronomy, University of Virginia, Char-lottesville, VA 22904-4325, USA

61 Laboratoire d’Astrophysique, École Polytechnique Fédéralede Lausanne, 1015 Lausanne, Switzerland

62 Department of Physics, Geology, and Engineering Tech,Northern Kentucky University, Highland Heights, KY 41099,USA

63 Centro de Investigaciones de Astronomı́a, AP 264, Mérida5101-A, Venezuela

64 McDonald Observatory, The University of Texas at Austin,1 University Station, Austin, TX 78712, USA

65 Tsinghua Center for Astrophysics and Department ofPhysics, Tsinghua University, Beijing 100084, P. R. China

66 Harvard-Smithsonian Center for Astrophysics, 60 GardenSt., MS 20, Cambridge, MA 02138, USA

67 School of Physics and Astronomy, University of Birming-ham, Edgbaston, Birmingham B15 2TT, UK

68 Stellar Astrophysics Centre, Department of Physics andAstronomy, Aarhus University, Ny Munkegade 120, DK-8000Aarhus C, Denmark

69 European Southern Observatory, Karl-Schwarzschild-Str.2, 85748 Garching, Germany

70 Université Lyon 1, Observatoire de Lyon, Centre de

Recherche Astrophysique de Lyon and École Normale Supérieurede Lyon, 9 avenue Charles André, F-69230 Saint-Genis Laval,France

71 Steward Observatory, University of Arizona, 933 NorthCherry Avenue, Tucson, AZ 85721, USA

72 Institut UTINAM, CNRS-UMR6213, OSU THETA,Université Bourgogne-Franche-Comté, 41bis avenue del’Observatoire, 25010 Besançon Cedex, France

73 Space Telescope Science Institute, 3700 San Martin Drive,Baltimore, MD 21218, USA

74 CSRA, Inc., 3700 San Martin Drive, Baltimore, MD 21218,USA

75 Department of Physics & Astronomy, University of Iowa,Iowa City, IA 52245

76 Laboratoire AIM, CEA/DRF – CNRS - Univ. ParisDiderot – IRFU/SAp, Centre de Saclay, 91191 Gif-sur-YvetteCedex, France

77 National Astronomy Observatories, Chinese Academy ofScience, 20A Datun Road, Chaoyang District, Beijing, 100012,P. R. China

78 Sorbonne Universités, UPMC Univ Paris 06, UMR 7095,Institut d’Astrophysique de Paris, 98 bis boulevard Arago,F-75014, Paris, France

79 Laboratoire de Physique Nucléaire et de Hautes Energies,Université Pierre et Marie Curie, 4 Place Jussieu,75005 Paris,France

80 Osservatorio Astronomico di Padova – INAF, Vicolodell’Osservatorio 5, I-35122, Padova, Italy

81 Institute of Cosmology & Gravitation, University ofPortsmouth, Dennis Sciama Building, Portsmouth, PO1 3FX,UK

82 DePauw University, Greencastle, IN 46135, USA83 LPNHE, CNRS/IN2P3, Université Pierre et Marie Curie

Paris 6, Université Denis Diderot Paris 7, 4 place Jussieu, 75252Paris CEDEX, France

84 Department of Astronomy, Case Western Reserve Univer-sity, Cleveland, OH 44106, USA

85 Max Planck Institute for Solar System Research,Justus-von-Liebig-Weg 3, 37077 Goettingen, Germany

86 Sydney Institute for Astronomy (SIfA), School of Physics,University of Sydney, NSW 2006, Australia

87 SETI Institute, 189 Bernardo Avenue, Mountain View, CA94043, USA

88 Rice University, 6100 Main St, Houston, TX 77005, USA89 Department of Astronomy, The Ohio State University, 140

W. 18th Ave., Columbus, OH 43210, USA90 Center for Cosmology and AstroParticle Physics, The Ohio

State University, 191 W. Woodruff Ave., Columbus, OH 43210,USA

91 Max-Planck-Institut fuer Astrophysik, Karl-Schwarzschild-Str. 1, D-85748 Garching, Germany

92 Institute for Astronomy, University of Vienna, Türken-schanzstrasse 17, 1180 Vienna, Austria

93 Department of Physics and Astronomy, University ofCalifornia, Irvine, Irvine, CA 92697, USA

94 Instituto de Astrof́ısica, Pontificia Universidad Católica deChile, Av. Vicuna Mackenna 4860, 782-0436 Macul, Santiago,Chile

4 SDSS Collaboration

1. INTRODUCTION

The Sloan Digital Sky Survey (SDSS) has been ob-serving the Universe using the 2.5-meter Sloan Foun-dation Telescope (Gunn et al. 2006) at Apache PointObservatory (APO) for over 15 years. The goal of theoriginal survey (2000–2005; York et al. 2000) was to maplarge-scale structure with five-band imaging over ∼ πsteradians of the sky and spectra of ∼ 106 galaxies and∼105 quasars. This program was accomplished using a

95 Academia Sinica Institute of Astronomy and Astrophysics,P.O. Box 23-141, Taipei 10617, Taiwan

96 Astrophysics Research Institute, Liverpool John MooresUniversity, IC2, Liverpool Science Park, 146 Brownlow Hill,Liverpool L3 5RF, UK

97 CSIC, Serrano, 117 - 28006, Madrid, Spain98 Space Science Institute, 4750 Walnut Street, Suite 205,

Boulder, CO 80301, USA99 ELTE Gothard Astrophysical Observatory, H-9704 Szom-

bathely, Szent Imre herceg st. 112, Hungary100 Departamento de Ciencias Fisicas, Universidad Andres

Bello, Sazie 2212, Santiago, Chile101 Max-Planck-Institut für Astronomie, Konigstuhl 17,

D-69117 Heidelberg, Germany102 LESIA, UMR 8109, Université Pierre et Marie Curie,

Université Denis Diderot, Observatoire de Paris, F-92195Meudon Cedex, France

103 Department of Physics and Astronomy, University ofWyoming, Laramie, WY 82071, USA

104 Department of Physics and Astronomy, University ofAlabama, Tuscaloosa, AL 35487-0324, USA

105 Institute for Astronomy, University of Edinburgh, RoyalObservatory, Edinburgh EH9 3HJ, UK

106 Université Paris 6 et CNRS, Institut d’Astrophysique deParis, 98bis blvd. Arago, 75014 Paris, France

107 Instituto de Astrof́ısica de Andalućıa (CSIC), Glorieta dela Astronomı́a, E-18080 Granada, Spain

108 Department of Astrnonomy and Astrophysics, Universityof California, Santa Cruz and UC Observatories, Santa Cruz,CA, 95064, USA

109 Dipartimento di Fisica e Astronomia, Università diPadova, Vicolo dell’Osservatorio 2, I-35122 Padova, Italy

110 Departamento de Fisica y Astronomia, Universidad de LaSerena, Cisternas 1200, La Serena, 0000-0002-1379-4204, Chile

111 Instituto de Astronomı́a, Universidad Nacional Autónomade México, Unidad Académica en Ensenada, Ensenada BC22860, Mexico

112 Department of Astronomy and Space Science, SejongUniversity, Seoul 143-747, Korea

113 Hubble Fellow114 Observatoire de la Côte d’Azur, Laboratoire Lagrange,

06304 Nice Cedex 4, France115 Department of Physics and Astronomy, Ohio University,

Clippinger Labs, Athens, OH 45701, USA116 Institute of Space Sciences (IEEC-CSIC), Carrer de Can

Magrans, E-08193, Barcelona, Spain117 University of Texas at Austin, McDonald Observatory,

McDonald Observatory, TX, 79734, USA118 Brookhaven National Laboratory, Upton, NY 11973, USA119 National Optical Astronomy Observatories, Tucson, AZ,

85719, USA120 School of Physics, The University of New South Wales,

Sydney NSW 2052, Australia121 Department of Astrophysical Sciences, Princeton Univer-

sity, Princeton, NJ 08544, USA122 Instituto de F́ısica, Universidad Nacional Autónoma de

México, Apdo. Postal 20-364, Mexico123 Department of Physical Sciences, The Open University,

Milton Keynes MK7 6AA, UK124 Department of Physics, University of North Carolina

Asheville, One University Heights, Asheville, NC 28804, USA125 Physics Department, Austin College, Sherman, TX 75092,

USA126 Department of Physics and Astronomy, University of

Kentucky, 505 Rose Street, Lexington, KY 40506, USA127 Department of Physics, Bridgewater College, 402 E

College St., Bridgewater, VA 22812, USA

drift-scan camera (Gunn et al. 1998) and two fiber-fedoptical R∼1800 spectrographs (Smee et al. 2013), eachwith 320 fibers.

The imaging and spectroscopy goals were not entirelyfulfilled in the initial five-year period, and thus SDSS-I was followed by SDSS-II (2005–2008; Abazajian et al.2009). Its first goal was to complete the planned initiallarge scale structure redshift survey as the Legacy pro-gram. It added SEGUE (Sloan Extension for GalacticUnderstanding and Exploration; Yanny et al. 2009), aspectroscopic survey focused on stars, and imaged anaverage of once every five days a ∼ 200 sq. deg areaalong the celestial equator with repeated scans in SDSS-I (“Stripe 82”), to search for Type Ia supernovae andother transients (Frieman et al. 2008).

The success of SDSS as a cosmological probe, partic-ularly the detection of the clustering of luminous redgalaxies (LRG) on the 100 h−1 Mpc scale expectedfrom baryon acoustic oscillations (BAO; Eisenstein et al.2005), led to the conception and implementation of BOSS(Baryon Oscillation Spectroscopic Survey; Dawson et al.2013) as the flagship program in the third version of thesurvey, SDSS-III (2008–2014; Eisenstein et al. 2011). Aspart of BOSS, SDSS-III imaged additional areas in thepart of the south Galactic cap visible from the Northernhemisphere. At the conclusion of these observations, theSDSS imaging camera was retired and is now part of thepermanent collection of the Smithsonian National Airand Space Museum1. During the summer shutdown in2009, the original SDSS spectrographs were replaced bynew, more efficient, spectrographs to be used by BOSS.The BOSS spectrographs featured expanded wavelengthcoverage (3560Å< λ

Data Release 13 5

Release 12 (Alam et al. 2015), which contained all ofthe SDSS-III data, as well as the re-reduced data fromSDSS-I and SDSS-II. SDSS-I to SDSS-III imaged 14,555degree2 in the five filters (Fukugita et al. 1996; Doiet al. 2010). Most of the sky was surveyed once ortwice, but regions in Stripe 82 were observed between70 and 90 times. By the time of their retirement, theSDSS spectrographs had obtained R∼1800 optical spec-tra for 860,836 galaxies, 116,003 quasars, and 521,990stars. With the BOSS spectrographs, the survey hasadded data with similar resolution for 1,372,737 galax-ies, 294,512 quasars, and 247,216 stars. APOGEE hascontributed high-resolution IR spectra of 156,593 stars.MARVELS had observed 3233 stars with at least 16epochs of radial velocity measurements.

The success of the previous Sloan Digital Sky Surveysand the continuing importance of the wide-field, multi-plexing capability of the Sloan Foundation Telescope mo-tivated the organization of the fourth phase of the survey,SDSS-IV (Blanton et al. 2017). SDSS-IV extends SDSSobservations to many fibers covering the spatial extent ofnearby galaxies, to new redshift regimes, and to the partsof the Milky Way and dwarf galaxies that are only visiblefrom the Southern Hemisphere. The MaNGA (MappingNearby Galaxies at APO) survey studies galaxy forma-tion and evolution across a wide range of masses andmorphological types by observing a substantial portionof the optical spatial extent of ∼104 galaxies (Bundy etal. 2015). It accomplishes this goal by employing 17 bun-dles ranging in size between 19 and 127 fibers to covertargets selected from an extended version of the NASA-Sloan Atlas2 and 12 bundles of 7 fibers for calibrationstars. These integral field units (IFUs) feed the BOSSspectrographs, providing information on the propertiesof gas and stars in galaxies out to 1.5–2.5 effective radii(Re).

Another survey, eBOSS (extended Baryon OscillationSpectroscopic Survey Dawson et al. 2016), shares thedark time equally with MaNGA. eBOSS will measurewith percent-level precision the distance-redshift relationwith BAO in the clustering of matter over the relativelyunconstrained redshift range 0.6 < z < 2.2. This red-shift range probes the Universe during its transition frommatter-dominated to dark-energy-dominated. Multiplemeasurements of the angular diameter distance (dA(z))and Hubble parameter (H(z)) from BAO over the red-shifts covered by eBOSS are therefore crucial for under-standing the nature of dark energy. eBOSS will usespectroscopic redshifts from more than 400,000 LRGsand nearly 200,000 Emission-Line Galaxies (ELGs) toextend the final BOSS galaxy clustering measurements(Alam et al. 2017) by providing two new BAO dis-tance measurements over the redshift interval 0.6 < z <1.1 Roughly 500,000 spectroscopically-confirmed quasarswill be used as tracers of the underlying matter den-sity field at 0.9 < z < 2.2, providing the first mea-surements of BAO in this redshift interval. Finally, theLyman-α forest imprinted on approximately 120,000 newquasar spectra will give eBOSS an improved BAO mea-surement over that achieved by BOSS (Delubac et al.2015; Bautista et al. 2017). The three new tracers willprovide BAO distance measurements with a precision of

2 http://www.nsatlas.org

1% at z = 0.7 (LRG), 2% at z = 0.85 (ELG), and 2%at z = 1.5 (quasar) while the enhanced Lyman-α for-est sample will improve BOSS constraints by a factorof 1.4. Furthermore, the clustering from eBOSS trac-ers will allow new measurements of redshift-space dis-tortions (RSD), non-Gaussianity in the primordial den-sity field, and the summed mass of neutrino species.Extensively observing these redshift ranges for the firsttime in SDSS required re-evaluation of targeting strate-gies. Preliminary targeting schemes for many of theseclasses of objects were tested as part of SEQUELS (SloanExtended QUasar, Emission-line galaxy, Luminous redgalaxy Survey), which used 126 plates observed acrossSDSS-III and -IV. DR13 includes all SEQUELS data,giving the largest SDSS sample to date of spectra target-ing intermediate redshift ranges. SDSS-IV also allocateda significant number of fibers on the eBOSS plates totwo additional dark-time programs. TDSS(Time DomainSpectroscopic Survey; Morganson et al. 2015) seeks tounderstand the nature of celestial variables by delib-erately targeting objects that vary in combined SDSSDR9 and Pan-STARRS1 data (PS1; Kaiser et al. 2002). A large number of the likely quasar targets so se-lected are also targeted by the main eBOSS algorithmsand therefore meet the goals of both surveys. TDSS-only targets fill ∼ 10 spectra per square degree. Themain goal of the SPIDERS (Spectroscopic Identificationof eROSITA Sources) survey is to characterize a sub-set of X-ray sources identified by eROSITA (extendedRoentgen Survey with an Imaging Telescope Array;Predehl et al. 2014) . Until the first catalog of eROSITAsources is available, SPIDERS will target sources fromthe RASS (Roetgen All Sky Survey; Voges et al. 1999)and XMM (X-ray Multi-mirror Mission; Jansen et al.2001). SPIDERS will also obtain on average ∼ 10 spec-tra per square degree over the course of SDSS-IV, but thenumber of fibers per square degree on a plate is weightedtoward the later years to take advantage of the new datafrom eROSITA.

In bright time at APO, APOGEE-2, the successor toAPOGEE (hereafter APOGEE-1) in SDSS-IV, will con-tinue its survey of the Milky Way stellar populations.Critical areas of the Galaxy, however, cannot be observedfrom APO, including the more distant half of the Galac-tic bar, the fourth quadrant of the disk, and importantdwarf satellites of the Milky Way, such as the MagellanicClouds and some dwarf spheroidals. SDSS-IV will for thefirst time include operations outside of APO as the resultof Carnegie Observatories and the Chilean ParticipationGroup joining the collaboration. A second APOGEEspectrograph is being constructed for installation on the2.5-meter du Pont Telescope (Bowen & Vaughan 1973)at Las Campanas Observatory (LCO) near La Serena,Chile. When APOGEE-2S begins survey operations in2017, approximately 300 survey nights on the du PontTelescope will be used to extend the APOGEE-2 surveyto the Southern Hemisphere.

Data Release 13 (DR13) is the first data release forSDSS-IV, which will have regular public, documenteddata releases, in keeping with the philosophy of SDSSsince its inception. In this paper, we describe the dataavailable in DR13, focusing on the data appearing herefor the first time. We present overall descriptions of thesample sizes and targeting and provide a detailed bibli-

http://www.nsatlas.org

6 SDSS Collaboration

ography of the technical papers available to understandthe data and the surveys in more detail. These technicalpapers and the SDSS website (www.sdss.org) containcritical information about these data, which here is onlysummarized.

2. OVERVIEW OF THE SURVEY LANDSCAPE

The release of DR13 coincides with the arrival of anastonishgly rich set of data from ongoing and recentlycompleted surveys outside of SDSS. Blanton et al. (2017),Bundy et al. (2015), and Majewski et al. (2017) describehow the SDSS-IV surveys compare in survey strategy,size, and data within the wider arena of spectroscopicsurveys. We cite here some key science results for theseworks to complement our brief history of SDSS.

Spectroscopic redshift surveys of large scale structurehave resulted in BAO measurements over a range of red-shifts. The BAO signal has been detected at lower red-shift (z∼0.1) from measurements of ∼75,000 galaxies inthe 6dF Galaxy Redshift Survey (Beutler et al. 2011).WiggleZ measured the BAO signal at similar redshiftsto BOSS based on redshifts of ∼225,000 galaxies. Thefinal WiggleZ results for the 1-D BAO peak (Kazin etal. 2014) and the 2-D BAO peak (Hinton et al. 2017) atz=0.44, 0.6 and 0.73 agree with the BOSS results. Stillunderway is the VIMOS Public Extragalactic RedshiftSurvey, which focuses on higher redshifts than previouswork (0.5 ∼ 5×109 M� form from the inside out, with themore massive galaxies having older stellar populations ineach scaled radial bin (Pérez et al. 2013). Cano-Dı́az etal. (2016) showed the relationship between star forma-tion rate (SFR) and stellar mass that held for entirestar-forming galaxies was also true for spatially resolvedregions within a galaxy. In another intriguing clue to therelation between small and large scales, Sánchez et al.(2014) found that the HII regions in 306 CALIFA galax-ies have a characteristic disk oxygen abundance gradientwhen scaled to its effective radius.

Another large IFU survey, the SAMI survey (Bryant etal. 2015), is currently underway at the Anglo-AustralianTelescope and will utimately observe 3400 galaxies. TheEarly Data Release (Allen et al. 2015) provided data for107 galaxies, while the first major data release (Greenet al. 2017) includes 772 galaxies. Science results fromthe SAMI survey so far include characterizing the galac-tic winds or extended diffuse ionizing gas in edge-on diskgalaxies Ho et al. (2016), mapping the quenching of starformation proceeding from the outside-in in dense en-vironments Schaefer et al. (2017), and identifying stellarmass as the main variable affecting fraction of ETGs thatare slow rotators Brough et al. (2017)

While APOGEE-2 is currently the only infrared stel-lar spectroscopic survey, there are several Galactic stel-lar surveys of similar scope observing at optical wave-lengths. The RAVE survey (Steinmetz et al. 2006) com-pleted observations in 2013 with R∼7,500 spectra of∼450,000 bright stars in the CaII triplet region releasedin Data Release 5 (Kunder et al. 2017). RAVE’s pri-mary goal was obtain spectroscopic measurements forstars with exquisite Gaia proper motion and parallaxmeasurements. The ∼ 250,000 stars in DR5 that haveproper motions and parallaxes in the first Gaia data re-lease (Gaia Collaboration et al. 2016) now make this workpossible in both the disk (e.g., Robin et al. 2017) and halo(e.g., Helmi et al. 2017). Among the many results pub-lished prior to the Gaia results were measurements of thelocal escape velocity (Smith et al. 2007; Piffl et al. 2014,e.g.,), detection of a “wobbly” Galaxy from asymmetricvelocities both radially and across the disk (Siebert et al.2011; Williams et al. 2013)), and the observation of extra-tidal stars from Galactic globular clusters (e.g., Kunderet al. 2014; Fernández-Trincado et al. 2015; Anguiano etal. 2016)

The Gaia-ESO survey recently completed observationsof ∼ 105 cluster and field stars at either R∼20,000 orR∼47,000 (Gilmore et al. 2012). The Gaia-ESO collab-oration has presented important results about the na-ture of several Galactic components, including the abun-dances and kinemetics in the bulge (e.g., Howes et al.2014; Rojas-Arriagada et al. 2017; Recio-Blanco et al.2017), characterizing the accreted component of the haloand (if any) disk (e.g., Ruchti et al. 2015), and measuringradial abundance gradients in the open cluster popula-tion (e.g., Jacobson et al. 2016; Magrini et al. 2017).

The LAMOST Galactic survey is obtaining spectra,4000 at a time, at a similar resolution to SEGUE (Zhao

www.sdss.org

Data Release 13 7

et al. 2012). Data Release 33 includes 5.75 million spectraand stellar parameters for > 3 million stars. LAMOSThas an ideal view of the field observed for four yearsby the Kepler satellite, and several groups have used the∼50,000 stars observed in the Kepler field to characterizeits stellar populations (e.g., Dong et al. 2014; Frasca etal. 2016; Ren et al. 2016; Chang et al. 2017; Dong et al.2017). Other work based on LAMOST spectra includesmeasurements of stellar activity (e.g., Fang et al. 2016)and identification of important subclasses of objects fromultra metal-poor stars (e.g., Li et al. 2015) and metalliticlined Am stars (e.g., Hou et al. 2015).

The GALAH survey (De Silva et al. 2015), whichstarted operations in 2014, released spectra for over200,000 stars observed with the HERMES spectrograph(R∼28,000) at the Anglo-Australian Telescope (Martellet al. 2017). When the survey is completed, spectra of∼ 106 stars in 4 optical windows accessing 29 elementsare expected.

3. SCOPE OF DATA RELEASE 13

SDSS-IV has been operating since July 2014. DR13contains data gathered between July 2014 and July 2015and is summarized in Table 1. The categories underMaNGA galaxies are described in §5. The SEQUELStargeting flags are listed and described in Alam et al.(2015). Figures 1, 2, 3 show the sky coverage of theMaNGA, eBOSS and APOGEE-2 surveys respectively.In the subsequent sections, we discuss each survey’s datain detail, but briefly DR13 includes

• Reduced data for the 82 MaNGA galaxy surveyplates, yielding 1390 reconstructed 3-D data cubesfor 1351 unique galaxies, that were completed byJuly 2015. Row-stacked spectra (RSS) and rawdata are also included.

• Reduced BOSS spectrograph data for an additional60 SEQUELS plates, completing the SEQUELSprogram. The total number of SEQUELS platesreleased in DR12 and DR13 is 126. These platesprovide a complete footprint covering roughly 400square degrees that will not be revisited in eBOSS.The targets include a superset of the eBOSS LRGand quasar samples, a sample of variability-selectedpoint sources at a much higher density than inTDSS, and new X-ray-selected objects selected bysimilar criteria to targets in SPIDERS.

• The reduced data for five BOSS plates at low dec-lination in the SGC. These plates were drilled dur-ing SDSS-III but not observed due to insufficientopen-dome time when they were observable. Theplates were observed early in SDSS-IV to fill in thefootprint in this region.

• Spectroscopic data from BOSS processed with anew version of the data reduction pipeline, whichresults in less-biased flux values.

• All APOGEE-1 data re-reduced with improved tel-luric subtraction and analyzed with an improvedpipeline and synthetic grid, including adding rota-tional broadening as a parameter for dwarf spectra.

3 dr3.lamost.org

• New species (C I, P, Ti II, Co, Cu, Ge, and Rb)with reported abundances for APOGEE-1 sample.

• Stellar parameters for APOGEE-1 stars with coolereffective temperatures (Teff < 3500K), derived byan extension of the grid of synthetic spectra us-ing MARCS (Gustafsson et al. 2008) model atmo-spheres.

• Recalibrated SDSS imaging catalogs, using the hy-percalibration to PanSTARRS-1 implemented byFinkbeiner et al. (2016).

• Valued-added catalogs, see Table 2. More detailand direct links to the catalogs and their datamod-els can be found at http:\www.sdss.org/dr13/data_access/vac.

• The most recent reductions of all data from previ-ous iterations of SDSS is included as a matter ofcourse. For MARVELS data, these data are thesame as in DR12; for SEGUE and SEGUE-2, thesame as in DR9.

DR13 contains only a subset of the reduced or rawdata for all surveys taken between July 2014 and July2015. The first two years of eBOSS data are neededbefore useful cosmological constraints can be extracted.APOGEE-2 is using the first year of SDSS-IV data towork on science verification and targeting optimizationfor new classes of targets and new survey strategies. Bothof these surveys will release more extensive new data inData Release 14.

4. DATA DISTRIBUTION

The data for DR13 are distributed through the samemechanisms available in DR12, with some significantchanges to the environment used to access the catalogdata (see below). Raw and processed image and spectro-scopic data are available, as before, through the ScienceArchive Server (SAS, data.sdss.org/sas/dr13), andalso for imaging data, optical spectra, and APOGEE IRspectra through an interactive web application (dr13.sdss.org, available soon). The catalogs of photomet-ric, spectroscopic, and derived quantities are availablethrough the Catalog Archive Server or CAS (Thakaret al. 2008; Thakar 2008) via two primary modes of ac-cess: browser-based queries of the database are avail-able through the SkyServer Web application (http://skyserver.sdss.org) in synchronous mode, and moreadvanced and extensive querying capabilities are avail-able through CasJobs (http://skyserver.sdss.org/casjobs) in asynchronous or batch mode that allowstime-consuming queries to be run in the background (Li& Thakar 2008).

The CAS is now part of the new SciServer(http://www.sciserver.org/) collaborative science frame-work that allows users single-sign-on access to a suite ofcollaborative data-driven science services that includesthe classic SDSS services of SkyServer and CasJobs.These services are essentially unchanged in their userinterfaces but have acquired powerful new capabilitiesand undergone fundamental re-engineering to make theminteroperable and applicable to other science domains.

dr3.lamost.orghttp:\www.sdss.org/dr13/data_access/vachttp:\www.sdss.org/dr13/data_access/vacdata.sdss.org/sas/dr13dr13.sdss.orgdr13.sdss.orghttp://skyserver.sdss.orghttp://skyserver.sdss.orghttp://skyserver.sdss.org/casjobshttp://skyserver.sdss.org/casjobshttp://www.sciserver.org/

8 SDSS Collaboration

TABLE 1Reduced spectroscopic data in DR13

Target Category # DR12 # DR12+13

MaNGA main galaxy sample:PRIMARY v1 2 0 600

SECONDARY v1 2 0 473COLOR-ENHANCED v1 2 0 216

MaNGA ancillary galaxies1 0 31MaNGA Other 0 62

SEQUELSLRG IZW 11781 21271LRG RIW 11691 20967

QSO EBOSS CORE 19455 33367QSO PTF 13227 22609

QSO REOBS 1357 2238

QSO EBOSS KDE 11836 20474QSO EBOSS FIRST 293 519

QSO BAD BOSS 59 95QSO BOSS TARGET 59 95

DR9 CALIB TARGET 28594 49765

SPIDERS RASS AGN 162 275SPIDERS RASS CLUS 1533 2844

TDSS A 9412 17394TDSS FES DE 40 70

TDSS FES DWARFC 19 29

TDSS FES NQHISN 73 148TDSS FES MGII 1 2

TDSS FES VARBAL 55 103SEQUELS PTF VARIABLE 700 1153

APOGEE-2All Stars 164562 164562

NMSU 1-meter stars 894 894Telluric stars 17293 17293

Commissioning stars 11917 11917

1 Many MaNGA ancillary targets were also targeted as part of the main galaxysample, and are counted twice in this table.

TABLE 2Value-Added Catalogs New in DR13

Catalog Description Reference http://data.sdss.org/sas/dr13/

SPIDERS Clusters demonstration sample catalog Clerc et al. (2016) eboss/spiders/analysis/SPIDERS AGN target selection catalog Dwelly et al. (2017) eboss/spiders/target/SPIDERS cluster target selection catalog Clerc et al. (2016) eboss/spiders/target/WISE Forced Photometry Lang et al. (2016) eboss/photoObj/external/WISEForcedTarget/301/Composite Spectra of Emission-line Galaxies Zhu et al. (2015) eboss/elg/composite/v1_0/ELG Fisher selection catalog Delubac et al. (2017) eboss/target/elg/fisher-selection/Redmonster redshift & spectral classification catalog Hutchinson et al. (2016) eboss/spectro/redux/redmonster/v5_9_0/v1_0_1/QSO Variability Palanque-Delabrouille et al. (2016) eboss/qso/variability/APOGEE DR13 red-clump catalog Bovy et al. (2014) apogee/vac/apogee-rc/cat/

New services are also available to users once they regis-ter on the SciServer portal, and these services work seam-lessly with the existing tools. Most notable among thenew offerings are SciDrive, a distributed DropBox-likefile store; SkyQuery, a federated cross-matching servicethat compares and combines data from a collection (fed-eration) of multi-wavelength archives (SkyNodes); andSciServer Compute, a powerful new system for upload-ing complex analysis scripts as Jupyter notebooks (usingPython, MatLab or R) running in Docker containers.

Links to all of these methods are provided atthe SDSS website (http://www.sdss.org/dr13/data_access) The data processing software for APOGEE,BOSS, and SEGUE are publicly available at http://www.sdss.org/dr13/software/products. A set of tu-

torial examples for accessing SDSS data is provided athttp://www.sdss.org/dr13/tutorials. All flat filesare available for download at http://data.sdss.org/datamodel/

5. RECALIBRATION OF IMAGING DATA

DR13 includes a photometric recalibration of the SDSSimaging data. Finkbeiner et al. (2016) rederived the g, r,i and z band zero points using the PS1 photometric cali-brations of Schlafly et al. (2012), as well as rederiving theflat fields in all five bands (including u) . This effort im-proved the accuracy of the SDSS photometry previouslyhindered by a paucity of overlapping scans to performub̈ercalibration across the entire SDSS sky and by sub-optimal flatfields. The residual systematics, as measured

http://data.sdss.org/sas/dr13/eboss/spiders/analysis/eboss/spiders/target/eboss/spiders/target/eboss/photoObj/external/WISEForcedTarget/301/eboss/elg/composite/v1_0/eboss/target/elg/fisher-selection/eboss/spectro/redux/redmonster/v5_9_0/v1_0_1/eboss/qso/variability/apogee/vac/apogee-rc/cat/http://www.sdss.org/dr13/data_accesshttp://www.sdss.org/dr13/data_accesshttp://www.sdss.org/dr13/software/productshttp://www.sdss.org/dr13/software/productshttp://www.sdss.org/dr13/tutorialshttp://data.sdss.org/datamodel/http://data.sdss.org/datamodel/

Data Release 13 9

Fig. 1.— In grey are shown the locations in equatorial coordinates of all possible plates with MaNGA galaxies, each containing 17 galaxytargets. Because the MaNGA targets are selected to have SDSS photometry, this footprint corresponds to the Data Release 7 imagingdata (Abazajian et al. 2009). Approximately 30% of these will be observed in the full planned MaNGA survey. The blue shows the platesobserved in the first year of MaNGA for which data cubes are released in this paper.

Fig. 2.— Coverage of DR13 data from BOSS and SEQUELS in equatorial coordinates. The blue areas show the locations in equatorialcoordinates of the five new BOSS plates (SGC) and the 126 SEQUELS plates (NGC) released in DR13. The green represents the areacovered by BOSS in DR12. The SEQUELS plates released in DR12 lie in the same region as the new ones in DR13, providing completecoverage over roughly 400 square degrees.

10 SDSS Collaboration

by comparison with PS1 photometry and spectral energydistributions for stars with spectra, are reduced to 0.9,0.7, 0.7 and 0.8% in the griz bands, respectively; sev-eral previously uncertain calibrations of specific runs arealso now much better constrained. The resulting recali-brated imaging catalogs are the basis for the eBOSS andMaNGA targeting.

For the MaNGA target selection, we are using theNASA-Sloan Atlas (NSA; Blanton et al. 2011), a reanal-ysis of the SDSS photometric data using sky subtractionand deblending better tuned for large galaxies. Rela-tive to the originally distributed version of that cata-log, we have used the new calibrations mentioned above,increased the redshift range up to z = 0.15, and haveadded an elliptical aperture Petrosian measurement offlux, which MaNGA targeting is based upon. DR13 in-cludes the NSA catalog (version v1 0 1) associated withthis reanalysis as the nsatlas CAS table and as a file onthe SAS. For the MaNGA galaxies released in DR13, weprovide the actual images (referred to in MaNGA docu-mentation as “preimaging”) on the SAS as well.

Lang et al. (2016) reanalyzed data from the Wide-fieldInfrared Satellite Explorer (WISE; Wright et al. 2010) touse for eBOSS targeting. They used positions and galaxyprofile measurements from SDSS photometry as inputstructural models and constrained fluxes in the WISE3.4 µm and 4.6 µm bands. These results agree withthe standard WISE reductions to within 0.03 mag forhigh signal-to-noise ratio, isolated point sources in WISE.However, the new reductions provide flux measurementsfor low signal-to-noise ratio (< 5σ) objects detected inthe SDSS but not in WISE (over 200 million objects).Despite the fact that the objects are undetected, theirflux measurements are nevertheless informative for tar-get selection, in particular for distinguishing stars fromquasars. This photometry is provided as a value-addedcatalog in the wiseForcedTarget CAS table and on theSAS as described in Table 2.

The Galactic extinction estimates published in theSDSS imaging tables (photoObj and related tables in theCAS) have been changed. The Galactic extinction stilluses the Schlegel et al. (1998) models of dust absorp-tion to estimate E(B − V ), but the Galactic extinctioncoefficients for each band have been updated as recom-mended by Schlafly & Finkbeiner (2011). The extinctioncoefficients Ru, Rg, Rr, Ri, and Rz are changed from thevalues used in BOSS (5.155, 3.793, 2.751, 2.086, 1.479) toupdated values (4.239, 3.303, 2.285, 1.698, 1.263). Thecorresponding numbers for the WISE bands are RW1 =0.184 for the WISE 3.4µm band and RW2 = 0.113 forthe 4.6µm band (Fitzpatrick 1999).

6. MANGA: INTEGRAL FIELD SPECTROSCOPIC DATA

MaNGA is investigating the internal kinematics andcomposition of gas and stars in low redshift (z ≤ 0.15)galaxies using fiber bundles to feed the BOSS spectro-graphs. Bundy et al. (2015) describe the high-level sci-entific goals, scope, and context of the survey in inves-tigating galaxy formation while Yan et al. (2016) give adetailed description of the survey design, execution, anddata quality relevant to DR13. With nearly 1390 datacubes released, MaNGA’s DR13 data products representthe largest public sample to date of galaxies observedwith integral field spectroscopy. This data set signifies a

valuable first step in MaNGA’s goals to reveal the inter-nal properties and dynamics of a statistically powerfulsample of galaxies, that spans a broad range in stellarmass, local environment, morphology, and star formationhistory. Individual observations across the sample are ofsufficient quality to characterize the spatially-dependentcomposition of stars and gas as well as their internalkinematics, thus providing important clues on growthand star formation fueling, the build-up of spheroidalcomponents, the cessation of star formation, and the in-tertwined assembly history of galaxy subcomponents.

The survey was made possible through an instrumen-tation initiative in SDSS-IV to develop a reliable andefficient way of bundling 1423 optical fibers into tightly-packed arrays that constitute MaNGA’s IFUs (Droryet al. 2015). For each pointing, MaNGA observes 17science targets with IFUs ranging from 19 to 127 fibers(with diameters of 12−32′′). The IFU size distribu-tion was optimized in concert with the sample design(Wake et al. 2017) that targets SDSS-I/II main sam-ple galaxies at a median redshift of 0.03 to obtain in6 years a sample of 104 galaxies with uniform radial cov-erage and a roughly flat distribution in log M∗ limitedto M∗ > 10

9M�. Careful attention was paid to opti-mizing hardware and an observing strategy that ensureshigh quality imaging spectroscopy (Law et al. 2015) andto surface photometric flux calibration with a precisionbetter than 5% across most of the wavelength range,3,622-10,354Å(Yan et al. 2016). As described in thesepapers, salient aspects included protocols for constrain-ing hour-angles of observations to limit differential at-mospheric diffraction, dithering exposures to avoid gapsin the coverage of the targets because of space betweenthe fibers, and special calibration mini-bundles to ensurereliable absolute and relative flux calibration. An au-tomated pipeline delivers sky-subtracted, flux-calibratedrow-stacked-spectra (RSS) and datacubes for all sources(Law et al. 2016).

6.1. MaNGA DR13 Main Galaxy Sample

At the completion of SDSS-IV, the MaNGA survey’smain galaxy sample will include ∼ 104 galaxies withM∗ > 10

9M� and a roughly flat stellar mass distribu-tion. DR13’s 1390 galaxy data cubes, corresponding to1351 unique galaxies makes it the largest public sample ofgalaxies with IFU spectroscopy. MaNGA’s main galaxysample consists of three major parts: Primary sample,Secondary sample, and the Color-Enhanced supplement.

The Primary sample and the Secondary sample areselected from two luminosity-dependent redshift bands,as shown in Figure 4. The selection for each sample isvolume-limited at each absolute i-band magnitude. Theshape of the redshift bands is motivated by MaNGA’sscience requirements of having a uniform spatial cover-age in units of galaxy’s effective radius Re and havinga roughly flat stellar mass distribution (Yan et al. 2016;Wake et al. 2017). Figure 5 shows the distribution of theMaNGA DR13 galaxies in the stellar mass vs. dark mat-ter halo mass plane. Because more massive galaxies areon average larger, we observe them at a larger distancethan low mass galaxies. We chose the redshift bandsso that the great majority of the Primary (Secondary)sample is covered by our fiber bundles to 1.5 Re (2.5 Re)along their major axes. This has the commensurate ef-

Data Release 13 11

Fig. 3.— Coverage of APOGEE-2 DR13 data in Galactic coordinates; the raw data and its coverage is the same as in DR12, but it hasbeen reprocessed through the latest reduction pipeline and ASPCAP versions. The color coding denotes the number of visits to each field,as indicated in the legend.

12 SDSS Collaboration

fect of changing the physical resolution systematically asa function of stellar mass, as illustrated in Figure 4. Po-tential deleterious effects of this change in sampling areaddressed by an ancillary program, described below.

We also designed a Color-Enhanced supplement, as asupplement to the Primary sample, to enhance the sam-pling of galaxies with rare color-magnitude combinations,such as low-mass red galaxies, high mass blue galaxies,and green valley galaxies. This is achieved by extendingthe redshift limits around the Primary sample redshiftband for each underpopulated region in color-magnitudespace.

The combination of the Primary sample and the Color-Enhanced supplement is referred to as the Primary+sample. We provide in our data release the redshift limitsover which each galaxy is selected. This permits a correc-tion to the sample using 1/Vmax weight to reconstruct avolume-limited representation of the galaxy population,provided that there is negligible galaxy evolution overthis limited redshift range. More details of how we ar-rived at this selection can be found in Yan et al. (2016)and Wake et al. (2017). Wake et al. (2017) provides thedetails of how to properly weight the sample to recon-struct a volume-limited representation.

Among the 1351 unique galaxies released in DR13,there are 600 Primary Sample galaxies, 473 SecondarySample galaxies, and 216 Color-Enhanced supplementgalaxies. There are 62 galaxies that do not belong to anyof the above. Some of these are ancillary program targets(see below), some are filler objects on plates with sparebundles, and others are galaxies selected using older, ob-solete versions of the selection and observed on earlyplates. For most statistical analyses, these 62 galaxiesshould be excluded.

Which sample a given target galaxy belongs to is givenby the MANGA TARGET1 bitmask (or mngtarg1 in the“drpall” file; see §5.2). Primary sample galaxies have bit10 set to 1, Secondary Sample galaxies have bit 11 set to1, and Color-Enhanced Supplement Galaxies have bit 12set to 1. Bits 1-9 are for obsolete selections and shouldbe ignored.

6.1.1. MaNGA Galaxy Ancillary Programs

Roughly 5% of the IFUs are assigned to ancillary sci-ence programs defined by and allocated through internalcompetition and review. This assignment takes advan-tage of sky regions with a low density of galaxies definedby our main survey criteria (above) or where certain rareclasses of objects, possibly outside our selection cuts, areof sufficiently high science value to re-allocate IFUs fromthe main program. Such high-value targets sometimescome from the main sample, but the ancillary sciencegoals require a different bundle size, a slightly differentcenter positions, or higher prioritization over the randomselection among the main sample galaxies. There are alsoscience cases where using observing strategies differentfrom standard MaNGA observations are required. Theselead to dedicated plates.

We solicited ancillary proposals in all these cate-gories during the first year of survey observations, andthey started to be included in plate design half waythrough this year. Consequently, the ancillary frac-tion in DR13 is smaller than 5%. Some ancillaryprograms have tens of targets approved but only a

few got observed during the first year, while somehave no observations during this period. More tar-gets for these programs will be observed in the fu-ture. To identify ancillary targets, one should use theMANGA TARGET3 bitmask (or mngtarg3 in the dr-pall file). All ancillary targets have MANGA TARGET3greater than zero. Additional information on the scien-tific justification and targeting for each ancillary programcan be found at http://www.sdss.org/dr13/managa/manga-target-selection/ancillary-targets. Herewe provide some highlights and the corresponding bit-names.

• Luminous AGN: This program increases the num-ber of host galaxies of the most luminous activegalactic nuclei (AGN). The first source of targets isthe BAT 70-month Hard X-ray catalog (Baumgart-ner et al. 2013). These have the bitname AGN BAT.To increase the sample size further we used the[OIII]-selected catalog of Mullaney et al. (2013)(bitname AGN OIII). To match the distribution ofbolometric luminosities between the samples, weselected 5 [OIII] objects at comparable Lbol to eachBAT object, within a redshift range of 0.01-0.08.The bolometric corrections are from Shao et al.(2013) and Vasudevan & Fabian (2009).

• Edge-On Starbursts: We will use edge-on star-bursts to study the morphology and ionizationstate of large-scale outflows. To identify good tar-gets, the specific star formation rate (sSFR) andinclination of every object in the baseline MaNGAtargeting catalog was calculated. The sSFR wasdetermined using WISE photometry from Lang etal. (2016) and the calibration between the W4 filterand 22 µm emission in Jarrett et al. (2013) . Wethen use a calibration from Cluver et al. (2014) toderive the sSFR. The axis ratio SERSIC BA in thetargeting catalog was used to derive the inclination.All targets in this program have log sSFR> −8.75and inclination > 75 deg.. The four galaxies inDR13 that have these properties, but were not in-cluded in the main galaxy target sample, have bit-name EDGE ON WINDS.

• Close Pairs and Mergers: Interactions and merg-ers can play a key role in galaxy evolution, andtherefore an ancillary program was accepted thateither slightly adjusted the field centers for sometargets already included in the main galaxy sam-ple or placed new IFUs on galaxies. Close pairsare defined as galaxies in the NSA catalog or theSDSS group catalog of Yang et al. (2007) and X.Yang (private communication) with projected sep-aration < 50 kpc h−1 and line-of-sight velocity(dV) < 500 km s−1, if both redshifts are available.If the bitname is PAIR ENLARGE, then to get thefull pair required a larger IFU than the one origi-nally scheduled by the targeting algorithm (Wakeet al. 2017). If the bitname is PAIR RECENTER thismeans the original assigned MaNGA IFU is suffi-ciently large, but requires re-centering. In additionto these already-planned galaxies, two sources ofnew objects were used. The one galaxy in DR13

http://www.sdss.org/dr13/managa/manga-target-selection/ancillary-targetshttp://www.sdss.org/dr13/managa/manga-target-selection/ancillary-targets

Data Release 13 13

Fig. 4.— Principal selection cuts for the main MaNGA samples, where h = H0/100 km s−1. The colored bands show the selection

cuts for the Primary (orange) and Secondary (green) samples illustrating the Mi dependence of the redshift limits. More luminous, andhence typically larger galaxies are selected at higher redshift than less luminous galaxies, ensuring that the angular size (1.5 Re or 2.5Re) distribution is roughly independent of luminosity. The volume sampled also increases as the luminosity increases in such a way as toensure a constant number density of galaxies at all luminosities. The points show the positions in this plane for the MaNGA galaxies inDR13, for the Primary (red triangles), Secondary (green squares) and Color-Enhanced samples (blue stars), although the Color-Enhancedselection also depends on NUV-icolor (see text for details). The numbers in the legend give the total number of observations of galaxiesin each class, including repeat observations. The right hand y-axis gives an indication of the physical size of the mean spatial resolutionelement of the MaNGA data.

with PAIR SIM comes from the Galaxy Zoo Merg-ers Sample (Holincheck et al. 2016). A criticalsample comes from the ancillary program that re-quests that each galaxy be assigned a separate IFUPAIR 2IFU. Only four pairs of interacting galaxiesare serendiptiously targeted in the main MaNGAgalaxy sample with separate IFUs, and this sam-ple will compensate for the strong bias in the sin-gle IFU sample towards close separations or higherredshifts.

• Massive Nearby Galaxies: Because the largestMaNGA IFU covers 32′′, more luminous, largergalaxies observed to the same effective radius havepoorer spatial resolution. The one MASSIVE ancil-lary target in DR13 is part of a program to obtaina sample of nearby large galaxies with spatial reso-lution better than 3 kpc and similar to the faintestgalaxies in the MaNGA primary sample, at the costof spatial extent.

• Milky Way Analogs: Licquia et al. (2015) defineda sample of Milky Way analogs based on M∗, SFR,absolute magnitudes, and colors. MaNGA is in-

cluding some of these analogs in the main galaxycatalog, but they are slightly biased or deficient incertain regions of parameter space. Galaxies withthe bitname MWA are drawn from the Licquia et al.(2015) catalog to provide galaxies in those under-represented parts of parameter space.

• Dwarf Galaxies in MaNGA: The MaNGA maingalaxy sample has galaxies with M∗ > 10

9M�, butdwarf galaxies are the most numerous galaxies inthe Universe. This ancillary program provides 2dwarf galaxies with MaNGA observations in DR13,the first observations of a larger sample expected bythe end of the survey covering a range of environ-ments. These galaxies are indicated by the bitnameDWARF and are drawn from the Geha et al. (2012)galaxy catalog with stellar masses < 109M�.

• Brightest Cluster Galaxies: The brightest clustergalaxies (BGCs) targeted here are brighter andin more massive halos than BCGs already in theMaNGA main sample and have the bitname BCG.We base our target selection on the updated Yanget al. (2007) cluster catalog, created from the SDSS

14 SDSS Collaboration

Fig. 5.— The location of MaNGA galaxies (black triangles) in this data release area in the plane of stellar mass (M∗, x-axis) versus darkmatter halo mass (Mh, y-axis). The two panels show red and blue galaxies separately,which are divided by a single color cut at g− r = 0.7.Stellar masses are taken from the NASA-Sloan Atlas, and halo masses from SDSS/DR7, using the method of Yang et al. (2007). Plottedas a colormap in the background are the number of MaNGA galaxies predicted for a 6-year full survey based on mock catalogs informedby the semi-analytic model of Guo et al. (2011) and constructed as in Li et al. (2006), which are the same as Figure 3 of Bundy et al.(2015). Normalized histograms show 1D marginalized M∗ distributions (top axes) and Mh distributions (right axes), with dashed lines forthe full primary sample and solid lines for the red (left) and blue (right) populations.

DR7 NYU VAGC, an update of the DR4 versionof Blanton et al. (2005)

• MaNGA Resolved Stellar Populations: The ancil-lary program targets NGC 4163, a nearby dwarfgalaxy with existing HST imaging and high-qualitycolor-magnitude diagrams selected from the ACSNearby Galaxy Survey (Dalcanton et al. 2009).This galaxy is flagged by the bitname ANGST.

6.2. MaNGA Data Products: Individual Fiber Spectraand 3-D Data Cubes

In DR13, MaNGA is releasing both raw data (in theform of individual CCD frames) and reduced data pro-duced by version 1 5 4 of the MaNGA Data ReductionPipeline (DRP). Figure 6 illustrates the quality of thespectra from this pipeline. The MaNGA observing strat-egy is described by Law et al. (2015), and the flux cal-ibration by Yan et al. (2016). Details of the MaNGADRP, data products, and data quality are given by Lawet al. (2016) (hereafter L16). All MaNGA data are inthe form of multi-extension FITS files.

The DRP data products consist of intermediate re-duced data (sky-subtracted, flux-calibrated fiber spectrawith red and blue data combined for individual exposuresof a plate) and final-stage data products (summary row-stacked spectra and data cubes) for each target galaxy.The summary row stacked spectra (RSS files) are two-dimensional arrays provided for each galaxy in whicheach row corresponds to a single fiber spectrum. Fora 127-fiber IFU with 9 exposures, there are thus 127 ×9 = 1143 rows in the RSS file. These RSS files have ad-ditional extensions giving astrometric information aboutthe wavelength-dependent locations of each fiber on thesky.

The three-dimensional data cubes (axes R.A., Decl.,wavelength) are created by combining the individual

spectra for a given galaxy together onto a regularized0.5′′ grid (see L16 for more detail). Both data cubes andRSS files are provided in a version with a log wavelengthscale, which is the standard extraction and is relativelysmooth in velocity space, and in a version with a linearwavelength scale, created directly from the native pixelsolution rather than by resampling the log-scaled spec-tra resampling. Each MaNGA data cube has associatedextensions corresponding to the estimated inverse vari-ance per pixel and a bad-pixel mask containing informa-tion about the quality of a given pixel within the cube(depth of coverage, bad values, presence of foregroundstars, etc). Additional extensions provide informationabout the instrumental spectral resolution, individual ex-posures that went into the composite data cube, recon-structed griz broadband images created from the IFUspectra, and estimates of the griz reconstructed pointspread function.

The objects observed by MaNGA for which data cubeshave been produced are summarized in the “drpall” file,a FITS binary table with one entry per object (includingboth galaxies and spectrophotometric standard stars ob-served with 7-fiber IFUs to calibrate the MaNGA data).This drpall file lists the name, coordinates, targeting in-formation (e.g., redshift as given by the NASA SloanAtlas), reduction quality, and other quantities of inter-est to allow users to identify galaxy targets of interest.We note that MaNGA adopts two naming schemes. Thefirst, termed “mangaid” is an identifier unique to a givenastronomical object (e.g., 1-266039). The second, the“plate-ifu” uniquely identifies a given observation by con-catenating the plate id with the IFU number (e.g., 7443-12701 identifies the first 127-fiber IFU on plate 7443).Since some galaxies are observed more than once on dif-ferent plates, a given mangaid can sometimes correspondto more than one plate-ifu.

The mangaid consists of 2 parts separated by a hyphen.The first part is the id of the parent catalog from which

Data Release 13 15

TABLE 3Summary of MaNGA Ancillary Programs with Data in DR13

Ancillary Program # of Targets in DR13 1 # of Total Targets 1,2 BITNAME binary digit value

Luminous AGN 1 267 AGN BAT 1 24 AGN OIII 2 4

Edge-On Star-forming Galaxies 4 166 EDGE ON WINDS 6 64Close Pairs and Mergers 5 510 PAIR ENLARGE 7 128

10 PAIR RECENTER 8 2561 PAIR SIM 9 5121 PAIR 2IFU 10 1024

Massive Nearby Galaxies 1 310 MASSIVE 12 4096Milky Way Analogs 2 250 MWA 13 8192Dwarf Galaxies in MaNGA 2 247 DWARF 14 16384Brightest Cluster Galaxies 2 378 BCG 17 131072MaNGA Resolved Stellar Populations 1 4 ANGST 18 262144

1 An individual galaxy can be targeted by more than one ancillary program.2 Number for each Ancillary Program refers to all targets in that program, regardless of bit name

a target was selected. The second part is the positionwithin that catalog. For most galaxy targets the catalogid is 1 which refers to the NSA. For a small number ofthe early targets the catalog id is 12 and refers to anearlier version of the NSA (v1b 0 2). All galaxies fromthis earlier version of the NSA are also in the final versionand so we release photometry etc for those targets fromthe final version of the NSA (v1 0 1), which is includedin the data release. Other catalogs referred to in the firstpart of the manga-id are for SDSS standard stars.

The full data model for all MaNGA DRP data productscan be found online at http://www.sdss.org/dr13/manga/manga-data/data-model/ and is also given inAppendix B of L16.

6.3. Retrieving MaNGA Data

The raw data, reduced data, RSS, and 3-D data cubesfor 1351 MaNGA galaxies are provided in DR13. Fromthese data products, maps of line ratios, spectroscopicindices, and kinematics can be made using standard soft-ware. Because the first step in using the MaNGA datafor science is to retrieve the spectra, we detail here andon the SDSS website4 how to access the MaNGA spectra.

6.3.1. Reduced Data Products

MaNGA DR13 reduced data products are stored on theScience Archive Server at http://data.sdss.org/sas/dr13/manga/spectro/redux/v1_5_4/. The top level di-rectory contains the summary drpall FITS table andsubdirectories for each plate. Inside each plate direc-tory there are subdirectories for each MJD on which theplate was observed, containing intermediate (exposurelevel) data products. The ‘stack’ subdirectory withineach plate directory contains the final RSS and cube filesfor each MaNGA galaxy, sorted according to their plate-ifu identifiers. Note that the ifu identifier in the filenamesindicates the size of the IFU; everything in the 127 series(e.g., 12701) is a 127-fiber bundle, etc. The 700 seriesifus (e.g., 701) are the twelve spectrophotometric 7-fiberminibundles that target stars on each plate.

These are the ways of getting at the data in DR13:

• Direct html browsing of the SAS at the abovelink. The file drpall-v1 5 4.fits can be downloaded

4 http:www.sdss.org/dr13/manga/manga-data/data-access/

through the web browser and queried using anyprogram able to read FITS binary tables. Once aset of galaxies of interest has been identified, in-dividual data cubes, summary RSS files, interme-diate data products, etc. can be downloaded bybrowsing through the web directory tree.

• Large downloads of many DRP data prod-ucts can be automated using rsync accessto the SAS. For instance, to download allMaNGA data cubes with a logarithmic wave-length format: \rsync-aLrvz--include"*/"--include"manga*LOGCUBE.fits.gz"--exclude"*"rsync://data.sdss.org/dr13/manga/spectro/redux/v1_5_4/./

• The MaNGA drpall file can also be queried on-line using the SDSS CASJobs system at http://skyserver.sdss.org/casjobs. While this canbe useful for identifying MaNGA observations ofinterest, CASJobs does not contain links to theMaNGA data cubes and another method must beused to download the data themselves.

• The SDSS SkyServer Explore tool at http://skyserver.sdss.org/dr13/en/tools/explore/will display basic information about MaNGAobservations in DR13 that fall within a given conesearch on the sky. The relevant explore pages alsoprovide direct links to the FITS data cubes on theSAS.

6.3.2. Raw Data

All MaNGA data taken in the first year of SDSS-IV observations are part of Data Release 13, in-cluding data from special ancillary plates and co-observing during APOGEE-2 time that are not partof the DRP results. The raw data are stored onthe SAS in the directory http://data.sdss.org/sas/dr13/manga/spectro/data/ in subdirectories based onthe MJD when the data were taken. The mangacoredirectory5 contains the information needed to figure outthe RA and Dec positions of fibers on plates, the ditheredMJDs to be combined to make the final spectrum in

5 http://svn.sdss.org/public/repo/manga/mangacore/tags/v1_2_3/

http://www.sdss.org/dr13/manga/manga-data/data-model/http://www.sdss.org/dr13/manga/manga-data/data-model/http://data.sdss.org/sas/dr13/manga/spectro/redux/v1_5_4/http://data.sdss.org/sas/dr13/manga/spectro/redux/v1_5_4/http:www.sdss.org/dr13/manga/manga-data/data-access/\ rsync -aLrvz --include "*/" --include "manga*LOGCUBE.fits.gz" --exclude "*" rsync://data.sdss.org/dr13/manga/spectro/redux/v1_5_4/ ./\ rsync -aLrvz --include "*/" --include "manga*LOGCUBE.fits.gz" --exclude "*" rsync://data.sdss.org/dr13/manga/spectro/redux/v1_5_4/ ./\ rsync -aLrvz --include "*/" --include "manga*LOGCUBE.fits.gz" --exclude "*" rsync://data.sdss.org/dr13/manga/spectro/redux/v1_5_4/ ./\ rsync -aLrvz --include "*/" --include "manga*LOGCUBE.fits.gz" --exclude "*" rsync://data.sdss.org/dr13/manga/spectro/redux/v1_5_4/ ./http://skyserver.sdss.org/casjobshttp://skyserver.sdss.org/casjobshttp://skyserver.sdss.org/dr13/en/tools/explore/http://skyserver.sdss.org/dr13/en/tools/explore/http://data.sdss.org/sas/dr13/manga/spectro/data/http://data.sdss.org/sas/dr13/manga/spectro/data/http://svn.sdss.org/public/repo/manga/mangacore/tags/v1_2_3/http://svn.sdss.org/public/repo/manga/mangacore/tags/v1_2_3/

16 SDSS Collaboration

apocomplete directory, and information on the calibra-tion files. L16 and the http://www.sdss.org/dr13/manga/manga-data/metadata/ website contain the rel-evant information about the file formats and the use ofthe calibration files to get to the fully reduced spectra.Because these files are prepared for internal use, they re-tain many old features that should be ignored, such asthe names assigned to targeting bits, which still retainthe names from SDSS-I.

6.4. Notes on using MaNGA data

There are several important caveats to keep in mindwhen working with MaNGA data. In this discussion wetreat only the MaNGA data cubes. Summary RSS, inter-mediate, and even raw data products present some statis-tical advantages to the data cubes, in particular reducedcovariance between adjacent data points and greater easeof forward modeling, but are substantially harder to use.

First and foremost, each MaNGA data cube has a FITSheader keyword DRP3QUAL indicating the quality ofthe reduction. 1-2% of the data cubes are flagged as sig-nificantly problematic for various reasons, ranging frompoor focus to flux calibration problems. Table B13 inL16 lists the bits that can be set with this flag that de-scribe why the end product was deemed unsatisfactory.Galaxies for which the CRITICAL quality bit (=30) is setin DRP3QUAL should be treated with extreme caution.While there may be some spaxels in that data cube thatare acceptable, in general the CRITICAL bit indicateswidespread problems with the data reduction. Each datacube also has an associated MASK cube describing thequality of individual spaxels in the data cube. ThisMASK extension can be used to identify areas of no cov-erage outside the fiber bundle footprint6, low coveragenear the edge of the dithered footprint, problematic ar-eas due to detector artifacts, regions known to containbright Milky Way foreground stars, etc. A simple sum-mary DONOTUSE bit is of particular importance indicatingelements that should be masked out for scientific analy-ses.

Since the MaNGA data cubes adopt a 0.5′′ samplingsize (chosen based on Fourier analysis in optimal observ-ing conditions to not truncate the high-k modes of theobservational PSF), while individual fibers have a 2′′ di-ameter footprint, there is significant covariance betweenadjacent MaNGA spaxels that must be taken into ac-count whenever combining spectra. A simplified methodfor doing this is discussed in §9.3 of L16. The typicalreconstructed point spread function of the MaNGA datacubes has FWHM of 2.5′′.

As discussed by L16, the instrumental line spread func-tion (LSF) in the wavelength direction reported by thevarious extensions within the MaNGA DR13 data prod-ucts is underestimated by about 10 ± 2%. This cor-rection is comparable to the errors in the reported res-olution of the BOSS spectrographs seen in single-fiberwork (e.g., Shu et al. 2012; Palanque-Delabrouille et al.2013). Although this makes little difference when deter-mining the astrophysical width of broad spectral lines,

6 The fiber footprint is a hexagon, but the standard FITS imagedata structure is based around rectangular arrays. There musttherefore be blank areas around the live IFU footprint in order toinscribe the hexagon inside a bounding rectangle.

it is important to account for when attempting to sub-tract the instrumental resolution from barely-resolvedlines. There are two effects that combine to producethis overestimate. The first is that the impact of thewavelength rectification on the effective spectral resolu-tion was not accounted for when combining spectra fromblue and red cameras. The second is that the Gaussianwidth of the LSF reported by the DRP is strictly thewidth of a pre-pixellization gaussian, while most end-user analysis routines adopt post-pixellization gaussiansinstead (i.e., the different between integrating a gaus-sian profile over the pixel boundaries vs evaluating agaussian at the pixel midpoint). This will be treatedmore completely in a future data release; in the mean-time a 10% correction to the instrumental LSF quotedby the MaNGA data products appears to be a reason-able correction factor if using post-pixellization analy-sis routines. However, because this correction factor it-self is uncertain, derived astrophysical line-widths sub-stantially below the instrumental resolution should beviewed to have unreliable accuracy at this time. A fulldiscussion of issues to consider is available at http://www.sdss.org/dr13/manga/manga-caveats/.

6.5. Highlights of MaNGA Science with DR13 Data

The MaNGA survey has produced a number of scien-tific results based on early data, indicating the breadth ofresearch possible with the MaNGA data. Here we brieflysummarize the results of papers completed within theSDSS-IV collaboration using MaNGA data on spatiallyresolved gas physics, stellar population properties, andstellar and gas kinematics. These MaNGA papers serveas a guide to prospective users of SDSS-IV data in thesespecific science areas. The larger context for this scienceis provided to a small degree in the Introduction, andthe papers here also provide citations to the extensiveliterature on each topic, to which we refer the interestedreader for additional context. Citations to the sciencein each area should refer to the original papers, whetherMaNGA-based or not, rather than this brief executivesummary.

6.5.1. Gas physics

The spatially resolved emission-line measurementshave been used to understand the physical conditions ofthe ionized gas in galaxies. Cheung et al. (2016a) iden-tified AGN winds as a surprisingly common occurrencein normal, quiescent galaxies, suggesting these winds aspotentially critical in suppressing star formation. Thesewinds may help address the question of how star forma-tion remains suppressed in early-type galaxies. Cheunget al. (2016a) report bisymmetric emission features co-aligned with strong ionized-gas velocity gradients in agalaxy from which they infer the presence of centrallydriven winds in typical quiescent galaxies that host low-luminosity active nuclei. These galaxies account for asmuch as ten per cent of the quiescent population withmasses around 2× 1010 M�. They calculate that the en-ergy input from the galaxies’ low-level active supermas-sive black hole is capable of driving the observed wind,which contains sufficient mechanical energy to heat am-bient, cooler gas (also detected) and thereby suppressstar formation.

http://www.sdss.org/dr13/manga/manga-data/metadata/http://www.sdss.org/dr13/manga/manga-data/metadata/http://www.sdss.org/dr13/manga/manga-caveats/http://www.sdss.org/dr13/manga/manga-caveats/

Data Release 13 17

Fig. 6.— Example spectra from a typical MaNGA data cube, adapted from Yan et al. (2016). The inset shows the SDSS color imagewith the hexagonal IFU footprint overlayed. The top spectrum is from the central spaxel; the bottom spectrum from a spaxel 1.2 Refrom the center and is multiplied by a factor of 6 for easier comparison with the central spectrum. The differences in the stellar and gascomponents between the two regions can clearly be seen, as well as the large number of diagnostic features to understand the star formationhistory and the physical conditions of the gas.

The broader nature of ionized gas in early-types hasalso been the subject of papers by Belfiore et al. (2016)and Belfiore et al. (2017) following up on the analysisof a small sample observed with the MaNGA prototypeinstrument in Belfiore et al. (2015). By using spatiallyresolved maps of nebular diagnostics and stellar popula-tion ages, this work has added substantial support to thenotion that evolved stellar populations provide the ion-ization source for a galaxy class that arguably should berenamed from LINER (Low Ionization Nuclear EmissionRegion) galaxies to LIER galaxies. LIERs, it turns out,are ubiquitous in both quiescent galaxies and in the cen-tral regions of galaxies where star formation takes placeat larger radii. The study of Belfiore et al. (2016) andBelfiore et al. (2017) have put the occurrence of the LIERphenomenon into a physically relevant framework thatcan be directly tied to the diversity of the galaxy popu-lation as a whole. Specifically, they identify two classesof galaxies as extended LIER (eLIER) and central LIER(cLIER), respectively, and study their kinematics andstellar population properties. cLIERs turn out to bemostly late type galaxies located around the green val-ley, while eLIERs are morphologically early types andare indistinguishable from passive galaxies devoid of lineemission in terms of their stellar populations, morphol-ogy and central stellar velocity dispersion.

The widespread ionization state of LIER gas mightoriginally manifest as the Diffuse Intergalactic Gas (DIG)which is intermixed with star-forming regions. Zhanget al. (2016) studied galactic DIG emission and demon-strate how DIG in star-forming galaxies impacts the mea-surements of emission line ratios at the spatial resolu-

tion of MaNGA, hence the interpretation of diagnosticdiagrams and the gas-phase metallicity measurements.They quantify for the MaNGA data how the contam-ination by DIG moves HII regions towards compositeof LIER-like regions. DIG significantly biases measure-ments of gas metallicity and metallicity gradients becauseat different surface brightness, line ratios and line ratiogradients can differ systematically.

A careful treatment of gas-phase metallicities inferredfrom spectral maps of galaxies has suggested a key rolefor the dependence of metallicity on local stellar masssurface density. Barrera-Ballesteros et al. (2016) presentthe stellar surface mass density vs gas metallicity relationfor more than 500,000 spatially-resolved star-forming re-gions from a sample of 653 disk galaxies. These galaxiesspan a larger range in mass than in previous sampleswhere the correlations were first discovered. They con-firm a tight relation between these local properties, withhigher metallicities as the surface density increases. Theyfind that even over this larger mass range this local re-lationship can simultaneously reproduce two well-knownproperties of disk galaxies: their global mass-metallicityrelationship and their radial metallicity gradients. Theirresults support the idea that in the present-day universelocal properties play a key role in determining the gas-phase metallicity in typical disk galaxies.

However, Cheung et al. (2016b) have found a galaxy inthe middle of a gas accretion event, providing a detailedlook at what appears to be a relatively rare occurrence inthe nearby Universe of this mode of galaxy growth. Theypresent serendipitous observations of a large, asymmet-ric Hα complex that extends ∼ 8′′ (∼ 6.3 kpc) beyond

18 SDSS Collaboration

the effective radius of a dusty, starbursting galaxy. ThisHα extension is approximately three times the effectiveradius of the host galaxy and displays a tail-like morphol-ogy. This is suggestive of an inflow, which is consistentwith its relatively lower gas-phase metallicities and itsirregular gaseous kinematics.

6.5.2. Stellar populations

Spatially resolved stellar population properties andstellar growth histories have been analyzed, following theanalysis of a small sample observed with the MaNGAprototype instrument by Wilkinson et al. (2015) and Liet al. (2015). MaNGA has explored the role of environ-ment in shaping the radial distribution of stellar agesand metallicities, with particular attention given to thepotential measurement systematics. Using different spec-tral fitting techniques and complementary environmentalmetrics, both Goddard et al. (2017b) and Zheng et al.(2017) conclude that any environmental signal in the av-erage shape of gradients is weak at best, with no obvioustrends emerging in the initial MaNGA data.

Goddard et al. (2017a) studied the internal gradientsof the stellar population age and metallicity within 1.5Re obtained from full spectral fitting and confirm sev-eral key results of previous surveys. Age gradients tendto be shallow for both early-type and late-type galax-ies. As well known from previous studies, metallicitygradients are often complex (and not well-modeled bylinear or log-linear functions of radius), varying in detailfrom galaxy to galaxy on small radial scales. On aver-age, however, over radial scales of order 1 Re, MaNGAdata provide the strongest statistical constraints to datethat metallicity gradients are negative in both early andlate-type galaxies, and are significantly steeper in disks.These results continue to indicate that the radial depen-dence of chemical enrichment processes are far more pro-nounced in disks than they are in spheroids, and indeedthe relatively flat gradients in early-type galaxies are in-consistent with monolithic collapse. For both early andlate-type galaxies, more massive galaxies have steepernegative metallicity and age gradients. Since early-typegalaxies tend to be more luminous, the overall steeperage and metallicity gradients in late-type galaxies reflectthe fact that the trends in these gradients with galaxymass are more pronounced for late-type galaxies. God-dard et al. (2017a) take advantage of the unique MaNGAsample size and mass range to characterize this correla-tion between metallicity and age gradients and galaxymass, which explains the scatter in gradients values seenin previous studies.

Ibarra-Medel et al. (2016) meanwhile infer spatially-resolved stellar mass assembly histories for the MaNGAgalaxies, extending previously known relations betweengalaxy type and assembly history to a larger massrange. Their findings are consistent with blue/star-forming/late-type galaxies assembling, on average, frominside to out. Red/quiescent/early-type galaxies presenta more uniform spatial mass assembly, or at leastone that has been dynamically well mixed since star-formation ceased, consistent with the flatter gradientsseen, e.g., Goddard et al. (2017a). In general, low-massgalaxies show evidence of more irregular global and spa-tial assembly histories than massive galaxies.

In a deve