# Validation of the Scientific Program for the Dark Energy Spectroscopic Instrument

A.G Adame, J Aguilar, S Ahlen, S Alam, G Aldering, D.M Alexander, R

Alfarsy, C. Allende Prieto, M Alvarez, O Alves, et al.

► To cite this version:

A.G Adame, J Aguilar, S Ahlen, S Alam, G Aldering, et al. Validation of the Scientific Program for the Dark Energy Spectroscopic Instrument. Astron.J., 2024, 167 (2), pp.62. 10.3847/1538-3881/ad0b08 . hal-04127783

HAL Id: hal-04127783

https://hal.science/hal-04127783

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# Validation of the Scientific Program for the Dark Energy Spectroscopic Instrument

A.G Adame, J Aguilar, S Ahlen, S Alam, G Aldering, D.M Alexander, R

Alfarsy, C. Allende Prieto, M Alvarez, O Alves, et al.

► To cite this version:

A.G Adame, J Aguilar, S Ahlen, S Alam, G Aldering, et al. Validation of the Scientific Program for the Dark Energy Spectroscopic Instrument. Astron.J., 2024, 167 (2), pp.62. 10.3847/1538-3881/ad0b08 . hal-04127783

HAL Id: hal-04127783

https://hal.science/hal-04127783v1

Submitted on 6 Feb 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

# Validation of the Scientific Program for the Dark Energy Spectroscopic Instrument

DESI Collaboration,

A. G. Adame<fnref n="1" />, J. Aguilar<fnref n="2" />, S. Ahlen<fnref n="3" />, S. Alam<fnref n="4" />, G. Aldering<fnref n="2" />, D. M. Alexander<fnref n="5" />,<fnref n="6" />, R. Alfarsy<fnref n="7" />, C. Allende Prieto<fnref n="8" />,<fnref n="9" />, M. Alvarez<fnref n="2" />, O. Alves<fnref n="10" />, A. Anand<fnref n="2" />, F. Andrade-Oliveira<fnref n="10" />, E. Armengaud<fnref n="11" />, J. Asorey<fnref n="12" />, S. Avila<fnref n="13" />, A. Aviles<fnref n="14" />,<fnref n="15" />, S. Bailey<fnref n="2" />, A. Balaguera-Antolínez<fnref n="8" />,<fnref n="9" />, O. Ballester<fnref n="13" />, C. Baltay<fnref n="16" />, A. Bault<fnref n="17" />, J. Bautista<fnref n="18" />, J. Behera¹⁹, S. F. Beltran²⁰, S. BenZvi²¹, L. Beraldo e Silva<fnref n="10" />,²², J. R. Bermejo-Climent²¹, A. Berti²³, R. Besuner²⁴,²⁵, F. Beutler²⁶, D. Bianchi²⁷, C. Blake²⁸, R. Blum²⁹, A. S. Bolton²⁹, S. Brieden²⁶, A. Brodzeller²³, D. Brooks³⁰, Z. Brown²¹, E. Buckley-Geer³¹,³², E. Burtin<fnref n="11" />, L. Cabayol-Garcia<fnref n="13" />, Z. Cai³³,³⁴,³⁵, R. Canning<fnref n="7" />, L. Cardiel-Sas<fnref n="13" />, A. Carnero Rosell<fnref n="8" />,<fnref n="9" />, F. J. Castander³⁶,³⁷, J. L. Cervantes-Cota<fnref n="15" />, S. Chabanier<fnref n="2" />, E. Chaussidon<fnref n="11" />, J. Chaves-Montero<fnref n="13" />, S. Chen³⁸, X. Chen<fnref n="16" />, C. Chuang²³,³⁹,⁴⁰, T. Claybaugh<fnref n="2" />, S. Cole<fnref n="6" />, A. P. Cooper⁴¹, A. Cuceu⁴²,⁴³,⁴⁴, T. M. Davis⁴⁵, K. Dawson²³, R. de Belsunce<fnref n="2" />,⁴⁶, R. de la Cruz²⁰, A. de la Macorra⁴⁷, A. de Mattia<fnref n="11" />, R. Demina²¹, U. Demirbozan<fnref n="13" />, J. DeRose<fnref n="2" />, A. Dey²⁹, B. Dey⁴⁸, G. Dhungana⁴⁹, J. Ding³⁵, Z. Ding⁵⁰, P. Doel³⁰, R. Doshi⁵¹, K. Douglass²¹, A. Edge<fnref n="6" />, S. Eftekharzadeh⁵², D. J. Eisenstein⁵³, A. Elliott⁴³,⁴⁴, S. Escoffier<fnref n="18" />, P. Fagrelius²⁹, X. Fan⁵⁴, K. Fanning⁴⁴, V. A. Fawcett⁵⁵, S. Ferraro<fnref n="2" />,²⁵, J. Ereza⁵⁶, B. Flaugher³², A. Font-Ribera<fnref n="13" />, D. Forero-Sánchez⁵⁷, J. E. Forero-Romero⁵⁸,⁵⁹, C. S. Frenk<fnref n="6" />, B. T. Gänsicke⁶⁰, L. Á. García⁶¹, J. García-Bellido<fnref n="1" />, C. Garcia-Quintero⁶², L. H. Garrison⁶³,⁶⁴, H. Gil-Marín⁶⁵, J. Golden-Marx⁵⁰, S. Gontcho A Gontcho<fnref n="2" />, A. X. Gonzalez-Morales<fnref n="14" />,²⁰, V. Gonzalez-Perez<fnref n="1" />,⁶⁶, C. Gordon<fnref n="13" />, O. Graur<fnref n="7" />, D. Green<fnref n="17" />, D. Gruen⁶⁷,⁶⁸, J. Guy<fnref n="2" />, B. Hadzhiyska<fnref n="2" />,²⁵, C. Hahn⁶⁹, J. J. Han⁵³, M. M. S Hanif<fnref n="10" />, H. K. Herrera-Alcantara²⁰, K. Honscheid⁴²,⁴³,⁴⁴, J. Hou⁷⁰, C. Howlett⁴⁵, D. Huterer<fnref n="10" />,⁷¹, V. Iršič⁴⁶, M. Ishak⁶², A. Jana¹⁹, L. Jiang⁷², J. Jimenez<fnref n="13" />, Y. P. Jing⁵⁰, S. Joudaki⁷³, E. Jullo⁷⁴, R. Joyce²⁹, S. Juneau²⁹, N. Kizhuprakkat⁴¹, N. G. Karaçaylı⁴²,⁴³,⁴⁴,⁷⁵, T. Karim⁵³, R. Kehoe⁴⁹, S. Kent³¹,³², A. Khederlarian⁴⁸, S. Kim⁷⁶, D. Kirkby<fnref n="17" />, T. Kisner<fnref n="2" />, F. Kitaura<fnref n="8" />,<fnref n="9" />, J. Kneib⁵⁷, S. E. Koposov²⁶,⁷⁷, A. Kovács⁷⁸,⁷⁹, A. Kremin<fnref n="2" />, A. Krolewski⁷³,⁸⁰,⁸¹, B. L'Huillier⁸², O. Lahav³⁰, A. Lambert<fnref n="2" />, C. Lamman⁵³, T.-W. Lan⁸³, M. Landriau<fnref n="2" />, D. Lang⁸⁰, J. U. Lange<fnref n="10" />,⁷¹, J. Lasker⁴⁹, L. Le Guillou⁸⁴, A. Leauthaud³³,³⁵, M. E. Levi<fnref n="2" />, T. S. Li⁸⁵, E. Linder<fnref n="2" />,²⁴,²⁵, A. Lyons⁸⁶, C. Magneville<fnref n="11" />, M. Manera<fnref n="13" />,⁸⁷, C. J. Manser⁶⁰,⁸⁸, D. Margala<fnref n="2" />, P. Martini⁴²,⁴⁴,⁷⁵, P. McDonald<fnref n="2" />, G. E. Medina⁸⁵, L. Medina-Varela⁶², A. Meisner²⁹, J. Mena-Fernández<fnref n="12" />, J. Meneses-Rizo⁴⁷, M. Mezcua³⁶,³⁷, R. Miquel<fnref n="13" />,⁸⁹, P. Montero-Camacho³⁴, J. Moon⁸², S. Moore<fnref n="6" />, J. Moustakas⁹⁰, E. Mueller⁹¹, J. Mundet<fnref n="13" />, A. Muñoz-Gutiérrez⁴⁷, A. D. Myers⁹², S. Nadathur<fnref n="7" />, L. Napolitano⁹², R. Neveux²⁶, J. A. Newman⁴⁸, J. Nie⁹³, G. Niz²⁰,⁹⁴, P. Norberg<fnref n="5" />,<fnref n="6" />, H. E. Noriega⁴⁷, E. Paillas⁷³, N. Palanque-Delabrouille<fnref n="2" />,<fnref n="11" />, A. Palmese⁹⁵, P. Zhiwei⁷², D. Parkinson⁹⁶, S. Penmetsa⁷³, W. J. Percival⁷³,⁸⁰,⁸¹, A. Pérez-Fernández⁴⁷, I. Pérez-Ràfols⁹⁷, M. Pieri⁷⁴, C. Poppett<fnref n="2" />,²⁴,²⁵, A. Porredon²⁶,⁴⁴, F. Prada⁵⁶, R. Pucha⁵⁴, A. Raichoor<fnref n="2" />, C. Ramírez-Pérez<fnref n="13" />, S. Ramirez-Solano⁴⁷, M. Rashkovetskyi⁵³, C. Ravoux<fnref n="11" />,<fnref n="18" />, A. Rocher<fnref n="11" />, C. Rockosi³³,³⁵,⁹⁸, A. J. Ross⁴²,⁴⁴,⁷⁵, G. Rossi⁸², R. Ruggeri²⁸,⁴⁵, V. Ruhlmann-Kleider<fnref n="11" />, C. G. Sabiu⁷⁶, K. Said⁴⁵, A. Saintonge³⁰, L. Samushia¹⁹,⁹⁹,¹⁰⁰, E. Sanchez<fnref n="12" />, C. Saulder⁹⁶, E. Schaan⁴⁰, E. F. Schlafly¹⁰¹, D. Schlegel<fnref n="2" />, D. Scholte³⁰, M. Schubnell<fnref n="10" />,⁷¹, H. Seo¹⁰², A. Shafieloo⁹⁶, R. Sharples<fnref n="6" />,¹⁰³, W. Sheu¹⁰⁴, J. Silber<fnref n="2" />, F. Sinaglia<fnref n="2" />,⁸⁹, M. Siudek³⁷, Z. Slepian<fnref n="2" />,⁷⁰, A. Smith<fnref n="6" />, D. Sprayberry²⁹, L. Stephey<fnref n="2" />, J. Suárez-Pérez⁵⁸, Z. Sun³⁴, T. Tan⁸⁴, G. Tarle<fnref n="10" />, R. Tojeiro¹⁰⁵, L. A. Ureña-López²⁰, R. Vaisakh⁴⁹, D. Valcin¹⁰², F. Valdes²⁹, M. Valluri<fnref n="10" />,²², M. Vargas-Magaña⁴⁷, A. Variu⁵⁷, L. Verde⁶⁵,⁸⁹, M. Walther⁶⁷,⁶⁸, B. Wang³⁴,¹⁰⁶, M. S. Wang²⁶, B. A. Weaver²⁹, N. Weaverdyck<fnref n="2" />, R. H. Wechsler³⁹,⁴⁰,¹⁰⁷, M. White²⁵,⁵¹, Y. Xie⁶², J. Yang⁵⁴, C. Yèche<fnref n="11" />, J. Yu⁵⁷, S. Yuan⁴⁰, H. Zhang¹⁹, Z. Zhang⁵¹, C. Zhao³⁴,⁵⁷, Z. Zheng²³, R. Zhou<fnref n="2" />, Z. Zhou⁹³, H. Zou⁷³, S. Zou³⁴, and Y. Zu⁴²,⁵⁰,¹⁰⁸

<footnote n="1">¹ Instituto de Física Teórica (IFT) UAM/CSIC, Universidad Autónoma de Madrid, Cantoblanco, E-28049, Madrid, Spain; spokespersons@desi.lbl.gov</footnote>

<footnote n="2">²Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA</footnote>

<footnote n="3">³Physics Dept., Boston University, 590 Commonwealth Avenue, Boston, MA 02215, USA</footnote>

<footnote n="4">⁴Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai 400005, India</footnote>

<footnote n="5">⁵Centre for Extragalactic Astronomy, Department of Physics, Durham University, South Road, Durham, DH1 3LE, UK</footnote>

<footnote n="6">⁶Institute for Computational Cosmology, Department of Physics, Durham University, South Road, Durham, DH1 3LE, UK</footnote>

<footnote n="7">⁷Institute of Cosmology & Gravitation, University of Portsmouth, Dennis Sciama Building, Portsmouth, PO1 3FX, UK</footnote>

<footnote n="8">⁸Departamento de Astrofísica, Universidad de La Laguna (ULL), E-38206, La Laguna, Tenerife, Spain</footnote>

<footnote n="9">⁹Instituto de Astrofísica de Canarias, C/ Vía Láctea, s/n, E-38205 La Laguna, Tenerife, Spain</footnote>

<footnote n="10">¹⁰University of Michigan, Ann Arbor, MI 48109, USA</footnote>

<footnote n="11">¹¹IRFU, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France</footnote>

<footnote n="12">¹²CIEMAT, Avenida Complutense 40, E-28040 Madrid, Spain</footnote>

<footnote n="13">¹³Institut de Física d'Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, E-08193 Bellaterra, Barcelona, Spain</footnote>

<footnote n="14">¹⁴Consejo Nacional de Ciencia y Tecnología, Av. Insurgentes Sur 1582. Colonia Crédito Constructor, Del. Benito Juárez C.P. 03940, México D.F. México</footnote>

<footnote n="15">¹⁵Departamento de Física, Instituto Nacional de Investigaciones Nucleares, Carretera México-Toluca S/N, La Marquesa, Ocoyoacac, Edo. de México C.P. 52750, México</footnote>

<footnote n="16">¹⁶Physics Department, Yale University, P.O. Box 208120, New Haven, CT 06511, USA</footnote>

<footnote n="17">¹⁷Department of Physics and Astronomy, University of California, Irvine, CA 92697, USA</footnote>

<footnote n="18">¹⁸Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France</footnote>
<figure><img src="image_3.png" /></figure>

Validation of the Scientific Program for the Dark Energy Spectroscopic Instrument

DESI Collaboration,

A. G. Adame<fnref n="1" />, J. Aguilar<fnref n="2" />, S. Ahlen<fnref n="3" />, S. Alam<fnref n="4" />, G. Aldering<fnref n="2" />, D. M. Alexander<fnref n="5" />,<fnref n="6" />, R. Alfarsy<fnref n="7" />, C. Allende Prieto<fnref n="8" />,<fnref n="9" />, M. Alvarez<fnref n="2" />, O. Alves¹⁰, A. Anand<fnref n="2" />, F. Andrade-Oliveira¹⁰, E. Armengaud<fnref n="11" />, J. Asorey<fnref n="12" />, S. Avila<fnref n="13" />, A. Aviles<fnref n="14" />,<fnref n="15" />, S. Bailey<fnref n="2" />, A. Balaguera-Antolínez<fnref n="8" />,<fnref n="9" />, O. Ballester<fnref n="13" />, C. Baltay<fnref n="16" />, A. Bault<fnref n="17" />, J. Bautista<fnref n="18" />, J. Behera¹⁹, S. F. Beltran²⁰, S. BenZvi²¹, L. Beraldo e Silva¹⁰,²², J. R. Bermejo-Climent²¹, A. Berti²³, R. Besuner²⁴,²⁵, F. Beutler²⁶, D. Bianchi²⁷, C. Blake²⁸, R. Blum²⁹, A. S. Bolton²⁹, S. Brieden²⁶, A. Brodzeller²³, D. Brooks³⁰, Z. Brown²¹, E. Buckley-Geer³¹,³², E. Burtin<fnref n="11" />, L. Cabayol-Garcia<fnref n="13" />, Z. Cai³³,³⁴,³⁵, R. Canning<fnref n="7" />, L. Cardiel-Sas<fnref n="13" />, A. Carnero Rosell<fnref n="8" />,<fnref n="9" />, F. J. Castander³⁶,³⁷, J. L. Cervantes-Cota<fnref n="15" />, S. Chabanier<fnref n="2" />, E. Chaussidon<fnref n="11" />, J. Chaves-Montero<fnref n="13" />, S. Chen³⁸, X. Chen<fnref n="16" />, C. Chuang²³,³⁹,⁴⁰, T. Claybaugh<fnref n="2" />, S. Cole<fnref n="6" />, A. P. Cooper⁴¹, A. Cuceu⁴²,⁴³,⁴⁴, T. M. Davis⁴⁵, K. Dawson²³, R. de Belsunce<fnref n="2" />,⁴⁶, R. de la Cruz²⁰, A. de la Macorra⁴⁷, A. de Mattia<fnref n="11" />, R. Demina²¹, U. Demirbozan<fnref n="13" />, J. DeRose<fnref n="2" />, A. Dey²⁹, B. Dey⁴⁸, G. Dhungana⁴⁹, J. Ding³⁵, Z. Ding⁵⁰, P. Doel³⁰, R. Doshi⁵¹, K. Douglass²¹, A. Edge<fnref n="6" />, S. Eftekharzadeh⁵², D. J. Eisenstein⁵³, A. Elliott⁴³,⁴⁴, S. Escoffier<fnref n="18" />, P. Fagrelius²⁹, X. Fan⁵⁴, K. Fanning⁴⁴, V. A. Fawcett⁵⁵, S. Ferraro<fnref n="2" />,²⁵, J. Ereza⁵⁶, B. Flaugher³², A. Font-Ribera<fnref n="13" />, D. Forero-Sánchez⁵⁷, J. E. Forero-Romero⁵⁸,⁵⁹, C. S. Frenk<fnref n="6" />, B. T. Gänsicke⁶⁰, L. Á. García⁶¹, J. García-Bellido<fnref n="1" />, C. Garcia-Quintero⁶², L. H. Garrison⁶³,⁶⁴, H. Gil-Marín⁶⁵, J. Golden-Marx⁵⁰, S. Gontcho A Gontcho<fnref n="2" />, A. X. Gonzalez-Morales<fnref n="14" />,²⁰, V. Gonzalez-Perez<fnref n="1" />,⁶⁶, C. Gordon<fnref n="13" />, O. Graur<fnref n="7" />, D. Green<fnref n="17" />, D. Gruen⁶⁷,⁶⁸, J. Guy<fnref n="2" />, B. Hadzhiyska<fnref n="2" />,²⁵, C. Hahn⁶⁹, J. J. Han⁵³, M. M. S Hanif¹⁰, H. K. Herrera-Alcantara²⁰, K. Honscheid⁴²,⁴³,⁴⁴, J. Hou⁷⁰, C. Howlett⁴⁵, D. Huterer¹⁰,⁷¹, V. Iršič⁴⁶, M. Ishak⁶², A. Jana¹⁹, L. Jiang⁷², J. Jimenez<fnref n="13" />, Y. P. Jing⁵⁰, S. Joudaki⁷³, E. Jullo⁷⁴, R. Joyce²⁹, S. Juneau²⁹, N. Kizhuprakkat⁴¹, N. G. Karaçaylı⁴²,⁴³,⁴⁴,⁷⁵, T. Karim⁵³, R. Kehoe⁴⁹, S. Kent³¹,³², A. Khederlarian⁴⁸, S. Kim⁷⁶, D. Kirkby<fnref n="17" />, T. Kisner<fnref n="2" />, F. Kitaura<fnref n="8" />,<fnref n="9" />, J. Kneib³⁷, S. E. Koposov²⁶,⁷⁷, A. Kovács⁷⁸,⁷⁹, A. Kremin<fnref n="2" />, A. Krolewski⁷³,⁸⁰,⁸¹, B. L'Huillier⁸², O. Lahav³⁰, A. Lambert<fnref n="2" />, C. Lamman⁵³, T.-W. Lan⁸³, M. Landriau<fnref n="2" />, D. Lang⁸⁰, J. U. Lange¹⁰,⁷¹, J. Lasker⁴⁹, L. Le Guillou⁸⁴, A. Leauthaud³³,³⁵, M. E. Levi<fnref n="2" />, T. S. Li⁸⁵, E. Linder<fnref n="2" />,²⁴,²⁵, A. Lyons⁸⁶, C. Magneville<fnref n="11" />, M. Manera<fnref n="13" />,⁸⁷, C. J. Manser⁶⁰,⁸⁸, D. Margala<fnref n="2" />, P. Martini⁴²,⁴⁴,⁷⁵, P. McDonald<fnref n="2" />, G. E. Medina⁸⁵, L. Medina-Varela⁶², A. Meisner²⁹, J. Mena-Fernández<fnref n="12" />, J. Meneses-Rizo⁴⁷, M. Mezcua³⁶,³⁷, R. Miquel<fnref n="13" />,⁸⁹, P. Montero-Camacho³⁴, J. Moon⁸², S. Moore<fnref n="6" />, J. Moustakas⁹⁰, E. Mueller⁹¹, J. Mundet<fnref n="13" />, A. Muñoz-Gutiérrez⁴⁷, A. D. Myers⁹², S. Nadathur<fnref n="7" />, L. Napolitano⁹², R. Neveux²⁶, J. A. Newman⁴⁸, J. Nie⁹³, G. Niz²⁰,⁹⁴, P. Norberg<fnref n="5" />,<fnref n="6" />, H. E. Noriega⁴⁷, E. Paillas⁷³, N. Palanque-Delabrouille<fnref n="2" />,<fnref n="11" />, A. Palmese⁹⁵, P. Zhiwei⁷², D. Parkinson⁹⁶, S. Penmetsa⁷³, W. J. Percival⁷³,⁸⁰,⁸¹, A. Pérez-Fernández⁴⁷, I. Pérez-Ràfols⁹⁷, M. Pieri⁷⁴, C. Poppett<fnref n="2" />,²⁴,²⁵, A. Porredon²⁶,⁴⁴, F. Prada⁵⁶, R. Pucha⁵⁴, A. Raichoor<fnref n="2" />, C. Ramírez-Pérez<fnref n="13" />, S. Ramirez-Solano⁴⁷, M. Rashkovetskyi⁵³, C. Ravoux<fnref n="11" />,<fnref n="18" />, A. Rocher<fnref n="11" />, C. Rockosi³³,³⁵,⁹⁸, A. J. Ross⁴²,⁴⁴,⁷⁵, G. Rossi⁸², R. Ruggeri²⁸,⁴⁵, V. Ruhlmann-Kleider<fnref n="11" />, C. G. Sabiu⁷⁶, K. Said⁴⁵, A. Saintonge³⁰, L. Samushia¹⁹,⁹⁹,¹⁰⁰, E. Sanchez<fnref n="12" />, C. Saulder⁹⁶, E. Schaan⁴⁰, E. F. Schlafly¹⁰¹, D. Schlegel<fnref n="2" />, D. Scholte³⁰, M. Schubnell¹⁰,⁷¹, H. Seo¹⁰², A. Shafieloo⁹⁶, R. Sharples<fnref n="6" />,¹⁰³, W. Sheu¹⁰⁴, J. Silber<fnref n="2" />, F. Sinaglia<fnref n="2" />,⁸⁹, M. Siudek³⁷, Z. Slepian<fnref n="2" />,⁷⁰, A. Smith<fnref n="6" />, D. Sprayberry²⁹, L. Stephey<fnref n="2" />, J. Suárez-Pérez⁵⁸, Z. Sun³⁴, T. Tan⁸⁴, G. Tarle¹⁰, R. Tojeiro¹⁰⁵, L. A. Ureña-López²⁰, R. Vaisakh⁴⁹, D. Valcin¹⁰², F. Valdes²⁹, M. Valluri¹⁰,²², M. Vargas-Magaña⁴⁷, A. Variu⁵⁷, L. Verde⁶⁵,⁸⁹, M. Walther⁶⁷,⁶⁸, B. Wang³⁴,¹⁰⁶, M. S. Wang²⁶, B. A. Weaver²⁹, N. Weaverdyck<fnref n="2" />, R. H. Wechsler³⁹,⁴⁰,¹⁰⁷, M. White²⁵,⁵¹, Y. Xie⁶², J. Yang⁵⁴, C. Yèche<fnref n="11" />, J. Yu⁵⁷, S. Yuan⁴⁰, H. Zhang¹⁹, Z. Zhang⁵¹, C. Zhao³⁴,⁵⁷, Z. Zheng²³, R. Zhou<fnref n="2" />, Z. Zhou⁹³, H. Zou⁷³, S. Zou³⁴, and Y. Zu⁴²,⁵⁰,¹⁰⁸

<footnote n="1">¹ Instituto de Física Teórica (IFT) UAM/CSIC, Universidad Autónoma de Madrid, Cantoblanco, E-28049, Madrid, Spain; spokespersons@desi.lbl.gov</footnote>

<footnote n="2">²Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA</footnote>

<footnote n="3">³Physics Dept., Boston University, 590 Commonwealth Avenue, Boston, MA 02215, USA</footnote>

<footnote n="4">⁴Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai 400005, India</footnote>

<footnote n="5">⁵Centre for Extragalactic Astronomy, Department of Physics, Durham University, South Road, Durham, DH1 3LE, UK</footnote>

<footnote n="6">⁶Institute for Computational Cosmology, Department of Physics, Durham University, South Road, Durham, DH1 3LE, UK</footnote>

<footnote n="7">⁷Institute of Cosmology & Gravitation, University of Portsmouth, Dennis Sciama Building, Portsmouth, PO1 3FX, UK</footnote>

<footnote n="8">⁸Departamento de Astrofísica, Universidad de La Laguna (ULL), E-38206, La Laguna, Tenerife, Spain</footnote>

<footnote n="9">⁹Instituto de Astrofísica de Canarias, C/ Vía Láctea, s/n, E-38205 La Laguna, Tenerife, Spain
¹⁰University of Michigan, Ann Arbor, MI 48109, USA</footnote>

<footnote n="11">¹¹IRFU, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France</footnote>

<footnote n="12">¹²CIEMAT, Avenida Complutense 40, E-28040 Madrid, Spain</footnote>

<footnote n="13">¹³Institut de Física d'Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, E-08193 Bellaterra, Barcelona, Spain</footnote>

<footnote n="14">¹⁴Consejo Nacional de Ciencia y Tecnología, Av. Insurgentes Sur 1582. Colonia Crédito Constructor, Del. Benito Juárez C.P. 03940, México D.F. México</footnote>

<footnote n="15">¹⁵Departamento de Física, Instituto Nacional de Investigaciones Nucleares, Carretera México-Toluca S/N, La Marquesa, Ocoyoacac, Edo. de México C.P. 52750, México</footnote>

<footnote n="16">¹⁶Physics Department, Yale University, P.O. Box 208120, New Haven, CT 06511, USA</footnote>

<footnote n="17">¹⁷Department of Physics and Astronomy, University of California, Irvine, CA 92697, USA</footnote>

<footnote n="18">¹⁸Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France</footnote>
¹⁹ Department of Physics, Kansas State University, 116 Cardwell Hall, Manhattan, KS 66506, USA

²⁰ Departamento de Física, Universidad de Guanajuato—DCI, C.P. 37150, Leon, Guanajuato, México

²¹ Department of Physics & Astronomy, University of Rochester, 206 Bausch and Lomb Hall, P.O. Box 270171, Rochester, NY 14627-0171, USA

²² Department of Astronomy, University of Michigan, Ann Arbor, MI 48109, USA

²³ Department of Physics and Astronomy, The University of Utah, 115 South 1400 East, Salt Lake City, UT 84112, USA

²⁴ Space Sciences Laboratory, University of California, Berkeley, 7 Gauss Way, Berkeley, CA 94720, USA

²⁵ University of California, Berkeley, 110 Sproul Hall #5800 Berkeley, CA 94720, USA

²⁶ Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ, UK

²⁷ Dipartimento di Fisica “Aldo Pontremoli,”Università degli Studi di Milano, Via Celoria 16, I-20133 Milano, Italy

²⁸ Centre for Astrophysics & Supercomputing, Swinburne University of Technology, P.O. Box 218, Hawthorn, VIC 3122, Australia

²⁹ NSF’s NOIRLab, 950 N. Cherry Avenue, Tucson, AZ 85719, USA

³⁰ Department of Physics & Astronomy, University College London, Gower Street, London, WC1E 6BT, UK

³¹ Department of Astronomy and Astrophysics, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637, USA

³² Fermi National Accelerator Laboratory, PO Box 500, Batavia, IL 60510, USA

³³ Department of Astronomy and Astrophysics, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95065, USA

³⁴ Department of Astronomy, Tsinghua University, 30 Shuangqing Road, Haidian District, Beijing, 100190, People’s Republic of China

³⁵ Department of Astronomy and Astrophysics, UCO/Lick Observatory, University of California, 1156 High Street, Santa Cruz, CA 95064, USA

³⁶ Institut d’Estudis Espacials de Catalunya (IEEC), E-08034 Barcelona, Spain

³⁷ Institute of Space Sciences, ICE-CSIC, Campus UAB, Carrer de Can Magrans s/n, E-08913 Bellaterra, Barcelona, Spain

³⁸ Institute for Advanced Study, 1 Einstein Drive, Princeton, NJ 08540, USA

³⁹ Physics Department, Stanford University, Stanford, CA 93405, USA

⁴⁰ SLAC National Accelerator Laboratory, Menlo Park, CA 94305, USA

⁴¹ Institute of Astronomy and Department of Physics, National Tsing Hua University, 101 Kuang-Fu Road Sec. 2, Hsinchu 30013, Taiwan

⁴² Center for Cosmology and AstroParticle Physics, The Ohio State University, 191 West Woodruff Avenue, Columbus, OH 43210, USA

⁴³ Department of Physics, The Ohio State University, 191 West Woodruff Avenue, Columbus, OH 43210, USA

⁴⁴ The Ohio State University, Columbus, OH 43210, USA

⁴⁵ School of Mathematics and Physics, University of Queensland, 4072, Australia

⁴⁶ Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge, CB3 0HA, UK

⁴⁷ Instituto de Física, Universidad Nacional Autónoma de México, Cd. de México C.P. 04510, México

⁴⁸ Department of Physics & Astronomy and Pittsburgh Particle Physics, Astrophysics, and Cosmology Center (PITT PACC), University of Pittsburgh, 3941 O’Hara Street, Pittsburgh, PA 15260, USA

⁴⁹ Department of Physics, Southern Methodist University, 3215 Daniel Avenue, Dallas, TX 75275, USA

⁵⁰ Department of Astronomy, School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China

⁵¹ Department of Physics, University of California, Berkeley, 366 LeConte Hall MC 7300, Berkeley, CA 94720-7300, USA

⁵² Universities Space Research Association, NASA Ames Research Centre, USA

⁵³ Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA

⁵⁴ Steward Observatory, University of Arizona, 933 N. Cherry Avenue, Tucson, AZ 85721, USA

⁵⁵ School of Mathematics, Statistics and Physics, Newcastle University, Newcastle, UK

⁵⁶ Instituto de Astrofísica de Andalucía (CSIC), Glorieta de la Astronomía, s/n, E-18008 Granada, Spain

⁵⁷ Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland

⁵⁸ Departamento de Física, Universidad de los Andes, Cra. 1 No. 18A-10, Edificio Ip, CP 111711, Bogotá, Colombia

⁵⁹ Observatorio Astronómico, Universidad de los Andes, Cra. 1 No. 18A-10, Edificio H, CP 111711 Bogotá, Colombia

⁶⁰ Department of Physics, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, UK

⁶¹ Universidad ECCI, Cra. 19 No. 49-20, Bogotá, Colombia, Código Postal 111311, USA

⁶² Department of Physics, The University of Texas at Dallas, Richardson, TX 75080, USA

⁶³ Scientific Computing Core, Flatiron Institute, 162 5th Avenue, New York, NY 10010, USA

⁶⁴ Center for Computational Astrophysics, Flatiron Institute, 162 5th Avenue, New York, NY 10010, USA

⁶⁵ Instituto de Ciencias del Cosmoc, (ICCUB) Universidad de Barcelona (IEEC-UB), Martí i Franquès 1, E-08028 Barcelona, Spain

⁶⁶ Centro de Investigación Avanzada en Física Fundamental (CIAFF), Facultad de Ciencias, Universidad Autónoma de Madrid, E-28049 Madrid, Spain

⁶⁷ Excellence Cluster ORIGINS, Boltzmannstrasse 2, D-85748 Garching, Germany

⁶⁸ University Observatory, Faculty of Physics, Ludwig-Maximilians-Universität, Scheinerstr. 1, D-81677 München, Germany

⁶⁹ Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA

⁷⁰ Department of Astronomy, University of Florida, 211 Bryant Space Science Center, Gainesville, FL 32611, USA

⁷¹ Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA

⁷² Kavli Institute for Astronomy and Astrophysics at Peking University, PKU, 5 Yiheyuan Road, Haidian District, Beijing 100871, People’s Republic of China

⁷³ Department of Physics and Astronomy, University of Waterloo, 200 University Avenue W, Waterloo, ON N2L 3G1, Canada

⁷⁴ Aix Marseille Univ, CNRS, CNES, LAM, Marseille, France

⁷⁵ Department of Astronomy, The Ohio State University, 4055 McPherson Laboratory, 140 W 18th Avenue, Columbus, OH 43210, USA

⁷⁶ Natural Science Research Institute, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul, Republic of Korea

⁷⁷ Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK

⁷⁸ Konkoly Observatory, CSFK, MTA Centre of Excellence, Budapest, Konkoly Thege Miklós út 15-17, H-1121 Hungary

⁷⁹ MTA-CSFK Lendület Large-scale Structure Research Group, H-1121 Budapest, Konkoly Thege Miklós út 15-17, Hungary

⁸⁰ Perimeter Institute for Theoretical Physics, 31 Caroline Street North, Waterloo, ON N2L 2Y5, Canada

⁸¹ Waterloo Centre for Astrophysics, University of Waterloo, 200 University Avenue W, Waterloo, ON N2L 3G1, Canada

⁸² Department of Physics and Astronomy, Sejong University, Seoul, 143-747, Republic of Korea

⁸³ Graduate Institute of Astrophysics and Department of Physics, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan

⁸⁴ Sorbonne Université, CNRS/IN2P3, Laboratoire de Physique Nucléaire et de Hautes Energies (LPNHE), F-75005 Paris, France

⁸⁵ Department of Astronomy & Astrophysics, University of Toronto, Toronto, ON M5S 3H4, Canada

⁸⁶ Department of Physics, Harvard University, 17 Oxford Street, Cambridge, MA 02138, USA

⁸⁷ Departament de Física, Universitat Autònoma de Barcelona, E-08193 Bellaterra, Barcelona, Spain.

⁸⁸ Astrophysics Group, Department of Physics, Imperial College London, Prince Consort Road, London, SW7 2AZ, UK

⁸⁹ Institució Catalana de Recerca i Estudis Avançats, Passage de Lluís Companys, 23, E-08010 Barcelona, Spain

⁹⁰ Department of Physics and Astronomy, Siena College, 515 Loudon Road, Loudonville, NY 12211, USA

⁹¹ Department of Physics and Astronomy, University of Sussex, Falmer, Brighton, BN1 9QH, U.K

⁹² Department of Physics & Astronomy, University of Wyoming, 1000 E. University, Dept. 3905, Laramie, WY 82071, USA

⁹³ National Astronomical Observatories, Chinese Academy of Sciences, A20 Datun Rd., Chaoyang District, Beijing, 100012, People’s Republic of China
<sup>19</sup>Department of Physics, Kansas State University, 116 Cardwell Hall, Manhattan, KS 66506, USA

<sup>20</sup>Departamento de Física, Universidad de Guanajuato—DCI, C.P. 37150, Leon, Guanajuato, México

<sup>21</sup>Department of Physics & Astronomy, University of Rochester, 206 Bausch and Lomb Hall, P.O. Box 270171, Rochester, NY 14627-0171, USA

<sup>22</sup>Department of Astronomy, University of Michigan, Ann Arbor, MI 48109, USA

<sup>23</sup>Department of Physics and Astronomy, The University of Utah, 115 South 1400 East, Salt Lake City, UT 84112, USA

<sup>24</sup>Space Sciences Laboratory, University of California, Berkeley, 7 Gauss Way, Berkeley, CA 94720, USA

<sup>25</sup>University of California, Berkeley, 110 Sproul Hall #5800 Berkeley, CA 94720, USA

<sup>26</sup>Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ, UK

<sup>27</sup>Dipartimento di Fisica “Aldo Pontremoli,”Università degli Studi di Milano, Via Celoria 16, I-20133 Milano, Italy

<sup>28</sup>Centre for Astrophysics & Supercomputing, Swinburne University of Technology, P.O. Box 218, Hawthorn, VIC 3122, Australia

<sup>29</sup>NSF’s NOIRLab, 950 N. Cherry Avenue, Tucson, AZ 85719, USA

<sup>30</sup>Department of Physics & Astronomy, University College London, Gower Street, London, WC1E 6BT, UK

<sup>31</sup>Department of Astronomy and Astrophysics, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637, USA

<sup>32</sup>Fermi National Accelerator Laboratory, PO Box 500, Batavia, IL 60510, USA

<sup>33</sup>Department of Astronomy and Astrophysics, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95065, USA

<sup>34</sup>Department of Astronomy, Tsinghua University, 30 Shuangqing Road, Haidian District, Beijing, 100190, People’s Republic of China

<sup>35</sup>Department of Astronomy and Astrophysics, UCO/Lick Observatory, University of California, 1156 High Street, Santa Cruz, CA 95064, USA

<sup>36</sup>Institut d’Estudis Espacials de Catalunya (IEEC), E-08034 Barcelona, Spain

<sup>37</sup>Institute of Space Sciences, ICE-CSIC, Campus UAB, Carrer de Can Magrans s/n, E-08913 Bellaterra, Barcelona, Spain

<sup>38</sup>Institute for Advanced Study, 1 Einstein Drive, Princeton, NJ 08540, USA

<sup>39</sup>Physics Department, Stanford University, Stanford, CA 93405, USA

<sup>40</sup>SLAC National Accelerator Laboratory, Menlo Park, CA 94305, USA

<sup>41</sup>Institute of Astronomy and Department of Physics, National Tsing Hua University, 101 Kuang-Fu Road Sec. 2, Hsinchu 30013, Taiwan

<sup>42</sup>Center for Cosmology and AstroParticle Physics, The Ohio State University, 191 West Woodruff Avenue, Columbus, OH 43210, USA

<sup>43</sup>Department of Physics, The Ohio State University, 191 West Woodruff Avenue, Columbus, OH 43210, USA

<sup>44</sup>The Ohio State University, Columbus, OH 43210, USA

<sup>45</sup>School of Mathematics and Physics, University of Queensland, 4072, Australia

<sup>46</sup>Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge, CB3 0HA, UK

<sup>47</sup>Instituto de Física, Universidad Nacional Autónoma de México, Cd. de México C.P. 04510, México

<sup>48</sup>Department of Physics & Astronomy and Pittsburgh Particle Physics, Astrophysics, and Cosmology Center (PITT PACC), University of Pittsburgh, 3941 O’Hara Street, Pittsburgh, PA 15260, USA

<sup>49</sup>Department of Physics, Southern Methodist University, 3215 Daniel Avenue, Dallas, TX 75275, USA

<sup>50</sup>Department of Astronomy, School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China

<sup>51</sup>Department of Physics, University of California, Berkeley, 366 LeConte Hall MC 7300, Berkeley, CA 94720-7300, USA

<sup>52</sup>Universities Space Research Association, NASA Ames Research Centre, USA

<sup>53</sup>Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA

<sup>54</sup>Steward Observatory, University of Arizona, 933 N. Cherry Avenue, Tucson, AZ 85721, USA

<sup>55</sup>School of Mathematics, Statistics and Physics, Newcastle University, Newcastle, UK

<sup>56</sup>Instituto de Astrofísica de Andalucía (CSIC), Glorieta de la Astronomía, s/n, E-18008 Granada, Spain

<sup>57</sup>Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland

<sup>58</sup>Departamento de Física, Universidad de los Andes, Cra. 1 No. 18A-10, Edificio Ip, CP 111711, Bogotá, Colombia

<sup>59</sup>Observatorio Astronómico, Universidad de los Andes, Cra. 1 No. 18A-10, Edificio H, CP 111711 Bogotá, Colombia

<sup>60</sup>Department of Physics, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, UK

<sup>61</sup>Universidad ECCI, Cra. 19 No. 49-20, Bogotá, Colombia, Código Postal 111311, USA

<sup>62</sup>Department of Physics, The University of Texas at Dallas, Richardson, TX 75080, USA

<sup>63</sup>Scientific Computing Core, Flatiron Institute, 162 5<sup>th</sup> Avenue, New York, NY 10010, USA

<sup>64</sup>Center for Computational Astrophysics, Flatiron Institute, 162 5<sup>th</sup> Avenue, New York, NY 10010, USA

<sup>65</sup>Instituto de Ciencias del Cosmoc, (ICCUB) Universidad de Barcelona (IEEC-UB), Martí i Franquès 1, E-08028 Barcelona, Spain

<sup>66</sup>Centro de Investigación Avanzada en Física Fundamental (CIAFF), Facultad de Ciencias, Universidad Autónoma de Madrid, E-28049 Madrid, Spain

<sup>67</sup>Excellence Cluster ORIGINS, Boltzmannstrasse 2, D-85748 Garching, Germany

<sup>68</sup>University Observatory, Faculty of Physics, Ludwig-Maximilians-Universität, Scheinerstr. 1, D-81677 München, Germany

<sup>69</sup>Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA

<sup>70</sup>Department of Astronomy, University of Florida, 211 Bryant Space Science Center, Gainesville, FL 32611, USA

<sup>71</sup>Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA

<sup>72</sup>Kavli Institute for Astronomy and Astrophysics at Peking University, PKU, 5 Yiheyuan Road, Haidian District, Beijing 100871, People’s Republic of China

<sup>73</sup>Department of Physics and Astronomy, University of Waterloo, 200 University Avenue W, Waterloo, ON N2L 3G1, Canada

<sup>74</sup>Aix Marseille Univ, CNRS, CNES, LAM, Marseille, France

<sup>75</sup>Department of Astronomy, The Ohio State University, 4055 McPherson Laboratory, 140 W 18th Avenue, Columbus, OH 43210, USA

<sup>76</sup>Natural Science Research Institute, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul, Republic of Korea

<sup>77</sup>Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK

<sup>78</sup>Konkoly Observatory, CSFK, MTA Centre of Excellence, Budapest, Konkoly Thege Miklós út 15-17, H-1121 Hungary

<sup>79</sup>MTA-CSFK Lendület Large-scale Structure Research Group, H-1121 Budapest, Konkoly Thege Miklós út 15-17, Hungary

<sup>80</sup>Perimeter Institute for Theoretical Physics, 31 Caroline Street North, Waterloo, ON N2L 2Y5, Canada

<sup>81</sup>Waterloo Centre for Astrophysics, University of Waterloo, 200 University Avenue W, Waterloo, ON N2L 3G1, Canada

<sup>82</sup>Department of Physics and Astronomy, Sejong University, Seoul, 143-747, Republic of Korea

<sup>83</sup>Graduate Institute of Astrophysics and Department of Physics, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan

<sup>84</sup>Sorbonne Université, CNRS/IN2P3, Laboratoire de Physique Nucléaire et de Hautes Energies (LPNHE), F-75005 Paris, France

<sup>85</sup>Department of Astronomy & Astrophysics, University of Toronto, Toronto, ON M5S 3H4, Canada

<sup>86</sup>Department of Physics, Harvard University, 17 Oxford Street, Cambridge, MA 02138, USA

<sup>87</sup>Departament de Física, Universitat Autònoma de Barcelona, E-08193 Bellaterra, Barcelona, Spain.

<sup>88</sup>Astrophysics Group, Department of Physics, Imperial College London, Prince Consort Road, London, SW7 2AZ, UK

<sup>89</sup>Institució Catalana de Recerca i Estudis Avançats, Passage de Lluís Companys, 23, E-08010 Barcelona, Spain

<sup>90</sup>Department of Physics and Astronomy, Siena College, 515 Loudon Road, Loudonville, NY 12211, USA

<sup>91</sup>Department of Physics and Astronomy, University of Sussex, Falmer, Brighton, BN1 9QH, U.K

<sup>92</sup>Department of Physics & Astronomy, University of Wyoming, 1000 E. University, Dept. 3905, Laramie, WY 82071, USA

<sup>93</sup>National Astronomical Observatories, Chinese Academy of Sciences, A20 Datun Rd., Chaoyang District, Beijing, 100012, People’s Republic of China
⁹⁴ Instituto Avanzado de Cosmología A.C., San Marcos 11—Atenas 202. Magdalena Contreras, 10720. Ciudad de México, México

⁹⁵ Department of Physics, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA

⁹⁶ Korea Astronomy and Space Science Institute, 776, Daedeokdae-ro, Daejeon 34055, Republic of Korea

⁹⁷ Departament de Física Quàntica i Astrofísica, Universitat de Barcelona, Martí i Franquès 1, E-08028 Barcelona, Spain

⁹⁸ University of California Observatories, 1156 High Street, Sana Cruz, CA 95065, USA

⁹⁹ Abastumani Astrophysical Observatory, Tbilisi, GE-0179, Georgia

¹⁰⁰ Faculty of Natural Sciences and Medicine, Ilia State University, 0194 Tbilisi, Georgia

¹⁰¹ Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA

¹⁰² Department of Physics & Astronomy, Ohio University, Athens, OH 45701, USA

¹⁰³ Centre for Advanced Instrumentation, Department of Physics, Durham University, South Road, Durham DH1 3LE, UK

¹⁰⁴ Department of Physics & Astronomy, University of California, Los Angeles, 430 Portola Plaza, Los Angeles, CA 90095, USA

¹⁰⁵ SUPA, School of Physics and Astronomy, University of St Andrews, St Andrews, KY16 9SS, UK

¹⁰⁶ Beihang University, Beijing 100191, People's Republic of China

¹⁰⁷ Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Menlo Park, CA 94305, USA

¹⁰⁸ Shanghai Key Laboratory for Particle Physics and Cosmology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
*Received 2023 June 15; revised 2023 October 16; accepted 2023 October 17; published 2024 January 15*

## Abstract

The Dark Energy Spectroscopic Instrument (DESI) was designed to conduct a survey covering 14,000 deg² over 5 yr to constrain the cosmic expansion history through precise measurements of baryon acoustic oscillations (BAO). The scientific program for DESI was evaluated during a 5 month survey validation (SV) campaign before beginning full operations. This program produced deep spectra of tens of thousands of objects from each of the stellar Milky Way Survey (MWS), Bright Galaxy Survey (BGS), luminous red galaxy (LRG), emission line galaxy (ELG), and quasar target classes. These SV spectra were used to optimize redshift distributions, characterize exposure times, determine calibration procedures, and assess observational overheads for the 5 yr program. In this paper, we present the final target selection algorithms, redshift distributions, and projected cosmology constraints resulting from those studies. We also present a One-Percent Survey conducted at the conclusion of SV covering 140 deg² using the final target selection algorithms with exposures of a depth typical of the main survey. The SV indicates that DESI will be able to complete the full 14,000 deg² program with spectroscopically confirmed targets from the MWS, BGS, LRG, ELG, and quasar programs with total sample sizes of 7.2, 13.8, 7.46, 15.7, and 2.87 million, respectively. These samples will allow exploration of the Milky Way halo, clustering on all scales, and BAO measurements with a statistical precision of 0.28% over the redshift interval $z < 1.1$, 0.39% over the redshift interval $1.1 < z < 1.9$, and 0.46% over the redshift interval $1.9 < z < 3.5$.

*Unified Astronomy Thesaurus concepts: Cosmology (343); Redshift surveys (1378)*

## 1. Introduction

Studies of the geometry and energy content of the Universe, physics of cosmic expansion, fundamental properties of standard model particles, and growth of structure remain the key focus of cosmology studies. Early measurements of cosmic expansion history using Type Ia supernovae (SNe Ia) helped to constrain the energy content, providing the first evidence for cosmic acceleration that could be explained by a form of dark energy (Riess et al. 1998; Perlmutter et al. 1999). Subsequent SNe Ia studies (e.g., Suzuki et al. 2012; Betoule et al. 2014; Scolnic et al. 2018) were able to constrain the equation of state for dark energy to a precision of roughly 4% when combined with cosmic microwave background (CMB) measurements from the Planck satellite (Planck Collaboration et al. 2011), consistent with a ΛCDM model where dark energy can be explained by a cosmological constant. Under this assumption of a flat ΛCDM model, final CMB measurements from Planck lead to measurements of the matter density to better than 1% precision and baryon density to better than 0.5% precision (Planck Collaboration et al. 2020).

Wide-field, optical spectroscopy offers cosmological measurements that are complementary to SNe Ia measurements of the distance–redshift relation and measurements of CMB

anisotropy. Spectroscopy of galaxies and quasars provides a precise, three-dimensional map of matter in the Universe in which the scale of baryon acoustic oscillations (BAO) can be measured at high precision. As a preferred scale in the clustering of matter (~150 Mpc comoving), BAO measured in large-scale structure provide a standard ruler for observational cosmology. Measurements from 2dFGRS (Colless et al. 2001) and the Sloan Digital Sky Survey (SDSS; York et al. 2000) marked the first use of BAO as a cosmological probe (Cole et al. 2005; Eisenstein et al. 2005), thus motivating the design of surveys dedicated to BAO and clustering measurements, such as WiggleZ (Blake et al. 2011a, 2011b). The Baryon Oscillation Spectroscopic Survey (BOSS; Dawson et al. 2013) of SDSS-III (Eisenstein et al. 2011) and the extended Baryon Oscillation Spectroscopic Survey (eBOSS; Dawson et al. 2016) of SDSS-IV (Blanton et al. 2017) are the largest of those completed spectroscopic programs. The SDSS, SDSS-II, BOSS, and eBOSS programs produced eight spectroscopic samples that led to BAO measurements spanning the redshift range $0.07 < z < 2.5$. When combined with the Planck temperature and polarization data, these BAO measurements provide nearly an order of magnitude improvement on curvature constraints relative to primary CMB constraints alone. Adding again the Pantheon SNe Ia sample (Scolnic et al. 2018), the BAO data allow constraints on the Hubble constant $H_0 = 67.87 \pm 0.86$ km s⁻¹ Mpc⁻¹ (Alam et al. 2021) under a cosmological model that allows for a time-varying equation of state for dark energy and nonzero curvature. It has been

<figure><img src="image_3.png" /></figure>

Original content from this work may be used under the terms of the [Creative Commons Attribution 4.0 licence](https://creativecommons.org/licenses/by/4.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
⁹⁴ Instituto Avanzado de Cosmología A.C., San Marcos 11—Atenas 202, Magdalena Contreras, 10720, Ciudad de México, México

⁹⁵ Department of Physics, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA

⁹⁶ Korea Astronomy and Space Science Institute, 776, Daedeokdae-ro, Daejeon 34055, Republic of Korea

⁹⁷ Departament de Física Quàntica i Astrofísica, Universitat de Barcelona, Martí i Franquès 1, E-08028 Barcelona, Spain

⁹⁸ University of California Observatories, 1156 High Street, Sana Cruz, CA 95065, USA

⁹⁹ Abastumani Astrophysical Observatory, Tbilisi, GE-0179, Georgia

¹⁰⁰ Faculty of Natural Sciences and Medicine, Ilia State University, 0194 Tbilisi, Georgia

¹⁰¹ Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA

¹⁰² Department of Physics & Astronomy, Ohio University, Athens, OH 45701, USA

¹⁰³ Centre for Advanced Instrumentation, Department of Physics, Durham University, South Road, Durham DH1 3LE, UK

¹⁰⁴ Department of Physics & Astronomy, University of California, Los Angeles, 430 Portola Plaza, Los Angeles, CA 90095, USA

¹⁰⁵ SUPA, School of Physics and Astronomy, University of St Andrews, St Andrews, KY16 9SS, UK

¹⁰⁶ Beihang University, Beijing 100191, People's Republic of China

¹⁰⁷ Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Menlo Park, CA 94305, USA

¹⁰⁸ Shanghai Key Laboratory for Particle Physics and Cosmology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
*Received 2023 June 15; revised 2023 October 16; accepted 2023 October 17; published 2024 January 15*

## Abstract

The Dark Energy Spectroscopic Instrument (DESI) was designed to conduct a survey covering 14,000 deg² over 5 yr to constrain the cosmic expansion history through precise measurements of baryon acoustic oscillations (BAO). The scientific program for DESI was evaluated during a 5 month survey validation (SV) campaign before beginning full operations. This program produced deep spectra of tens of thousands of objects from each of the stellar Milky Way Survey (MWS), Bright Galaxy Survey (BGS), luminous red galaxy (LRG), emission line galaxy (ELG), and quasar target classes. These SV spectra were used to optimize redshift distributions, characterize exposure times, determine calibration procedures, and assess observational overheads for the 5 yr program. In this paper, we present the final target selection algorithms, redshift distributions, and projected cosmology constraints resulting from those studies. We also present a One-Percent Survey conducted at the conclusion of SV covering 140 deg² using the final target selection algorithms with exposures of a depth typical of the main survey. The SV indicates that DESI will be able to complete the full 14,000 deg² program with spectroscopically confirmed targets from the MWS, BGS, LRG, ELG, and quasar programs with total sample sizes of 7.2, 13.8, 7.46, 15.7, and 2.87 million, respectively. These samples will allow exploration of the Milky Way halo, clustering on all scales, and BAO measurements with a statistical precision of 0.28% over the redshift interval $z < 1.1$, 0.39% over the redshift interval $1.1 < z < 1.9$, and 0.46% over the redshift interval $1.9 < z < 3.5$.

*Unified Astronomy Thesaurus concepts: Cosmology (343); Redshift surveys (1378)*

## 1. Introduction

Studies of the geometry and energy content of the Universe, physics of cosmic expansion, fundamental properties of standard model particles, and growth of structure remain the key focus of cosmology studies. Early measurements of cosmic expansion history using Type Ia supernovae (SNe Ia) helped to constrain the energy content, providing the first evidence for cosmic acceleration that could be explained by a form of dark energy (Riess et al. 1998; Perlmutter et al. 1999). Subsequent SNe Ia studies (e.g., Suzuki et al. 2012; Betoule et al. 2014; Scolnic et al. 2018) were able to constrain the equation of state for dark energy to a precision of roughly 4% when combined with cosmic microwave background (CMB) measurements from the Planck satellite (Planck Collaboration et al. 2011), consistent with a ΛCDM model where dark energy can be explained by a cosmological constant. Under this assumption of a flat ΛCDM model, final CMB measurements from Planck lead to measurements of the matter density to better than 1% precision and baryon density to better than 0.5% precision (Planck Collaboration et al. 2020).

Wide-field, optical spectroscopy offers cosmological measurements that are complementary to SNe Ia measurements of the distance–redshift relation and measurements of CMB

anisotropy. Spectroscopy of galaxies and quasars provides a precise, three-dimensional map of matter in the Universe in which the scale of baryon acoustic oscillations (BAO) can be measured at high precision. As a preferred scale in the clustering of matter (~150 Mpc comoving), BAO measured in large-scale structure provide a standard ruler for observational cosmology. Measurements from 2dFGRS (Colless et al. 2001) and the Sloan Digital Sky Survey (SDSS; York et al. 2000) marked the first use of BAO as a cosmological probe (Cole et al. 2005; Eisenstein et al. 2005), thus motivating the design of surveys dedicated to BAO and clustering measurements, such as WiggleZ (Blake et al. 2011a, 2011b). The Baryon Oscillation Spectroscopic Survey (BOSS; Dawson et al. 2013) of SDSS-III (Eisenstein et al. 2011) and the extended Baryon Oscillation Spectroscopic Survey (eBOSS; Dawson et al. 2016) of SDSS-IV (Blanton et al. 2017) are the largest of those completed spectroscopic programs. The SDSS, SDSS-II, BOSS, and eBOSS programs produced eight spectroscopic samples that led to BAO measurements spanning the redshift range $0.07 < z < 2.5$. When combined with the Planck temperature and polarization data, these BAO measurements provide nearly an order of magnitude improvement on curvature constraints relative to primary CMB constraints alone. Adding again the Pantheon SNe Ia sample (Scolnic et al. 2018), the BAO data allow constraints on the Hubble constant $H_0 = 67.87 \pm 0.86$ km s⁻¹ Mpc⁻¹ (Alam et al. 2021) under a cosmological model that allows for a time-varying equation of state for dark energy and nonzero curvature. It has been

<figure><img src="image_4.png" /></figure>

Original content from this work may be used under the terms of the [Creative Commons Attribution 4.0 licence](https://creativecommons.org/licenses/by/4.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
demonstrated that this measurement of the Hubble constant is robust against both assumptions of expansion history and estimates of baryon density. However, several local measurements of the Hubble constant find higher values (e.g., Freedman et al. 2019; Wong et al. 2020; Riess et al. 2022), albeit with varying degrees of tension.

Spectroscopic samples of galaxies and quasars can also be used to probe the growth of structure through redshift-space distortions (RSD). RSD appear in the clustering of matter due to the peculiar velocities induced by gravitational interactions, thus creating an apparent enhancement of clustering along the line of sight relative to clustering perpendicular to the line of sight (Kaiser 1987). RSD data complement recent weak-lensing measurements (e.g., Mandelbaum et al. 2018; Hikage et al. 2019; Hildebrandt et al. 2020; Joachimi et al. 2021; Abbott et al. 2022) by offering constraints on the gravitational infall of matter over cosmological scales. Growth of structure measurements allows enhanced tests of the energy components, of neutrino masses, and of General Relativity. Even when assuming a cosmological model that allows for a time-varying equation of state for dark energy and nonzero curvature, percent-level constraints on $\Omega_{\Lambda}$, $H_0$, and $\sigma_8$ are possible when using the full sample of BAO and RSD measurements from the SDSS series of experiments (Howlett et al. 2015; Ross et al. 2015; Alam et al. 2017; de Mattia et al. 2020; du Mas des Bourboux et al. 2020; Gil-Marín et al. 2020; Neveux et al. 2020; Tamone et al. 2020; Bautista et al. 2021; Hou et al. 2021; Raichoor et al. 2021), CMB data from Planck, SNe Ia data from the Pantheon sample (Scolnic et al. 2018) and more recently (Scolnic et al. 2022), and weak-lensing data from the Dark Energy Survey (Abbott et al. 2018) and more recently (Abbott et al. 2022). Under this model, the combination of BAO, RSD, CMB, SNe Ia, and weak-lensing data leads to a constraint $\Omega_k = -0.0022 \pm 0.0022$, $w_a = -0.49_{-0.30}^{+0.35}$, and $w_p = -1.018 \pm 0.032$, at a pivot redshift $z_p = 0.29$. Here, the time-varying equation of state for dark energy is defined as $w(z) = w_p + (a_p - a)w_a$, where $a_p$, the expansion factor corresponding to the pivot redshift, is chosen to make the uncertainties on $w_p$ and $w_a$ uncorrelated. Furthermore, the combination of samples produces tests of gravity that are consistent with General Relativity, a measurement of the clustering amplitude $\sigma_8 = 0.8140 \pm 0.0093$, and of the summed neutrino masses $\sum m_\nu < 0.115$ eV (95% confidence; Alam et al. 2021). When evaluating the SDSS BAO and RSD independently from the other samples, the clustering amplitude is found to be $\sigma_8 = 0.85 \pm 0.03$, a measurement that does not support the somewhat low estimates of structure growth reported in recent weak-lensing studies (e.g., Dark Energy Survey & Kilo-Degree Survey Collaboration et al. 2023).

The Dark Energy Spectroscopic Instrument (DESI; Levi et al. 2013; DESI Collaboration et al. 2016a, 2016b) was designed to advance studies of the cosmological model by large margins over previous programs through measurements of the clustering of galaxies, quasars, and the Ly$\alpha$ forest. DESI will be used to conduct a 5 yr survey over 14,000 deg$^2$ with a spectroscopic sample size that will be 10 times that of the previous SDSS programs. This footprint will be covered by six different classes of targets. Following the motivation to perform BAO measurements near the cosmic-variance limit, we will use selections based on optical and infrared imaging data to identify a bright sample of low-redshift galaxies (Bright Galaxy Survey, hereafter BGS; $z_{\text{median}} \sim 0.2$), luminous red

galaxies (LRG; $0.4 < z < 1.1$), emission line galaxies (ELG; $0.6 < z < 1.6$), quasars as direct tracers ($0.9 < z < 2.1$), and Lyman-$\alpha$ forest (Ly$\alpha$ forest) quasars ($2.1 < z < 3.5$) to trace the distribution of neutral hydrogen. Toward this goal, data from only 2 months of operations has already resulted in a detection of the BAO signal in both the BGS and LRG samples (Moon et al. 2023). The extensive program will also extract cosmological information from the derived power spectra to constrain neutrino masses, modified gravity, and the physics of inflation. In addition, a sample of stellar targets will be observed to a high density in an overlapping Milky Way Survey (MWS; Cooper et al. 2023). These stellar spectra will be used to explore the stellar evolution, kinematics, and assembly history of the Milky Way.

Because the surface density and faintness of the wide-field DESI sample far exceed the capabilities of previous spectroscopic facilities, these samples had to be extensively explored with the DESI instrument itself before the commencement of the 5 yr program. To do so, we conducted observations in a phase of survey validation (SV). These observations were used to test the quality of data against the primary BAO science requirements, optimize target selection algorithms, and inform the final DESI operational and analysis program. The first stage of SV, the target selection validation, took place from 2020 December 14 through 2021 April 2. In the final stage of SV, we performed a pilot survey of the full DESI program that covered approximately 140 deg$^2$ ("One-Percent") using a superset of the final selection of MWS, BGS, LRG, ELG, and quasar targets. At least 95% of targets were observed from each of the samples over 20 distinct fields.

In this paper, we present an overview of the DESI target selection and One-Percent Survey validation programs, the results, and the implications for the 5 yr program. A full description of the final target selection algorithms for the LRG, ELG, and quasar samples can be found in accompanying papers by Raichoor et al. (2023), Zhou et al. (2023), and Chaussidon et al. (2023), respectively. The procedures for identifying all classes of targets can be found in Myers et al. (2023). The description of prioritization of targets for observation is detailed in Schlafly et al. (2023). An overview of the observational strategy and projections for the BGS program can be found in the accompanying paper by Hahn et al. (2023), while an overview of the MWS science program can be found in Cooper et al. (2023). Visual inspections played an essential role in verifying the performance of the instrument, the data reduction pipeline, and the target selection algorithms. The visual inspection process and characterization of the spectroscopic performance for the galaxy samples can be found in Lan et al. (2023), while the same for quasars can be found in Alexander et al. (2023).

This paper is organized as follows. In Section 2, we present an overview of the initial requirements for BAO precision and the programmatic questions that SV was designed to address. In Section 3, we describe the target selection SV program, observations, and the resulting calibration procedures. In Section 4, we present the imaging data, target selection algorithms, and the One-Percent Survey observations that were vetted during SV and will be used for studies of clustering. We present the exposure times, survey strategy, and redshift distributions expected for the 5 yr survey in Section 5. In Section 6, we present the cosmological forecasts, and in Section 7, we present a summary of the plans for cosmological
demonstrated that this measurement of the Hubble constant is
robust against both assumptions of expansion history and
estimates of baryon density. However, several local measure-
ments of the Hubble constant find higher values (e.g.,
Freedman et al. 2019; Wong et al. 2020; Riess et al. 2022),
albeit with varying degrees of tension.

Spectroscopic samples of galaxies and quasars can also be
used to probe the growth of structure through redshift-space
distortions (RSD). RSD appear in the clustering of matter due
to the peculiar velocities induced by gravitational interactions,
thus creating an apparent enhancement of clustering along the
line of sight relative to clustering perpendicular to the line of
sight (Kaiser 1987). RSD data complement recent weak-lensing
measurements (e.g., Mandelbaum et al. 2018; Hikage et al.
2019; Hildebrandt et al. 2020; Joachimi et al. 2021; Abbott
et al. 2022) by offering constraints on the gravitational infall of
matter over cosmological scales. Growth of structure measure-
ments allows enhanced tests of the energy components, of
neutrino masses, and of General Relativity. Even when
assuming a cosmological model that allows for a time-varying
equation of state for dark energy and nonzero curvature,
percent-level constraints on Ω<sub>Λ</sub>, H<sub>0</sub>, and σ<sub>8</sub> are possible when
using the full sample of BAO and RSD measurements from the
SDSS series of experiments (Howlett et al. 2015; Ross et al.
2015; Alam et al. 2017; de Mattia et al. 2020; du Mas des
Bourboux et al. 2020; Gil-Marín et al. 2020; Neveux et al.
2020; Tamone et al. 2020; Bautista et al. 2021; Hou et al. 2021;
Raichoor et al. 2021), CMB data from Planck, SNe Ia data from
the Pantheon sample (Scolnic et al. 2018) and more recently
(Scolnic et al. 2022), and weak-lensing data from the
Dark Energy Survey (Abbott et al. 2018) and more recently
(Abbott et al. 2022). Under this model, the combination
of BAO, RSD, CMB, SNe Ia, and weak-lensing data leads
to a constraint Ω<sub>k</sub> = −0.0022 ± 0.0022, w<sub>de</sub> = −0.49<sup>+0.35</sup><sub>−0.30</sub>, and
w<sub>p</sub> = −1.018 ± 0.032, at a pivot redshift z<sub>p</sub> = 0.29. Here, the
time-varying equation of state for dark energy is defined as
w(z) = w<sub>p</sub> + (a<sub>p</sub> − a)w<sub>de</sub>, where a<sub>p</sub>, the expansion factor
corresponding to the pivot redshift, is chosen to make the
uncertainties on w<sub>p</sub> and w<sub>de</sub> uncorrelated. Furthermore, the
combination of samples produces tests of gravity that are
consistent with General Relativity, a measurement of the
clustering amplitude σ<sub>8</sub> = 0.8140 ± 0.0093, and of the summed
neutrino masses Σm<sub>ν</sub> < 0.115 eV (95% confidence; Alam et al.
2021). When evaluating the SDSS BAO and RSD indepen-
dently from the other samples, the clustering amplitude is
found to be σ<sub>8</sub> = 0.85 ± 0.03, a measurement that does not
support the somewhat low estimates of structure growth
reported in recent weak-lensing studies (e.g., Dark Energy
Survey & Kilo-Degree Survey Collaboration et al. 2023).

The Dark Energy Spectroscopic Instrument (DESI; Levi
et al. 2013; DESI Collaboration et al. 2016a, 2016b) was
designed to advance studies of the cosmological model by large
margins over previous programs through measurements of the
clustering of galaxies, quasars, and the Lyα forest. DESI will
be used to conduct a 5 yr survey over 14,000 deg² with a
spectroscopic sample size that will be 10 times that of the
previous SDSS programs. This footprint will be covered by six
different classes of targets. Following the motivation to
perform BAO measurements near the cosmic-variance limit,
we will use selections based on optical and infrared imaging
data to identify a bright sample of low-redshift galaxies (Bright
Galaxy Survey, hereafter BGS; z<sub>median</sub> ~ 0.2), luminous red

galaxies (LRG; 0.4 < z < 1.1), emission line galaxies (ELG;
0.6 < z < 1.6), quasars as direct tracers (0.9 < z < 2.1), and
Lyman-α forest (Lyα forest) quasars (2.1 < z < 3.5) to trace
the distribution of neutral hydrogen. Toward this goal, data
from only 2 months of operations has already resulted in a
detection of the BAO signal in both the BGS and LRG samples
(Moon et al. 2023). The extensive program will also extract
cosmological information from the derived power spectra to
constrain neutrino masses, modified gravity, and the physics of
inflation. In addition, a sample of stellar targets will be
observed to a high density in an overlapping Milky Way
Survey (MWS; Cooper et al. 2023). These stellar spectra will
be used to explore the stellar evolution, kinematics, and
assembly history of the Milky Way.

Because the surface density and faintness of the wide-field
DESI sample far exceed the capabilities of previous spectro-
scopic facilities, these samples had to be extensively explored
with the DESI instrument itself before the commencement of
the 5 yr program. To do so, we conducted observations in a
phase of survey validation (SV). These observations were used
to test the quality of data against the primary BAO science
requirements, optimize target selection algorithms, and inform
the final DESI operational and analysis program. The first stage
of SV, the target selection validation, took place from 2020
December 14 through 2021 April 2. In the final stage of SV, we
performed a pilot survey of the full DESI program that covered
approximately 140 deg² (“One-Percent”) using a superset of
the final selection of MWS, BGS, LRG, ELG, and quasar
targets. At least 95% of targets were observed from each of the
samples over 20 distinct fields.

In this paper, we present an overview of the DESI target
selection and One-Percent Survey validation programs, the
results, and the implications for the 5 yr program. A full
description of the final target selection algorithms for the LRG,
ELG, and quasar samples can be found in accompanying
papers by Raichoor et al. (2023), Zhou et al. (2023), and
Chaussidon et al. (2023), respectively. The procedures for
identifying all classes of targets can be found in Myers et al.
(2023). The description of prioritization of targets for
observation is detailed in Schlafly et al. (2023). An overview
of the observational strategy and projections for the BGS
program can be found in the accompanying paper by Hahn
et al. (2023), while an overview of the MWS science program
can be found in Cooper et al. (2023). Visual inspections played
an essential role in verifying the performance of the instrument,
the data reduction pipeline, and the target selection algorithms.
The visual inspection process and characterization of the
spectroscopic performance for the galaxy samples can be found
in Lan et al. (2023), while the same for quasars can be found in
Alexander et al. (2023).

This paper is organized as follows. In Section 2, we present
an overview of the initial requirements for BAO precision and
the programmatic questions that SV was designed to address.
In Section 3, we describe the target selection SV program,
observations, and the resulting calibration procedures. In
Section 4, we present the imaging data, target selection
algorithms, and the One-Percent Survey observations that were
vetted during SV and will be used for studies of clustering. We
present the exposure times, survey strategy, and redshift
distributions expected for the 5 yr survey in Section 5. In
Section 6, we present the cosmological forecasts, and in
Section 7, we present a summary of the plans for cosmological
studies, release of data products to the broader community, and highlights of other science opportunities with the DESI data. Throughout, we use the AB magnitude system and assume a fiducial cosmology described by the final Planck results (Planck Collaboration et al. 2020), where $\Omega_M = 0.315$, $\sigma_8 = 0.811$, and $h = 0.674$.

## 2. Survey Validation

The primary purpose of SV was to confirm that the survey design, instrument performance, and data quality would be sufficient to meet the top-level goals on BAO measurement precision. Here, we present an overview of those goals, the instrument design, and the specific questions that the SV observational program was designed to address.

### 2.1. DESI Science Requirements

DESI is designed as a Stage-IV dark energy experiment as defined by the Dark Energy Task Force (DETF; Albrecht et al. 2006). A Stage-IV experiment implies at least a factor of 10 improvement in dark energy figure of merit (FoM) relative to a representative Stage-II program. The cosmology results from the 3 yr Supernova Legacy Survey (SNLS) were chosen as this representative program. The SNLS sample of 472 SNe Iae produced constraints on the time-evolving equation of state for dark energy $w_0 = -0.905 \pm 0.196$, and $w_a = -0.984_{-0.997}^{+1.094}$ under the assumption of a flat universe (Sullivan et al. 2011).

The detailed assumptions and forecast procedures using only CMB and BAO measurements are presented in the science, targeting, and survey design report (DESI Collaboration et al. 2016a). Briefly, we define the FoM as $[\sigma(w_p)\sigma(w_a)]^{-1}$ for dark energy with a time-evolving equation of state. The dark energy equation of state parameters are forecast in a model where curvature is also treated as a free parameter. A 9000 deg2 DESI survey of galaxies, quasars, and the Lya forest would achieve a DETF FoM for BAO science of 121, whereas the FoM of the year 3 SNLS result was found to be 11. Doing so requires measurements of the isotropic cosmic distance scale, $R(z)$, to a precision 0.28% over the interval $0.0 < z < 1.1$ and 0.39% over the interval $1.1 < z < 1.9$. Additional quasar and Ly$\alpha$ forest BAO measurements of $H(z)$ are required to a precision of 1.05% over the interval $1.9 < z < 3.7$.

These early BAO and FoM predictions were based on an assumed redshift distribution for the various target classes that had not yet been measured from imaging or spectroscopic data. Early algorithms for selection of targets (Raichoor et al. 2020; Ruiz-Macias et al. 2020; Yèche et al. 2020; Zhou et al. 2020) held promise for meeting the requirements of final spectroscopic sample size and redshift range. The FoM will be significantly improved with a larger survey area, the addition of RSD measurements, and the inclusion of weak-lensing, SNe Ia, or other dark energy probes. Cosmological forecasts that account for the final target selection algorithms, predicted areal coverage, and additional measurements can be found in Section 6.

### 2.2. DESI Instrument Design

DESI was built with the requirement of obtaining a minimum of 30 million redshifts to achieve the subpercent precision BAO measurements described above, while providing an additional margin through a 14,000 deg² footprint. A full description of the motivation and requirements for the

instrument, control system, and data management can be found in the instrument design report (DESI Collaboration et al. 2016b) and an overview of the completed instrument (DESI Collaboration et al. 2022).

To enable the required performance, new corrective optics were installed at the National Optical Astronomy Observatory's 4 m Mayall telescope at Kitt Peak, Arizona to allow the installation of a 0.8 m diameter focal plane (Miller et al. 2023). The field of view available to the instrument is 8.0 deg², of which 7.45 deg² is accessible for spectroscopy. The roughly circular focal plane is divided into ten *petals* distributed over equal angles in azimuth. The instrument design incorporates robotically actuated fibers to minimize overhead from fiber repositioning between exposures (Silber et al. 2023). The positioners are arranged with a mean 10.525 mm pitch between centers, each with a range of motion that covers a 12 mm (nearly 3 arcmin) diameter. Each positioner hosts a fiber with a core diameter of 107 µm, corresponding to an average 1.1"5 diameter projection on the sky. A focal plane consisting of 5020 of these fiber positioners was constructed. 5000 fibers feed ten spectrographs that cover a wavelength range from 360 to 980 nm. The remaining 20 fibers feed a separate camera for independent measurements of sky background.

Each spectrograph consists of three cameras with a resolving power, $R = \lambda/\Delta\lambda$, that ranges from roughly 2000 at the shortest wavelengths to nearly 5500 at the longest wavelengths (P. Jelinsky et al. 2023, in preparation). The focal plane is installed at prime focus, with 47.5 m fiber runs connecting each positioner to a spectrograph in a climate controlled, enclosed environment (C. Poppett et al. 2023, in preparation). The instrument is controlled in real-time through a series of automated data acquisition components that determine dynamic exposure times, perform data quality assessment, and convert on-sky target coordinates to fiber positions (Kent et al. 2023). The automated data acquisition and rapid reconfigurability of the fiber positions enable very efficient operations with a deadtime between exposures of less than 120 s.

Based on experience from previous spectroscopic programs and simulated spectra for realistic target samples, the instrument was expected to complete a 14,000 deg² survey in 5 yr. The faint ELG targets are the most challenging spectra to classify, requiring spectra that are sufficiently deep to detect [O II] fluxes down to $8 \times 10^{-17} \text{ erg s}^{-1} \text{ cm}^{-2}$. Given the 5 yr observing window and goal for a 14,000 deg² footprint, exposure depths must be equivalent to 1000 s exposures taken at zenith through regions of sky with no Galactic extinction. The [O II] flux requirement of the ELG sample was expected to set the observational pace for redshift completion, while the brighter LRG and quasar targets were expected to reach high completeness, even with shallower exposures. Exposure times for BGS and MWS targets were to be tuned to balance high-redshift completeness with high surface density during the times when the moon produced higher sky background levels. The assumed redshift success rates and data quality as a function of exposure time were tested in the SV with results described in Section 5.

### 2.3. Questions to Inform the Survey Validation Program

During SV, we obtained data to test the quality of spectra against the objective of completing BAO measurements to a precision required for a Stage-IV program. These data were further used to optimize target selection algorithms and inform
studies, release of data products to the broader community, and highlights of other science opportunities with the DESI data. Throughout, we use the AB magnitude system and assume a fiducial cosmology described by the final Planck results (Planck Collaboration et al. 2020), where $\Omega_M = 0.315$, $\sigma_8 = 0.811$, and $h = 0.674$.

## 2. Survey Validation

The primary purpose of SV was to confirm that the survey design, instrument performance, and data quality would be sufficient to meet the top-level goals on BAO measurement precision. Here, we present an overview of those goals, the instrument design, and the specific questions that the SV observational program was designed to address.

### 2.1. DESI Science Requirements

DESI is designed as a Stage-IV dark energy experiment as defined by the Dark Energy Task Force (DETF; Albrecht et al. 2006). A Stage-IV experiment implies at least a factor of 10 improvement in dark energy figure of merit (FoM) relative to a representative Stage-II program. The cosmology results from the 3 yr Supernova Legacy Survey (SNLS) were chosen as this representative program. The SNLS sample of 472 SNe Iae produced constraints on the time-evolving equation of state for dark energy $w_0 = -0.905 \pm 0.196$, and $w_a = -0.984_{-0.997}^{+1.094}$ under the assumption of a flat universe (Sullivan et al. 2011).

The detailed assumptions and forecast procedures using only CMB and BAO measurements are presented in the science, targeting, and survey design report (DESI Collaboration et al. 2016a). Briefly, we define the FoM as $[\sigma(w_p)\sigma(w_a)]^{-1}$ for dark energy with a time-evolving equation of state. The dark energy equation of state parameters are forecast in a model where curvature is also treated as a free parameter. A 9000 deg2 DESI survey of galaxies, quasars, and the Lya forest would achieve a DETF FoM for BAO science of 121, whereas the FoM of the year 3 SNLS result was found to be 11. Doing so requires measurements of the isotropic cosmic distance scale, $R(z)$, to a precision 0.28% over the interval $0.0 < z < 1.1$ and 0.39% over the interval $1.1 < z < 1.9$. Additional quasar and Ly$\alpha$ forest BAO measurements of $H(z)$ are required to a precision of 1.05% over the interval $1.9 < z < 3.7$.

These early BAO and FoM predictions were based on an assumed redshift distribution for the various target classes that had not yet been measured from imaging or spectroscopic data. Early algorithms for selection of targets (Raichoor et al. 2020; Ruiz-Macias et al. 2020; Yèche et al. 2020; Zhou et al. 2020) held promise for meeting the requirements of final spectroscopic sample size and redshift range. The FoM will be significantly improved with a larger survey area, the addition of RSD measurements, and the inclusion of weak-lensing, SNe Ia, or other dark energy probes. Cosmological forecasts that account for the final target selection algorithms, predicted areal coverage, and additional measurements can be found in Section 6.

### 2.2. DESI Instrument Design

DESI was built with the requirement of obtaining a minimum of 30 million redshifts to achieve the subpercent precision BAO measurements described above, while providing an additional margin through a 14,000 deg² footprint. A full description of the motivation and requirements for the

instrument, control system, and data management can be found in the instrument design report (DESI Collaboration et al. 2016b) and an overview of the completed instrument (DESI Collaboration et al. 2022).

To enable the required performance, new corrective optics were installed at the National Optical Astronomy Observatory's 4 m Mayall telescope at Kitt Peak, Arizona to allow the installation of a 0.8 m diameter focal plane (Miller et al. 2023). The field of view available to the instrument is 8.0 deg², of which 7.45 deg² is accessible for spectroscopy. The roughly circular focal plane is divided into ten *petals* distributed over equal angles in azimuth. The instrument design incorporates robotically actuated fibers to minimize overhead from fiber repositioning between exposures (Silber et al. 2023). The positioners are arranged with a mean 10.525 mm pitch between centers, each with a range of motion that covers a 12 mm (nearly 3 arcmin) diameter. Each positioner hosts a fiber with a core diameter of 107 µm, corresponding to an average 1.1"5 diameter projection on the sky. A focal plane consisting of 5020 of these fiber positioners was constructed. 5000 fibers feed ten spectrographs that cover a wavelength range from 360 to 980 nm. The remaining 20 fibers feed a separate camera for independent measurements of sky background.

Each spectrograph consists of three cameras with a resolving power, $R = \lambda/\Delta\lambda$, that ranges from roughly 2000 at the shortest wavelengths to nearly 5500 at the longest wavelengths (P. Jelinsky et al. 2023, in preparation). The focal plane is installed at prime focus, with 47.5 m fiber runs connecting each positioner to a spectrograph in a climate controlled, enclosed environment (C. Poppett et al. 2023, in preparation). The instrument is controlled in real-time through a series of automated data acquisition components that determine dynamic exposure times, perform data quality assessment, and convert on-sky target coordinates to fiber positions (Kent et al. 2023). The automated data acquisition and rapid reconfigurability of the fiber positions enable very efficient operations with a deadtime between exposures of less than 120 s.

Based on experience from previous spectroscopic programs and simulated spectra for realistic target samples, the instrument was expected to complete a 14,000 deg² survey in 5 yr. The faint ELG targets are the most challenging spectra to classify, requiring spectra that are sufficiently deep to detect [O II] fluxes down to $8 \times 10^{-17} \text{ erg s}^{-1} \text{ cm}^{-2}$. Given the 5 yr observing window and goal for a 14,000 deg² footprint, exposure depths must be equivalent to 1000 s exposures taken at zenith through regions of sky with no Galactic extinction. The [O II] flux requirement of the ELG sample was expected to set the observational pace for redshift completion, while the brighter LRG and quasar targets were expected to reach high completeness, even with shallower exposures. Exposure times for BGS and MWS targets were to be tuned to balance high-redshift completeness with high surface density during the times when the moon produced higher sky background levels. The assumed redshift success rates and data quality as a function of exposure time were tested in the SV with results described in Section 5.

### 2.3. Questions to Inform the Survey Validation Program

During SV, we obtained data to test the quality of spectra against the objective of completing BAO measurements to a precision required for a Stage-IV program. These data were further used to optimize target selection algorithms and inform
the final DESI operational and analysis program. The SV
observations were designed to allow us to finalize the target
selection algorithms and survey strategy as follows:

1. By performing a selection of SV targets that exceeds the main survey target densities, various selection boundaries could be assessed so that the final algorithm could be tuned for optimal redshift distributions.

2. By obtaining sufficiently deep spectra on SV targets to determine the parent redshift distributions with high confidence, we could thus determine the number of tracers for modified selections as a function of redshift, even with uncertainties in the data reduction pipelines.

3. By conducting spectroscopy over a large number of exposures, multiple data splits could be used to test repeatability and determine the statistical uncertainties on redshift estimates, completeness of spectral classification, and purity in assignment of redshifts.

4. By assigning a sufficiently large number of standard star and white dwarf targets to each field, spectrophotometric data quality could be assessed with varying flux calibration schemes.

5. By assigning a sufficiently large number of sky fibers to each field, sky-subtraction algorithms could be vetted to determine how many sky fibers are required to achieve nearly Poisson-limited sky subtraction.

6. By performing observations of each field in varying conditions, exposure times as a function of sky brightness, seeing, transparency, airmass, and Galactic extinction could be computed and used to calibrate the real-time, dynamic exposure-time calculator (ETC).

7. By assessing the relationship between redshift success rate and exposure depth, exposure times for the main program could be established to optimize the science return in 5 yr of operations.

3. Target Selection Validation

We conducted target selection validation observations over the period 2020 December through early 2021 April, with a few additional observations completing in May. We took the data to address the questions above while scheduling a program long enough to allow time to complete the studies before beginning the main survey. These observations were divided between targets for the MWS in a dedicated stellar SV program, for the BGS on dedicated fields that also included MWS targets at lower priority, and for LRG, ELG, and quasar samples. In all cases of target selection validation observation, each field was covered by a single tile with one dedicated position for each of the 5000 fibers.

3.1. *Observations*

The basic observational goal of target selection validation
was to obtain high-quality spectra for a statistically representa-
tive sample that would include the final selection algorithm for
each class of target. The deep spectra were intended to allow
tests of reliability of the redshift estimates. The broader
selection was intended to allow optimization of the sample
definitions to maximize scientific yield.

To achieve roughly uniform redshift performance in the
main program, exposure times will be adjusted to account for
the Galactic extinction, airmass, seeing, transparency, and sky

background. Galactic extinction and airmass can be predicted
ahead of time, but seeing, transparency, and sky background
are determined in real-time using feedback from guide cameras
and the sky monitor. There was no calibration of the real-time
exposure time estimates prior to SV, so we modified exposure
times only based on extinction, airmass, the phase and position
of the moon, and the seeing delivered in the previous exposure.
We assumed a power law for the relationship between exposure
time and airmass, such that $t_{\text{exp}} = t_0 X^{1.25}$. Here, $X$ represents
airmass, and the power-law form was determined empirically
from BOSS/eBOSS observations. The constant in front is a
normalization factor that is defined separately for dark and
bright time observations.

The LRG, ELG, and quasar programs were conducted when the sky was darkest. Assuming median seeing of $1''$, photometric conditions, typical sky in dark time, Galactic extinction $E(B-V)=0$, and observations at zenith, spectral simulations indicated that 1000 s exposures were sufficient to determine redshifts for ELG targets with [O II] line fluxes above the threshold described in Section 2.2. A series of survey simulations accounting for variations in observing conditions indicated that we would complete each LRG, ELG, and quasar field with these effective exposure times in 5 yr (Schlafly et al. 2023). Correcting only for airmass, Galactic extinction, seeing, and moon phase and location on a field-to-field basis, we used this 1000 s effective exposure time for each epoch of LRG, ELG, and quasar observation. Typically, four epochs were obtained for each field over four different nights. This observing strategy provided data at varying airmass, observing conditions, and depth.

Pixel-level simulations of the spectrograph indicated that a 4000 s cumulative effective exposure time was sufficient to classify the faintest targets in the nominal selections with a high degree of confidence. Exposures of this depth are also sufficient to classify the majority of interlopers in the target selections that could potentially confuse classifications in normal-depth exposures. Objects that could not be classified under this observation strategy will surely result in redshift failures during the shorter, main survey exposures. An exception to the four epoch, 4000 s observing strategy was made for three fields containing only ELG targets and for three fields containing only quasar and LRG targets. These six fields were observed to exposure times ranging from the equivalent of 6.5 to 15 epochs. The goal for these data was to facilitate visual inspection, provide a more accurate truth table of redshift estimates, and allow multiple subsamples of the data for consistency tests. These will be among the deepest exposures taken by DESI.

These fields were observed with various combinations of
ELG, LRG, and quasar targets. Target acquisition efficiencies
improved dramatically over these 4 months as a result of
enhancements of the fiber assignment and focal plane control
software. Overall, the selection for all targets was designed to
be a well-controlled, random subsampling to enable modeling
of the underlying population for each tracer.

The stellar and BGS observations were primarily conducted
during the times when the moon was above the horizon. Based
on early commissioning data and simulated spectra, BGS and
MWS targets can be successfully classified in 180 s exposures
under nominal conditions in dark time. Our simplified moon-
light model for most of these exposures increased their
exposure time by a factor of 3.6, in addition to terms for
Galactic extinction, airmass, and seeing described above. Most
the final DESI operational and analysis program. The SV
observations were designed to allow us to finalize the target
selection algorithms and survey strategy as follows:

1. By performing a selection of SV targets that exceeds the main survey target densities, various selection boundaries could be assessed so that the final algorithm could be tuned for optimal redshift distributions.

2. By obtaining sufficiently deep spectra on SV targets to determine the parent redshift distributions with high confidence, we could thus determine the number of tracers for modified selections as a function of redshift, even with uncertainties in the data reduction pipelines.

3. By conducting spectroscopy over a large number of exposures, multiple data splits could be used to test repeatability and determine the statistical uncertainties on redshift estimates, completeness of spectral classification, and purity in assignment of redshifts.

4. By assigning a sufficiently large number of standard star and white dwarf targets to each field, spectrophotometric data quality could be assessed with varying flux calibration schemes.

5. By assigning a sufficiently large number of sky fibers to each field, sky-subtraction algorithms could be vetted to determine how many sky fibers are required to achieve nearly Poisson-limited sky subtraction.

6. By performing observations of each field in varying conditions, exposure times as a function of sky brightness, seeing, transparency, airmass, and Galactic extinction could be computed and used to calibrate the real-time, dynamic exposure-time calculator (ETC).

7. By assessing the relationship between redshift success rate and exposure depth, exposure times for the main program could be established to optimize the science return in 5 yr of operations.

3. Target Selection Validation

We conducted target selection validation observations over the period 2020 December through early 2021 April, with a few additional observations completing in May. We took the data to address the questions above while scheduling a program long enough to allow time to complete the studies before beginning the main survey. These observations were divided between targets for the MWS in a dedicated stellar SV program, for the BGS on dedicated fields that also included MWS targets at lower priority, and for LRG, ELG, and quasar samples. In all cases of target selection validation observation, each field was covered by a single tile with one dedicated position for each of the 5000 fibers.

3.1. *Observations*

The basic observational goal of target selection validation
was to obtain high-quality spectra for a statistically representa-
tive sample that would include the final selection algorithm for
each class of target. The deep spectra were intended to allow
tests of reliability of the redshift estimates. The broader
selection was intended to allow optimization of the sample
definitions to maximize scientific yield.

To achieve roughly uniform redshift performance in the
main program, exposure times will be adjusted to account for
the Galactic extinction, airmass, seeing, transparency, and sky

background. Galactic extinction and airmass can be predicted
ahead of time, but seeing, transparency, and sky background
are determined in real-time using feedback from guide cameras
and the sky monitor. There was no calibration of the real-time
exposure time estimates prior to SV, so we modified exposure
times only based on extinction, airmass, the phase and position
of the moon, and the seeing delivered in the previous exposure.
We assumed a power law for the relationship between exposure
time and airmass, such that t<sub>exp</sub> = t<sub>0</sub>X<sup>1.25</sup>. Here, X represents
airmass, and the power-law form was determined empirically
from BOSS/eBOSS observations. The constant in front is a
normalization factor that is defined separately for dark and
bright time observations.

The LRG, ELG, and quasar programs were conducted when the sky was darkest. Assuming median seeing of 1"/1, photometric conditions, typical sky in dark time, Galactic extinction E(B − V ) = 0, and observations at zenith, spectral simulations indicated that 1000 s exposures were sufficient to determine redshifts for ELG targets with [O II] line fluxes above the threshold described in Section 2.2. A series of survey simulations accounting for variations in observing conditions indicated that we would complete each LRG, ELG, and quasar field with these effective exposure times in 5 yr (Schlafly et al. 2023). Correcting only for airmass, Galactic extinction, seeing, and moon phase and location on a field-to-field basis, we used this 1000 s effective exposure time for each epoch of LRG, ELG, and quasar observation. Typically, four epochs were obtained for each field over four different nights. This observing strategy provided data at varying airmass, observing conditions, and depth.

Pixel-level simulations of the spectrograph indicated that a 4000 s cumulative effective exposure time was sufficient to classify the faintest targets in the nominal selections with a high degree of confidence. Exposures of this depth are also sufficient to classify the majority of interlopers in the target selections that could potentially confuse classifications in normal-depth exposures. Objects that could not be classified under this observation strategy will surely result in redshift failures during the shorter, main survey exposures. An exception to the four epoch, 4000 s observing strategy was made for three fields containing only ELG targets and for three fields containing only quasar and LRG targets. These six fields were observed to exposure times ranging from the equivalent of 6.5 to 15 epochs. The goal for these data was to facilitate visual inspection, provide a more accurate truth table of redshift estimates, and allow multiple subsamples of the data for consistency tests. These will be among the deepest exposures taken by DESI.

These fields were observed with various combinations of
ELG, LRG, and quasar targets. Target acquisition efficiencies
improved dramatically over these 4 months as a result of
enhancements of the fiber assignment and focal plane control
software. Overall, the selection for all targets was designed to
be a well-controlled, random subsampling to enable modeling
of the underlying population for each tracer.

The stellar and BGS observations were primarily conducted
during the times when the moon was above the horizon. Based
on early commissioning data and simulated spectra, BGS and
MWS targets can be successfully classified in 180 s exposures
under nominal conditions in dark time. Our simplified moon-
light model for most of these exposures increased their
exposure time by a factor of 3.6, in addition to terms for
Galactic extinction, airmass, and seeing described above. Most
<figure><img src="image_4.png" /><figcaption>**Figure 1.** The field centers for the fields designed to test MWS, BGS, LRG, ELG, and quasar selections and spectroscopic performance in the DESI target selection validation program. The light gray regions show the full imaging footprint available from Bok and Mayall imaging while the dark gray regions show the full imaging footprint available from the DECam imaging. The black outline shows the footprint of the Dark Energy Survey (DES). Details on the imaging can be found in Section 4.1.</figcaption></figure>

fields were observed under this scheme on four different nights. When possible, one of these observations was taken during dark time to provide high-quality reference spectra. As with the LRG, ELG, and quasar targets, we observed several fields to a depth equivalent to ten epochs of main survey time. These fields contained BGS targets at the highest priority and were used for tests of calibration and consistency in redshift classification.

The footprint for all of these observations can be found in Figure 1.

### 3.1.1. Stellar SV Targets

The nearest DESI targets will be Milky Way stars. These targets will include white dwarfs, low-mass stars in the immediate solar neighborhood, rare stars, and stars in the Galactic thick disk and halo that formed more than 10 billion yr ago. In the main DESI survey, these targets will be observed concurrently with BGS targets, but at a lower priority for fiber assignment. For validation of these targets, we designed a series of tiles with dedicated stellar targets to allow tighter control over fiber assignments in fields that were most conducive to stellar science. The preliminary target selection algorithms are presented in Allende Prieto et al. (2020) while a full description of the program and results can be found in the accompanying MWS overview paper (Cooper et al. 2023).

In the main survey, the bulk of the MWS sample will be magnitude limited between $16 < r < 19$ with additional proper-motion and parallax criteria. The selection in the stellar SV program was expanded by removing astrometric criteria, allowing fainter targets to explore the low signal-to-noise limit of the stellar pipelines, and relaxing the criteria for identifying white dwarf candidates from photometric data. As with the main survey, high-value, sparse target classes such as Blue Horizontal Branch stars and RR Lyrae variables were

prioritized for fiber assignment. In addition, to enable comparison of derived stellar properties, priorities were adjusted to obtain spectra at high completeness from objects already observed in APOGEE (Majewski et al. 2017), SEGUE (Yanny et al. 2009; Rockosi et al. 2022), BOSS, the Gaia-ESO Spectroscopic Survey (Gilmore et al. 2012), or GALAH (De Silva et al. 2015).

A summary of the Stellar SV observations can be found in Table 1. Observations of these tiles were designed to address specific questions of stellar spectroscopy associated with sample selection, performance of analysis pipelines, special field selection, and cross-calibration with previous surveys. For this reason, field centers were chosen to sample a variety of environments. In total, six fields were chosen to cover a range of Galactic latitudes, 10 fields were chosen to sample globular and open clusters, three fields were chosen to sample Milky Way satellite galaxies [Draco, Ursa Major II (UMaII), and Sextans], and one field was centered on a region that had a high stellar density from prior BOSS observations.

### 3.1.2. BGS Targets

The lowest-redshift galaxies from DESI will come primarily from the BGS sample. These galaxies will be observed during the time when the moon is significantly above the horizon, and the sky is too bright to allow efficient observation of fainter targets. Approximately 14 million of the brightest galaxies within the DESI footprint will be observed over the course of the survey, sampling galaxies at a high density with a median redshift of approximately $z = 0.2$. This sample alone will be 10 times larger than the SDSS-I and SDSS-II main sample that was observed from 1999 to 2008. A summary of the final selection can be found in Section 4.3 while full description of the program and results can be found in the accompanying BGS overview paper (Hahn et al. 2023).
<figure><img src="image_5.png" /><figcaption>**Figure 1.** The field centers for the fields designed to test MWS, BGS, LRG, ELG, and quasar selections and spectroscopic performance in the DESI target selection validation program. The light gray regions show the full imaging footprint available from Bok and Mayall imaging while the dark gray regions show the full imaging footprint available from the DECam imaging. The black outline shows the footprint of the Dark Energy Survey (DES). Details on the imaging can be found in Section 4.1.</figcaption></figure>

fields were observed under this scheme on four different nights. When possible, one of these observations was taken during dark time to provide high-quality reference spectra. As with the LRG, ELG, and quasar targets, we observed several fields to a depth equivalent to ten epochs of main survey time. These fields contained BGS targets at the highest priority and were used for tests of calibration and consistency in redshift classification.

The footprint for all of these observations can be found in Figure 1.

### 3.1.1. Stellar SV Targets

The nearest DESI targets will be Milky Way stars. These targets will include white dwarfs, low-mass stars in the immediate solar neighborhood, rare stars, and stars in the Galactic thick disk and halo that formed more than 10 billion yr ago. In the main DESI survey, these targets will be observed concurrently with BGS targets, but at a lower priority for fiber assignment. For validation of these targets, we designed a series of tiles with dedicated stellar targets to allow tighter control over fiber assignments in fields that were most conducive to stellar science. The preliminary target selection algorithms are presented in Allende Prieto et al. (2020) while a full description of the program and results can be found in the accompanying MWS overview paper (Cooper et al. 2023).

In the main survey, the bulk of the MWS sample will be magnitude limited between $16 < r < 19$ with additional proper-motion and parallax criteria. The selection in the stellar SV program was expanded by removing astrometric criteria, allowing fainter targets to explore the low signal-to-noise limit of the stellar pipelines, and relaxing the criteria for identifying white dwarf candidates from photometric data. As with the main survey, high-value, sparse target classes such as Blue Horizontal Branch stars and RR Lyrae variables were

prioritized for fiber assignment. In addition, to enable comparison of derived stellar properties, priorities were adjusted to obtain spectra at high completeness from objects already observed in APOGEE (Majewski et al. 2017), SEGUE (Yanny et al. 2009; Rockosi et al. 2022), BOSS, the Gaia-ESO Spectroscopic Survey (Gilmore et al. 2012), or GALAH (De Silva et al. 2015).

A summary of the Stellar SV observations can be found in Table 1. Observations of these tiles were designed to address specific questions of stellar spectroscopy associated with sample selection, performance of analysis pipelines, special field selection, and cross-calibration with previous surveys. For this reason, field centers were chosen to sample a variety of environments. In total, six fields were chosen to cover a range of Galactic latitudes, 10 fields were chosen to sample globular and open clusters, three fields were chosen to sample Milky Way satellite galaxies [Draco, Ursa Major II (UMaII), and Sextans], and one field was centered on a region that had a high stellar density from prior BOSS observations.

### 3.1.2. BGS Targets

The lowest-redshift galaxies from DESI will come primarily from the BGS sample. These galaxies will be observed during the time when the moon is significantly above the horizon, and the sky is too bright to allow efficient observation of fainter targets. Approximately 14 million of the brightest galaxies within the DESI footprint will be observed over the course of the survey, sampling galaxies at a high density with a median redshift of approximately $z = 0.2$. This sample alone will be 10 times larger than the SDSS-I and SDSS-II main sample that was observed from 1999 to 2008. A summary of the final selection can be found in Section 4.3 while full description of the program and results can be found in the accompanying BGS overview paper (Hahn et al. 2023).
<table><caption>**Table 1**
Summary Statistics of Survey Validation Fields</caption><thead><tr><th>Program</th><th>Number of Tiles</th><th>Number of Nights</th><th>Number of Exposures</th><th>Exposure Time (hr)</th><th>Effective Exposure Time (hr)<sup>a</sup></th></tr></thead><tbody><tr><td colspan="6">Deep fields selected for full visual inspection</td></tr><tr><td>BGS</td><td>1</td><td>6</td><td>30</td><td>3.0</td><td>0.8</td></tr><tr><td>ELG</td><td>3</td><td>9</td><td>51</td><td>12.6</td><td>8.7</td></tr><tr><td>LRG and quasar</td><td>3</td><td>10</td><td>54</td><td>12.9</td><td>6.7</td></tr><tr><td colspan="6">MWS, BGS, LRG, ELG, and quasar fields</td></tr><tr><td>Stellar</td><td>15</td><td>30</td><td>176</td><td>24.8</td><td>6.5</td></tr><tr><td>BGS and MWS</td><td>50</td><td>49</td><td>562</td><td>64.6</td><td>15.3</td></tr><tr><td>ELG</td><td>22</td><td>26</td><td>157</td><td>36.8</td><td>24.3</td></tr><tr><td>ELG and quasar</td><td>12</td><td>24</td><td>151</td><td>33.6</td><td>20.6</td></tr><tr><td>LRG and quasar</td><td>28</td><td>41</td><td>292</td><td>66.5</td><td>39.1</td></tr><tr><td>LRG and quasar (updated selection)</td><td>12</td><td>11</td><td>70</td><td>13.5</td><td>12.0</td></tr><tr><td colspan="6">One-Percent Survey</td></tr><tr><td>Bright</td><td>214</td><td>35</td><td>288</td><td>37.2</td><td>15.1</td></tr><tr><td>Dark</td><td>239</td><td>33</td><td>374</td><td>96.9</td><td>86.4</td></tr></tbody></table>

**Note.**

The nominal BGS selection for the main survey is designed to rely on an *r*-band magnitude limit (BGS Bright). One goal of SV was to test the redshift success rate as a function of exposure time and magnitude, thus providing guidance on the nominal exposure times for this sample. Another goal of SV was to establish a selection that would prioritize galaxies over stars. By comparing the *G*-band magnitude from Gaia (Gaia Collaboration et al. 2016) to the *r*-band total magnitude, we can separate stars and galaxies in the main BGS program. No color selection was used in the target selection phase of SV so that the final selection could be optimized based on *G* − *r* star–galaxy separation. A third goal was to find a selection that maximizes completeness in the galaxy population while minimizing spurious targets from deblending and other photometric artifacts. To explore the signatures of spurious signal, the selection did not apply masks around large galaxies and included a subset of objects that were selected without the quality cuts defined for the nominal BGS target selection algorithm.

A second sample of fainter BGS targets (BGS Faint) was observed at a slightly lower density than the bright targets. The selection algorithms were extended to investigate whether a subsample of color-selected galaxies not in the BGS Bright sample can be spectroscopically classified at high completeness. Fainter objects also allowed us to explore the dependence of redshift success rates on total magnitude and on an aperture magnitude matched to the DESI fiber radius.

A summary of the BGS target selection validation observations can be found in Table 1. In total, 50 fields were observed over regions with varying galactic extinction, stellar density, and imaging quality. Eight of these fields overlapped the footprint of the Galaxy and Mass Assembly (GAMA; Driver et al. 2009) survey, which is a highly complete galaxy redshift survey to a similar depth as the BGS sample.

### 3.1.3. LRG Targets

Over the approximate redshift range $0.4 < z < 1.1$, DESI will use LRG targets as the primary tracer for large-scale structure.

These luminous, massive galaxies have long since ceased star formation and therefore exhibit evolved, red spectral energy distributions (SEDs). These galaxies may be most efficiently selected by taking advantage of the prominent 1.6 µm (rest frame) “bump” (John 1988; Sawicki 2002) that causes a strong correlation between optical and/or near-infrared (NIR) color and redshift.

For DESI, we therefore used an algorithm similar to that used for eBOSS LRGs (Prakash et al. 2016) to select the LRG sample from optical and infrared catalogs. A simple cut in optical and/or infrared colors as a function of optical color eliminates the lowest-redshift galaxies and rejects stars in an effective manner.

Redshift estimation is informed primarily by the 4000 Å break and absorption features in LRG spectra. Given the need to reliably estimate the continuum and model these absorption features, the LRG sample was planned to be flux-limited. The selection was extended toward fainter magnitudes than were expected for the main program to test the redshift success rate as a function of flux and thus set the limiting magnitudes for the sample.

The selection followed the same philosophy as planned for the main survey, but with less restrictive boundaries on all colors and magnitudes to allow fine-tuning of the redshift distribution. In addition, two variants on color and magnitude were explored. An optical selection relied on a sliding cut in *r* − *z* color as a function of *z*-band magnitude. An infrared selection relied on a sliding cut in *r*-W1 color as a function of W1-band magnitude, where W1 is the 3.4 micron bandpass from the Wide-field Infrared Survey Explorer (WISE; Wright et al. 2010).

LRG targets were all observed concurrently with quasar targets. A summary of the observations can be found in Table 1. In total, 43 fields containing LRG and quasar targets were observed over regions with varying galactic extinction, stellar density, and imaging quality. In all cases, LRG targets were given fibers after all quasar targets had been assigned. In 31 of these fields, quasar selection was broadened to explore

<footnote>a Effective exposure times are defined in Section 3.3.</footnote>
<table><caption>**Table 1**
Summary Statistics of Survey Validation Fields</caption><thead><tr><th>Program</th><th>Number of Tiles</th><th>Number of Nights</th><th>Number of Exposures</th><th>Exposure Time (hr)</th><th>Effective Exposure Time (hr)<sup>a</sup></th></tr></thead><tbody><tr><td colspan="6">Deep fields selected for full visual inspection</td></tr><tr><td>BGS</td><td>1</td><td>6</td><td>30</td><td>3.0</td><td>0.8</td></tr><tr><td>ELG</td><td>3</td><td>9</td><td>51</td><td>12.6</td><td>8.7</td></tr><tr><td>LRG and quasar</td><td>3</td><td>10</td><td>54</td><td>12.9</td><td>6.7</td></tr><tr><td colspan="6">MWS, BGS, LRG, ELG, and quasar fields</td></tr><tr><td>Stellar</td><td>15</td><td>30</td><td>176</td><td>24.8</td><td>6.5</td></tr><tr><td>BGS and MWS</td><td>50</td><td>49</td><td>562</td><td>64.6</td><td>15.3</td></tr><tr><td>ELG</td><td>22</td><td>26</td><td>157</td><td>36.8</td><td>24.3</td></tr><tr><td>ELG and quasar</td><td>12</td><td>24</td><td>151</td><td>33.6</td><td>20.6</td></tr><tr><td>LRG and quasar</td><td>28</td><td>41</td><td>292</td><td>66.5</td><td>39.1</td></tr><tr><td>LRG and quasar (updated selection)</td><td>12</td><td>11</td><td>70</td><td>13.5</td><td>12.0</td></tr><tr><td colspan="6">One-Percent Survey</td></tr><tr><td>Bright</td><td>214</td><td>35</td><td>288</td><td>37.2</td><td>15.1</td></tr><tr><td>Dark</td><td>239</td><td>33</td><td>374</td><td>96.9</td><td>86.4</td></tr></tbody></table>

**Note.**

The nominal BGS selection for the main survey is designed to rely on an *r*-band magnitude limit (BGS Bright). One goal of SV was to test the redshift success rate as a function of exposure time and magnitude, thus providing guidance on the nominal exposure times for this sample. Another goal of SV was to establish a selection that would prioritize galaxies over stars. By comparing the *G*-band magnitude from Gaia (Gaia Collaboration et al. 2016) to the *r*-band total magnitude, we can separate stars and galaxies in the main BGS program. No color selection was used in the target selection phase of SV so that the final selection could be optimized based on *G* − *r* star–galaxy separation. A third goal was to find a selection that maximizes completeness in the galaxy population while minimizing spurious targets from deblending and other photometric artifacts. To explore the signatures of spurious signal, the selection did not apply masks around large galaxies and included a subset of objects that were selected without the quality cuts defined for the nominal BGS target selection algorithm.

A second sample of fainter BGS targets (BGS Faint) was observed at a slightly lower density than the bright targets. The selection algorithms were extended to investigate whether a subsample of color-selected galaxies not in the BGS Bright sample can be spectroscopically classified at high completeness. Fainter objects also allowed us to explore the dependence of redshift success rates on total magnitude and on an aperture magnitude matched to the DESI fiber radius.

A summary of the BGS target selection validation observations can be found in Table 1. In total, 50 fields were observed over regions with varying galactic extinction, stellar density, and imaging quality. Eight of these fields overlapped the footprint of the Galaxy and Mass Assembly (GAMA; Driver et al. 2009) survey, which is a highly complete galaxy redshift survey to a similar depth as the BGS sample.

### 3.1.3. LRG Targets

Over the approximate redshift range $0.4 < z < 1.1$, DESI will use LRG targets as the primary tracer for large-scale structure.

These luminous, massive galaxies have long since ceased star formation and therefore exhibit evolved, red spectral energy distributions (SEDs). These galaxies may be most efficiently selected by taking advantage of the prominent 1.6 µm (rest frame) “bump” (John 1988; Sawicki 2002) that causes a strong correlation between optical and/or near-infrared (NIR) color and redshift.

For DESI, we therefore used an algorithm similar to that used for eBOSS LRGs (Prakash et al. 2016) to select the LRG sample from optical and infrared catalogs. A simple cut in optical and/or infrared colors as a function of optical color eliminates the lowest-redshift galaxies and rejects stars in an effective manner.

Redshift estimation is informed primarily by the 4000 Å break and absorption features in LRG spectra. Given the need to reliably estimate the continuum and model these absorption features, the LRG sample was planned to be flux-limited. The selection was extended toward fainter magnitudes than were expected for the main program to test the redshift success rate as a function of flux and thus set the limiting magnitudes for the sample.

The selection followed the same philosophy as planned for the main survey, but with less restrictive boundaries on all colors and magnitudes to allow fine-tuning of the redshift distribution. In addition, two variants on color and magnitude were explored. An optical selection relied on a sliding cut in *r* − *z* color as a function of *z*-band magnitude. An infrared selection relied on a sliding cut in *r*-W1 color as a function of W1-band magnitude, where W1 is the 3.4 micron bandpass from the Wide-field Infrared Survey Explorer (WISE; Wright et al. 2010).

LRG targets were all observed concurrently with quasar targets. A summary of the observations can be found in Table 1. In total, 43 fields containing LRG and quasar targets were observed over regions with varying galactic extinction, stellar density, and imaging quality. In all cases, LRG targets were given fibers after all quasar targets had been assigned. In 31 of these fields, quasar selection was broadened to explore

<footnote>a Effective exposure times are defined in Section 3.3.</footnote>
new techniques, leading to a lower yield of LRG targets. In these fields, observations yielded roughly 1200 LRG spectra on average. In 12 fields, the quasar targets were selected according to an algorithm that more closely represented that of the main program, thus decreasing the number of quasar targets. However, because those 12 fields were observed toward the end of target selection validation, the instrument was near optimal performance, and a much larger fraction of quasar targets were assigned fibers. On average in these fields, roughly 800 LRG targets produced spectra. An analysis of the LRG target selection algorithms can be found in Zhou et al. (2023).

### 3.1.4. ELG Targets

The majority of the spectroscopic redshift measurements for DESI will come from ELGs at redshifts $0.6 < z < 1.6$. These galaxies exhibit strong nebular emission lines originating in the ionized H II regions surrounding short-lived, but luminous, massive stars (e.g., Moustakas et al. 2006). ELGs are typically late-type spiral and irregular galaxies, although any galaxy actively forming new stars at a sufficiently high rate will qualify as an ELG. Because of their vigorous ongoing star formation, the integrated rest-frame colors of ELGs are dominated by massive stars, and hence will typically be bluer than LRG and other galaxies with evolved stellar populations. This relatively blue continuum allows the efficient selection of ELG targets from optical grz-band photometry.

Selection of ELG targets for DESI leverages the fact that the cosmic star formation rate was roughly an order of magnitude higher at $z \sim 1$ than today. Galaxies with strong line-emission are therefore very common at the epoch where LRG targets become increasingly difficult to spectroscopically classify. In particular, the prominent [O II] doublet in ELG spectra consists of a pair of emission lines separated in rest-frame wavelength by 2.783 Å. This wavelength separation of the doublet provides a unique signature, allowing definitive line identification (especially in cases where the doublet is resolved, enabled by the design for spectral resolution) and secure redshift measurements from [O II] alone for a large fraction of ELG targets.

During the target selection phase of SV, ELG targets were selected to explore the relationship between redshift, [O II] line strength, and $(g-r)/(r-z)$ color. In addition, we varied the definition of the magnitude limit (either g-band fiber magnitude or g-band total magnitude) and explored the performance of the instrument and selection for fainter objects than were expected for the main selection.

A summary of the ELG observations can be found in Table 1. In total, 25 fields containing ELG targets were observed while 12 fields containing both ELG and quasar targets were observed. Not included in the table is the technical detail that ELG targets were also used as filler for remaining fibers in the 28 LRG and quasar fields. As with the other target classes, these fields covered regions with varying galactic extinction, stellar density, and imaging quality. The fields that contained ELG targets at the highest priority produced an average of roughly 3200 ELG spectra. In the fields that also contained quasar targets, ELG targets were given fibers after all quasar targets had been assigned. On average in these fields, roughly 2400 ELG targets produced spectra, although roughly 600 also satisfied the quasar selection and therefore had a higher priority in fiber assignment. For training the ELG target selection algorithms,

those 12 tiles required downweighting of the targets with overlapping ELG and quasar selections so that they represent a fair fraction of the parent ELG target sample. An analysis of the ELG target selection algorithms and the downweighting scheme can be found in Raichoor et al. (2023).

### 3.1.5. Quasar Targets

The highest-redshift spectroscopic sample for DESI will consist of quasars. We will measure large-scale structure using quasars as direct tracers of dark matter in the redshift range $0.9 < z < 2.1$. The DESI spectrographs cover the $\lambda = 1216$ Å Ly$\alpha$ transition for objects with redshift above $z \sim 2.0$. At redshifts $z = 2.1$ and higher, we will use the foreground neutral-hydrogen Ly$\alpha$ forest absorption observed in quasar spectra to measure large-scale structure. In the main DESI survey, we will obtain additional exposures on confirmed Ly$\alpha$ quasars to measure the Ly$\alpha$ forest at the highest signal-to-noise ratio (S/N) allowed under the observational constraints.

Quasars are fueled by gravitational accretion onto super-massive black holes, leading to emission that can outshine the host galaxy. These are the brightest population of nontransient $z > 1$ targets that have a density high enough to use as tracers of large-scale structure (e.g., Palanque-Delabrouille et al. 2016). Even in the nearest quasars, the emitting regions are too small to be resolved, so these targets will generally appear in images as point sources that are easily confused with stars. Quasars are $\sim 2$ mag brighter in the NIR compared to stars of similar optical magnitude and color, so we use optical photometry combined with WISE infrared photometry in the W1 and W2 bands to discriminate against contaminating stars.

During the target selection phase of SV, we tested two different methods for identifying quasar targets. The first was based on color cuts, and the second was based on random forest (RF) algorithms trained to select quasars from photometric catalogs. We also explored extensions of the initial set of photometric cuts: a relaxed definition of stellar morphology and an extension of the r-band magnitude limit to test the redshift distribution and population of fainter objects. We also tested alternative methods to the color and RF selections: a selection based on variability in the WISE light curves and a selection of high-redshift quasars based on g-band and r-band dropout techniques.

Because quasars appear at a lower density than either ELG or LRG targets, they were typically assigned fibers at the highest priority during target selection validation. As described above, several combinations of fields contained quasars. Those 12 fields with ELG targets produced quasar spectra at an average of 1300 per field. The first 31 quasar and LRG fields produced an average of roughly 1800 quasar spectra while the last 12 quasar and LRG fields produced an average of roughly 2200 quasar spectra. For further details on the target selection algorithms, see Chaussidon et al. (2023).

## 3.2. Data Reduction

The SV data were processed with a new spectroscopic pipeline developed specifically for DESI. A detailed description of this pipeline can be found in Guy et al. (2023); we provide here a brief overview.

The DESI pipeline inherits much of the philosophy from SDSS, but was fully rewritten. The most significant difference
new techniques, leading to a lower yield of LRG targets. In these fields, observations yielded roughly 1200 LRG spectra on average. In 12 fields, the quasar targets were selected according to an algorithm that more closely represented that of the main program, thus decreasing the number of quasar targets. However, because those 12 fields were observed toward the end of target selection validation, the instrument was near optimal performance, and a much larger fraction of quasar targets were assigned fibers. On average in these fields, roughly 800 LRG targets produced spectra. An analysis of the LRG target selection algorithms can be found in Zhou et al. (2023).

### 3.1.4. ELG Targets

The majority of the spectroscopic redshift measurements for DESI will come from ELGs at redshifts $0.6 < z < 1.6$. These galaxies exhibit strong nebular emission lines originating in the ionized H II regions surrounding short-lived, but luminous, massive stars (e.g., Moustakas et al. 2006). ELGs are typically late-type spiral and irregular galaxies, although any galaxy actively forming new stars at a sufficiently high rate will qualify as an ELG. Because of their vigorous ongoing star formation, the integrated rest-frame colors of ELGs are dominated by massive stars, and hence will typically be bluer than LRG and other galaxies with evolved stellar populations. This relatively blue continuum allows the efficient selection of ELG targets from optical grz-band photometry.

Selection of ELG targets for DESI leverages the fact that the cosmic star formation rate was roughly an order of magnitude higher at $z \sim 1$ than today. Galaxies with strong line-emission are therefore very common at the epoch where LRG targets become increasingly difficult to spectroscopically classify. In particular, the prominent [O II] doublet in ELG spectra consists of a pair of emission lines separated in rest-frame wavelength by 2.783 Å. This wavelength separation of the doublet provides a unique signature, allowing definitive line identification (especially in cases where the doublet is resolved, enabled by the design for spectral resolution) and secure redshift measurements from [O II] alone for a large fraction of ELG targets.

During the target selection phase of SV, ELG targets were selected to explore the relationship between redshift, [O II] line strength, and $(g-r)/(r-z)$ color. In addition, we varied the definition of the magnitude limit (either g-band fiber magnitude or g-band total magnitude) and explored the performance of the instrument and selection for fainter objects than were expected for the main selection.

A summary of the ELG observations can be found in Table 1. In total, 25 fields containing ELG targets were observed while 12 fields containing both ELG and quasar targets were observed. Not included in the table is the technical detail that ELG targets were also used as filler for remaining fibers in the 28 LRG and quasar fields. As with the other target classes, these fields covered regions with varying galactic extinction, stellar density, and imaging quality. The fields that contained ELG targets at the highest priority produced an average of roughly 3200 ELG spectra. In the fields that also contained quasar targets, ELG targets were given fibers after all quasar targets had been assigned. On average in these fields, roughly 2400 ELG targets produced spectra, although roughly 600 also satisfied the quasar selection and therefore had a higher priority in fiber assignment. For training the ELG target selection algorithms,

those 12 tiles required downweighting of the targets with overlapping ELG and quasar selections so that they represent a fair fraction of the parent ELG target sample. An analysis of the ELG target selection algorithms and the downweighting scheme can be found in Raichoor et al. (2023).

### 3.1.5. Quasar Targets

The highest-redshift spectroscopic sample for DESI will consist of quasars. We will measure large-scale structure using quasars as direct tracers of dark matter in the redshift range $0.9 < z < 2.1$. The DESI spectrographs cover the $\lambda = 1216$ Å Ly$\alpha$ transition for objects with redshift above $z \sim 2.0$. At redshifts $z = 2.1$ and higher, we will use the foreground neutral-hydrogen Ly$\alpha$ forest absorption observed in quasar spectra to measure large-scale structure. In the main DESI survey, we will obtain additional exposures on confirmed Ly$\alpha$ quasars to measure the Ly$\alpha$ forest at the highest signal-to-noise ratio (S/N) allowed under the observational constraints.

Quasars are fueled by gravitational accretion onto super-massive black holes, leading to emission that can outshine the host galaxy. These are the brightest population of nontransient $z > 1$ targets that have a density high enough to use as tracers of large-scale structure (e.g., Palanque-Delabrouille et al. 2016). Even in the nearest quasars, the emitting regions are too small to be resolved, so these targets will generally appear in images as point sources that are easily confused with stars. Quasars are $\sim 2$ mag brighter in the NIR compared to stars of similar optical magnitude and color, so we use optical photometry combined with WISE infrared photometry in the W1 and W2 bands to discriminate against contaminating stars.

During the target selection phase of SV, we tested two different methods for identifying quasar targets. The first was based on color cuts, and the second was based on random forest (RF) algorithms trained to select quasars from photometric catalogs. We also explored extensions of the initial set of photometric cuts: a relaxed definition of stellar morphology and an extension of the r-band magnitude limit to test the redshift distribution and population of fainter objects. We also tested alternative methods to the color and RF selections: a selection based on variability in the WISE light curves and a selection of high-redshift quasars based on g-band and r-band dropout techniques.

Because quasars appear at a lower density than either ELG or LRG targets, they were typically assigned fibers at the highest priority during target selection validation. As described above, several combinations of fields contained quasars. Those 12 fields with ELG targets produced quasar spectra at an average of 1300 per field. The first 31 quasar and LRG fields produced an average of roughly 1800 quasar spectra while the last 12 quasar and LRG fields produced an average of roughly 2200 quasar spectra. For further details on the target selection algorithms, see Chaussidon et al. (2023).

## 3.2. Data Reduction

The SV data were processed with a new spectroscopic pipeline developed specifically for DESI. A detailed description of this pipeline can be found in Guy et al. (2023); we provide here a brief overview.

The DESI pipeline inherits much of the philosophy from SDSS, but was fully rewritten. The most significant difference
from SDSS is the spectral extraction technique. We use here a
full forward model of the CCD image, based on a precise two-
dimensional model of the point-spread function (PSF) in each
camera. This method, proposed by Bolton & Schlegel (2010),
is more complex than the row by row extraction used in the
past as it involves solving a large linear system and requires a
post-processing method to provide uncorrelated spectral fluxes.
The advantages are an improved statistical precision, uncorre-
lated fluxes on a unique wavelength grid, and a resolution
matrix that provides a well-defined framework to account for
the spatially varying spectrograph resolution when analyzing
the spectra.

A first version of the software had been developed and tested
on image simulations and spectrograph test data before the full
instrument installation at Kitt Peak. It was further improved
during the commissioning and the SV periods. Spectroscopic
data automatically transfered to the National Energy Research
Scientific Computing Center (NERSC) were processed on a
daily basis with the most recent version of the software. During
the SV period and the following months, several reprocessing
runs of the SV data were made available to the collaboration. In
each case, the internal release was accompanied by a uniform
and documented software version.

Calibrations using a dome screen illuminated with a range of
lamps were obtained during the afternoon prior to the nightly
operations. This data set was used to determine precisely the
coordinates of spectral traces, the wavelength calibration, and
the PSF in each of the 30 CCDs for the upcoming night. Flat
field data were also acquired to correct for the nonuniformities
of the fiber transmission.

Exposures taken during each night were first pre-processed
to convert analog-to-digital convertor counts to electrons per
pixel and perform bias and dark current subtraction, pixel flat-
fielding, electronic crosstalk corrections, and assignment of bad
pixel maps. A notable difference with other pipelines was the
use of a model of the CCD image to estimate the Poisson noise
in the pixels. The spectral extraction used the afternoon
calibrations, but with the trace coordinates adjusted and the
wavelength calibration refined using sky lines. The output of
the sequence of algorithms are uncorrelated spectral fluxes for
all targets on the same wavelength grid, their variance, and a
sparse resolution matrix to convert any spectral model to the
resolution of the spectrograph. The next steps of the processing
comprised flat-fielding, sky subtraction, and spectrophoto-
metric calibration. The resolution matrix was used at each step;
for instance, a high-resolution, deconvolved sky model was
derived from the sky fiber data and then reconvolved to the
resolution of each fiber before being subtracted.

During SV, we characterized the number of sky fibers and
standard stars needed to achieve an accurate sky subtraction
and flux calibration. For the former, the driver was the redshift
efficiency and purity of ELGs for which sky line residuals can
introduce confusion. We found that we could observe with
fewer than 40 sky fibers per petal before significant degradation
of the ELG redshift efficiency. However, we conservatively
maintained at least 40 sky fibers as a requirement for each
petal. This choice has minimal impact on the rate of fiber
assignment for science targets because we include most
nonmoving or disabled fiber positioners in the list of sky
fibers. Similarly, we found that we can achieve an excellent
flux calibration precision with 10 standard stars per petal and
maintained this number as a goal for fiber assignment.

The data taken for the SV program were most recently
processed with the fuji version<fnref n="109" /> of the data reduction
pipeline. All SV data will be released publicly in 2023 using
this version of the pipeline (DESI Collaboration et al. 2023).

3.3. *Calibration of Exposure Times*

Exposure times in the main survey are tuned to achieve a relatively uniform spectral data quality over all fields, in order to optimize the survey efficiency. This tuning relies on an online ETC (D. Kirkby et al. 2023, in preparation) that determines when to end each exposure based on real-time monitoring of observing conditions. Specifically, the ETC monitors the atmospheric transparency, the fraction of light entering fibers (degraded by atmospheric seeing), and sky brightness, all using dedicated instrumentation, and estimates the accumulated S/N for different target categories. The ETC also splits exposures that are predicted to be long into shorter exposures to facilitate cosmic-ray rejection. The desired S/N takes into account the observing airmass, the average galactic extinction of the field, and the type of targets being observed.

One goal of SV was to calibrate the ETC and characterize its
performance, using the natural variations in observing condi-
tions during SV. Calibration is needed to determine zero-points
for the transparency and sky brightness measurements, and to
establish empirical scalings of spectroscopic signal-to-noise to
exposure time for different target types and under varying
observing conditions. The scatter in online ETC estimates of
overall throughput (combining the effects of varying transpar-
ency and fraction of light entering a fiber) compared with
offline spectrograph estimates was measured to be less than
5%. The corresponding scatter in ETC estimated sky back-
ground level compared with offline reductions of sky spectra
was also less than 5%.

After calibration of the throughput and background predic-
tions, an effective exposure time was defined to determine the
completion status of each field, and corresponds to an
equivalent real exposure time at airmass 1, zero galactic
extinction, 1"1 FWHM seeing, and zenith dark sky. Table 1
lists the cumulative effective exposure times achieved during
SV for different programs.

Due to chromatic effects in airmass, galactic extinction, and
other observational effects, it is not possible to determine an
effective exposure time that is consistent across all wave-
lengths. Likewise, given the varying location of spectroscopic
features in observer-frame wavelength, it is not possible to
determine an effective exposure time that is appropriate to all
spectroscopic targets. Instead, we designated a fiducial target
profile on the sky for each observation, selected from point-
like, ELG-like, or BGS-like. The target profile contributes to
the fraction of light entering a fiber, along with the atmospheric
and instrumental PSFs, with a larger profile reducing the
fraction but also reducing the sensitivity to atmospheric seeing.
We also accounted for the different expected contributions of
sensor read noise from faint and bright sources.

In addition to estimating effective exposure times, the ETC
monitors the survey speed as an indicator of the rate at which
effective time is being accumulated. Survey speed is the
instantaneous measure of the signal accumulation in the sky-
noise limit. This ignores read noise, Poisson noise, and the
effects of atmospheric absorption beyond airmass =1 or

<footnote n="109">¹⁰⁹ https://github.com/desihub/desispec/releases/tag/0.51.13</footnote>
from SDSS is the spectral extraction technique. We use here a
full forward model of the CCD image, based on a precise two-
dimensional model of the point-spread function (PSF) in each
camera. This method, proposed by Bolton & Schlegel (2010),
is more complex than the row by row extraction used in the
past as it involves solving a large linear system and requires a
post-processing method to provide uncorrelated spectral fluxes.
The advantages are an improved statistical precision, uncorre-
lated fluxes on a unique wavelength grid, and a resolution
matrix that provides a well-defined framework to account for
the spatially varying spectrograph resolution when analyzing
the spectra.

A first version of the software had been developed and tested
on image simulations and spectrograph test data before the full
instrument installation at Kitt Peak. It was further improved
during the commissioning and the SV periods. Spectroscopic
data automatically transfered to the National Energy Research
Scientific Computing Center (NERSC) were processed on a
daily basis with the most recent version of the software. During
the SV period and the following months, several reprocessing
runs of the SV data were made available to the collaboration. In
each case, the internal release was accompanied by a uniform
and documented software version.

Calibrations using a dome screen illuminated with a range of
lamps were obtained during the afternoon prior to the nightly
operations. This data set was used to determine precisely the
coordinates of spectral traces, the wavelength calibration, and
the PSF in each of the 30 CCDs for the upcoming night. Flat
field data were also acquired to correct for the nonuniformities
of the fiber transmission.

Exposures taken during each night were first pre-processed
to convert analog-to-digital convertor counts to electrons per
pixel and perform bias and dark current subtraction, pixel flat-
fielding, electronic crosstalk corrections, and assignment of bad
pixel maps. A notable difference with other pipelines was the
use of a model of the CCD image to estimate the Poisson noise
in the pixels. The spectral extraction used the afternoon
calibrations, but with the trace coordinates adjusted and the
wavelength calibration refined using sky lines. The output of
the sequence of algorithms are uncorrelated spectral fluxes for
all targets on the same wavelength grid, their variance, and a
sparse resolution matrix to convert any spectral model to the
resolution of the spectrograph. The next steps of the processing
comprised flat-fielding, sky subtraction, and spectrophoto-
metric calibration. The resolution matrix was used at each step;
for instance, a high-resolution, deconvolved sky model was
derived from the sky fiber data and then reconvolved to the
resolution of each fiber before being subtracted.

During SV, we characterized the number of sky fibers and
standard stars needed to achieve an accurate sky subtraction
and flux calibration. For the former, the driver was the redshift
efficiency and purity of ELGs for which sky line residuals can
introduce confusion. We found that we could observe with
fewer than 40 sky fibers per petal before significant degradation
of the ELG redshift efficiency. However, we conservatively
maintained at least 40 sky fibers as a requirement for each
petal. This choice has minimal impact on the rate of fiber
assignment for science targets because we include most
nonmoving or disabled fiber positioners in the list of sky
fibers. Similarly, we found that we can achieve an excellent
flux calibration precision with 10 standard stars per petal and
maintained this number as a goal for fiber assignment.

The data taken for the SV program were most recently
processed with the fuji version<fnref n="109" /> of the data reduction
pipeline. All SV data will be released publicly in 2023 using
this version of the pipeline (DESI Collaboration et al. 2023).

3.3. Calibration of Exposure Times

Exposure times in the main survey are tuned to achieve a
relatively uniform spectral data quality over all fields, in order
to optimize the survey efficiency. This tuning relies on an
online ETC (D. Kirkby et al. 2023, in preparation) that
determines when to end each exposure based on real-time
monitoring of observing conditions. Specifically, the ETC
monitors the atmospheric transparency, the fraction of light
entering fibers (degraded by atmospheric seeing), and sky
brightness, all using dedicated instrumentation, and estimates
the accumulated S/N for different target categories. The ETC
also splits exposures that are predicted to be long into shorter
exposures to facilitate cosmic-ray rejection. The desired S/N
takes into account the observing airmass, the average galactic
extinction of the field, and the type of targets being observed.

One goal of SV was to calibrate the ETC and characterize its
performance, using the natural variations in observing condi-
tions during SV. Calibration is needed to determine zero-points
for the transparency and sky brightness measurements, and to
establish empirical scalings of spectroscopic signal-to-noise to
exposure time for different target types and under varying
observing conditions. The scatter in online ETC estimates of
overall throughput (combining the effects of varying transpar-
ency and fraction of light entering a fiber) compared with
offline spectrograph estimates was measured to be less than
5%. The corresponding scatter in ETC estimated sky back-
ground level compared with offline reductions of sky spectra
was also less than 5%.

After calibration of the throughput and background predic-
tions, an effective exposure time was defined to determine the
completion status of each field, and corresponds to an
equivalent real exposure time at airmass 1, zero galactic
extinction, 1"1 FWHM seeing, and zenith dark sky. Table 1
lists the cumulative effective exposure times achieved during
SV for different programs.

Due to chromatic effects in airmass, galactic extinction, and
other observational effects, it is not possible to determine an
effective exposure time that is consistent across all wave-
lengths. Likewise, given the varying location of spectroscopic
features in observer-frame wavelength, it is not possible to
determine an effective exposure time that is appropriate to all
spectroscopic targets. Instead, we designated a fiducial target
profile on the sky for each observation, selected from point-
like, ELG-like, or BGS-like. The target profile contributes to
the fraction of light entering a fiber, along with the atmospheric
and instrumental PSFs, with a larger profile reducing the
fraction but also reducing the sensitivity to atmospheric seeing.
We also accounted for the different expected contributions of
sensor read noise from faint and bright sources.

In addition to estimating effective exposure times, the ETC
monitors the survey speed as an indicator of the rate at which
effective time is being accumulated. Survey speed is the
instantaneous measure of the signal accumulation in the sky-
noise limit. This ignores read noise, Poisson noise, and the
effects of atmospheric absorption beyond airmass =1 or

<footnote n="109">¹⁰⁹ https://github.com/desihub/desispec/releases/tag/0.51.13</footnote>
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Example of the sky coverage of one DESI tile centered at $(\alpha, \delta) = (0, 0)$. The white circles display the individual fiber patrol regions. Left: an image that spans 4° on a side, illustrating the entire DESI focal plane. Right: the smaller region identified by the red square in the left panel. DESI Main Survey dark targets are circled (LRGs in red, ELGs in cyan, and quasars in yellow). The background imaging is a grz-band composite image from the DESI Legacy Surveys. The region was chosen to demonstrate that some fibers have limited number of targets accessible while others can be heavily oversubscribed. For example, even though this region will be covered by five or more tiles, the six ELG targets accessible to the central fiber close to the bottom of the image are unlikely to be observed because there is a quasar and seven LRG targets also within reach. There are also regions that are not accessible to fibers at all. For example, the hole near the middle of the right panel is the location of a fiducial that is used to calibrate the focal plane coordinate system.</figcaption></figure>

Galactic extinction. This is a measure of the sky conditions, normalized such that a value of 1 represents typical transparency, seeing, and background, while smaller values represent observations in degraded conditions. The survey speed informs automated decisions about when to switch from BGS and MWS targets, observed mostly during bright moon conditions, to LRG, ELG, and quasar targets, observed mostly during dark sky conditions.

## 4. Target Selection and One-Percent Survey

The spectroscopic footprint for the full DESI program will cover two large footprints, one in each hemisphere of the Galactic sky. Over that 14,900 deg² footprint, we will observe MWS, BGS, LRG, ELG, and quasar targets using selection algorithms informed by the SV data. After considering edge effects, these targets will be observed to high completeness in the central 14,000 deg², as intended in the instrument and survey design.

As demonstrated in Figure 2, there are typically multiple targets accessible to any one fiber in a DESI pointing. In the main dark time survey, quasar targets will be assigned fibers at the highest priority, followed by LRG and then ELG targets. Because the sky is covered with multiple layers of tiles, each coordinate on the sky has roughly five opportunities of fiber assignment, allowing high completeness even for the ELG targets. During the times when BGS and MWS targets are observed, BGS targets will be given the higher priority assignment. The algorithms have been finalized for prioritizing targets in the assignment of fibers. Those algorithms, including a full description of the priority scheme, are found in A. Raichoor et al. (2023, in preparation).

A preliminary run has been performed for all targets over the full footprint, leading to estimates of the fraction of targets

from each sample that will be assigned a fiber. The efficiency of the fiber assignment algorithm for each target class depends on density, clustering, and priority. However, for targets that are assigned a fiber, roughly 1% of objects targeted as a galaxy will produce failed spectra that are not appropriate for spectral classification. Those spectra may have had improper positioner placement, significant contamination from cosmetic defects on the detector, or other concerns. Quasar targets are not expected to suffer from this source of incompleteness because they are given highest priority in fiber assignment and are typically reobserved when such an issue arises (Schlafly et al. 2023). In this section, we present the imaging data, the final target selection algorithms, and the fraction of targets within the central 14,000 deg² that are expected to get a meaningful spectrum.<fnref n="110" />

Immediately following the completion of target selection validation observations, we conducted a survey of 140 deg² in all five target classes. Titled “One-Percent Survey,” these observations produced spectroscopic samples to significantly higher fiber assignment completeness and redshift success completeness than will be obtained in the full program. We conclude this section with an overview of the One-Percent Survey.

### 4.1. Imaging Data

The photometric catalogs used for identifying DESI targets are derived from three optical imaging surveys in combination with data from WISE and Gaia. The three optical surveys were designed for DESI target selection in the grz bands at depths that were appropriate for the selection of $z > 1$ ELG targets.

<footnote n="110">¹¹⁰ The reported area does not correct for regions lost to masking, which account for roughly 1% of the footprint.</footnote>
<figure><img src="image_6.png" /><figcaption>**Figure 2.** Example of the sky coverage of one DESI tile centered at $(\alpha, \delta) = (0, 0)$. The white circles display the individual fiber patrol regions. Left: an image that spans 4° on a side, illustrating the entire DESI focal plane. Right: the smaller region identified by the red square in the left panel. DESI Main Survey dark targets are circled (LRGs in red, ELGs in cyan, and quasars in yellow). The background imaging is a grz-band composite image from the DESI Legacy Surveys. The region was chosen to demonstrate that some fibers have limited number of targets accessible while others can be heavily oversubscribed. For example, even though this region will be covered by five or more tiles, the six ELG targets accessible to the central fiber close to the bottom of the image are unlikely to be observed because there is a quasar and seven LRG targets also within reach. There are also regions that are not accessible to fibers at all. For example, the hole near the middle of the right panel is the location of a fiducial that is used to calibrate the focal plane coordinate system.</figcaption></figure>

Galactic extinction. This is a measure of the sky conditions, normalized such that a value of 1 represents typical transparency, seeing, and background, while smaller values represent observations in degraded conditions. The survey speed informs automated decisions about when to switch from BGS and MWS targets, observed mostly during bright moon conditions, to LRG, ELG, and quasar targets, observed mostly during dark sky conditions.

## 4. Target Selection and One-Percent Survey

The spectroscopic footprint for the full DESI program will cover two large footprints, one in each hemisphere of the Galactic sky. Over that 14,900 deg² footprint, we will observe MWS, BGS, LRG, ELG, and quasar targets using selection algorithms informed by the SV data. After considering edge effects, these targets will be observed to high completeness in the central 14,000 deg², as intended in the instrument and survey design.

As demonstrated in Figure 2, there are typically multiple targets accessible to any one fiber in a DESI pointing. In the main dark time survey, quasar targets will be assigned fibers at the highest priority, followed by LRG and then ELG targets. Because the sky is covered with multiple layers of tiles, each coordinate on the sky has roughly five opportunities of fiber assignment, allowing high completeness even for the ELG targets. During the times when BGS and MWS targets are observed, BGS targets will be given the higher priority assignment. The algorithms have been finalized for prioritizing targets in the assignment of fibers. Those algorithms, including a full description of the priority scheme, are found in A. Raichoor et al. (2023, in preparation).

A preliminary run has been performed for all targets over the full footprint, leading to estimates of the fraction of targets

from each sample that will be assigned a fiber. The efficiency of the fiber assignment algorithm for each target class depends on density, clustering, and priority. However, for targets that are assigned a fiber, roughly 1% of objects targeted as a galaxy will produce failed spectra that are not appropriate for spectral classification. Those spectra may have had improper positioner placement, significant contamination from cosmetic defects on the detector, or other concerns. Quasar targets are not expected to suffer from this source of incompleteness because they are given highest priority in fiber assignment and are typically reobserved when such an issue arises (Schlafly et al. 2023). In this section, we present the imaging data, the final target selection algorithms, and the fraction of targets within the central 14,000 deg² that are expected to get a meaningful spectrum.<fnref n="110" />

Immediately following the completion of target selection validation observations, we conducted a survey of 140 deg² in all five target classes. Titled “One-Percent Survey,” these observations produced spectroscopic samples to significantly higher fiber assignment completeness and redshift success completeness than will be obtained in the full program. We conclude this section with an overview of the One-Percent Survey.

### 4.1. Imaging Data

The photometric catalogs used for identifying DESI targets are derived from three optical imaging surveys in combination with data from WISE and Gaia. The three optical surveys were designed for DESI target selection in the grz bands at depths that were appropriate for the selection of $z > 1$ ELG targets.

<footnote n="110">¹¹⁰ The reported area does not correct for regions lost to masking, which account for roughly 1% of the footprint.</footnote>
<table><caption>**Table 2**
Imaging Statistics for Selection of Targets in Survey Validation and the Main DESI Survey</caption><thead><tr><th></th><th>BASS+MzLS</th><th>DECaLS</th></tr></thead><tbody><tr><td>Area [deg<sup>2</sup>]</td><td>5,170</td><td>11,717</td></tr><tr><td>Median g depth [mag]</td><td>24.29</td><td>24.81</td></tr><tr><td>Median r depth [mag]</td><td>23.72</td><td>24.24</td></tr><tr><td>Median z depth [mag]</td><td>23.33</td><td>23.34</td></tr><tr><td>Median W1 depth [mag]</td><td>21.59</td><td>21.37</td></tr><tr><td>Median W2 depth [mag]</td><td>21.02</td><td>20.71</td></tr><tr><td>Median g seeing [arcsec]</td><td>1.90</td><td>1.49</td></tr><tr><td>Median r seeing [arcsec]</td><td>1.68</td><td>1.36</td></tr><tr><td>Median z seeing [arcsec]</td><td>1.24</td><td>1.28</td></tr></tbody></table>

Note. Areal coverage and other parameters for the BASS, MzLS, and DECaLS imaging surveys limited to the sky area at $\delta > -30^{\circ}$ with coverage in all of the bands. Median depths are for point sources detected at $5\sigma$. Median seeing values are computed with a depth-weighted average at each location on the sky. There are 370 deg$^2$ with imaging in all three filters in the overlap area between DECaLS and BASS+MzLS.

Additional photometry from WISE allows selection of BGS, LRG, and QSO targets while the small PSF of the Gaia G-band data allows $G-r$ to be used as an accurate star-galaxy separator in the BGS sample, and the Gaia astrometric data facilitates stellar selections.

The Dark Energy Camera Legacy Survey (DECaLS) was the largest of the three dedicated surveys. DECaLS imaging in the grz filters was collected in a three-pass observing strategy using the Dark Energy Camera (DECam; Flaugher et al. 2015) at the 4 m Blanco telescope. DECaLS made use of existing grz DECam data from other programs where available, most notably the Dark Energy Survey (DES; Dark Energy Survey Collaboration et al. 2016). DECaLS is the sole source of grz imaging used for selecting targets in the south Galactic cap. The north Galactic cap (NGC) coverage from DECaLS was limited to $\delta < 34^{\circ}$ due to the constraints of observing at a high airmass.

In the NGC, at decl. $\delta > 32^{\circ}37.5$, imaging from two coordinated programs from the Kitt Peak National Observatory is used for selecting targets. Imaging was performed using the g-band and r-band filters in the Beijing-Arizona Sky Survey (BASS; Zou et al. 2017) using the 90Prime camera at the 2.3 m Bok telescope. Imaging in the z-band filter was performed in the Mosaic z-band Legacy Survey (MzLS) using the 4 m Mayall telescope. An upgraded camera (Mosaic-3; Dey et al. 2016) with 4 k × 4 k, thick, deep-depletion CCDs were installed at prime focus specifically to obtain better quantum efficiency at red wavelengths before starting this program.

The imaging area above $\delta > -30^{\circ}$ and median $5\sigma$ point-source magnitudes for each of the three grz-band imaging surveys is found in Table 2. This imaging area defines the region accessible to DESI at a reasonable airmass.

The full processing for all imaging data follows an approach similar to that in Dey et al. (2019), with several improvements in data reduction algorithms. The grz photometric catalogs were created using common positions and profiles to model the flux for each source across all images following the Tractor (Lang et al. 2016a) methodology. These models were also used to compute new photometric measurements in all four of the WISE bandpasses, as in Lang (2014), Lang et al. (2016b), and Meisner et al. (2017). The exposure times for the 3.4 and

4.6 µm bands are 7 times longer than those used in the original WISE all-sky survey catalogs (Cutri et al. 2021). In addition, the application of Tractor to determine WISE photometry results in less confusion in extended sources due to the higher-resolution optical references used to derive source models. For targets in SV, the forced photometry on the grz and WISE images was complemented by the photometric and astrometric data from the second public data release from Gaia (Gaia Collaboration et al. 2018). The Legacy Survey images and photometric catalogs can be found in the ninth public data release of the DESI imaging surveys (D. J. Schlegel et al. 2023, in preparation).<fnref n="111" /> These catalogs were used to identify targets for the main survey, with one exception (Myers et al. 2023, Section 4.1.4): the astrometric data for all MWS targets are taken from the Gaia Early Data Release 3 catalogs (Gaia Collaboration et al. 2021). All catalog fluxes are reported without applying Galactic extinction corrections, but target selections are extinction-corrected (Schlegel et al. 1998) except where noted.

## 4.2. Milky Way Survey (MWS)

Details of the scientific motivation and selection criteria for the MWS are presented in Cooper et al. (2023). We summarize the target selection strategy here.

As shown in Figure 3, the MWS is defined by three main target classes comprising an essentially magnitude-limited sample of stars in the range $16<r<19$. We apply simple star-galaxy separation<fnref n="112" /> based on Gaia EDR3 (astrometric_excess noise <3) to Legacy Survey PSF sources and exclude sources with missing or contaminated photometry in g and r. No further selection is applied to sources bluer than $g-r=0.7$ (MWS Main Blue). Redder sources with small Gaia parallax and proper motion (MWS Main Red), consistent with giant stars in the Milky Way's stellar halo, are separated from those more likely to be dwarf stars in the Milky Way disk (MWS Main Broad). The sources lacking Gaia astrometry are assigned to Main Broad. The proper-motion separation between Main Red and Main Broad increases as the square root of flux from 5 mas yr$^{-1}$ at $r=19$ to 20 mas yr$^{-1}$, at $r=16$ (shown by the hatched region, $0.7 < \log_{10}|\mu|/\text{mas yr}^{-1} < 1.3$, in Figure 3). The separation in parallax is $3\sigma_{\pi} + 0.3$ mas where $\sigma_{\pi}$ is the parallax uncertainty reported by Gaia. We impose an additional cut on uncorrected flux at $r_{\text{obs}} < 20$.

Main Red and Main Blue targets will be given equal fiber assignment priority in the main survey program, while Main Broad targets will have lower priority. After fiber assignment, the final data set is expected to be approximately 33% complete for Main Red and Main Blue targets, with slightly lower completeness for Main Broad. By default, these MWS targets will be observed at most once during the main survey.

In addition, MWS defines several classes of targets with very low surface density and high scientific value. These comprise white dwarf stars (selected by Gaia photometry and astrometry), stars within 100 pc of the Sun (selected by Gaia parallax), Blue Horizontal Branch (BHB) stars (selected by Legacy Survey and WISE colors), and RR Lyrae variables (selected from the Gaia variability catalog). In the immediate vicinity of several dwarf galaxies, globular clusters, and open

<footnote n="111">¹¹¹ https://www.legacysurvey.org/dr9/</footnote>

<footnote n="112">¹¹² The MWS star-galaxy separation is different from that used for the Bright Galaxy Survey, described below.</footnote>
<table><caption>**Table 2**
Imaging Statistics for Selection of Targets in Survey Validation and the Main DESI Survey</caption><thead><tr><th></th><th>BASS+MzLS</th><th>DECaLS</th></tr></thead><tbody><tr><td>Area [deg<sup>2</sup>]</td><td>5,170</td><td>11,717</td></tr><tr><td>Median g depth [mag]</td><td>24.29</td><td>24.81</td></tr><tr><td>Median r depth [mag]</td><td>23.72</td><td>24.24</td></tr><tr><td>Median z depth [mag]</td><td>23.33</td><td>23.34</td></tr><tr><td>Median W1 depth [mag]</td><td>21.59</td><td>21.37</td></tr><tr><td>Median W2 depth [mag]</td><td>21.02</td><td>20.71</td></tr><tr><td>Median g seeing [arcsec]</td><td>1.90</td><td>1.49</td></tr><tr><td>Median r seeing [arcsec]</td><td>1.68</td><td>1.36</td></tr><tr><td>Median z seeing [arcsec]</td><td>1.24</td><td>1.28</td></tr></tbody></table>

Note. Areal coverage and other parameters for the BASS, MzLS, and DECaLS imaging surveys limited to the sky area at $\delta > -30^{\circ}$ with coverage in all of the bands. Median depths are for point sources detected at $5\sigma$. Median seeing values are computed with a depth-weighted average at each location on the sky. There are 370 deg$^2$ with imaging in all three filters in the overlap area between DECaLS and BASS+MzLS.

Additional photometry from WISE allows selection of BGS, LRG, and QSO targets while the small PSF of the Gaia G-band data allows $G-r$ to be used as an accurate star-galaxy separator in the BGS sample, and the Gaia astrometric data facilitates stellar selections.

The Dark Energy Camera Legacy Survey (DECaLS) was the largest of the three dedicated surveys. DECaLS imaging in the grz filters was collected in a three-pass observing strategy using the Dark Energy Camera (DECam; Flaugher et al. 2015) at the 4 m Blanco telescope. DECaLS made use of existing grz DECam data from other programs where available, most notably the Dark Energy Survey (DES; Dark Energy Survey Collaboration et al. 2016). DECaLS is the sole source of grz imaging used for selecting targets in the south Galactic cap. The north Galactic cap (NGC) coverage from DECaLS was limited to $\delta < 34^{\circ}$ due to the constraints of observing at a high airmass.

In the NGC, at decl. $\delta > 32^{\circ}37.5$, imaging from two coordinated programs from the Kitt Peak National Observatory is used for selecting targets. Imaging was performed using the g-band and r-band filters in the Beijing-Arizona Sky Survey (BASS; Zou et al. 2017) using the 90Prime camera at the 2.3 m Bok telescope. Imaging in the z-band filter was performed in the Mosaic z-band Legacy Survey (MzLS) using the 4 m Mayall telescope. An upgraded camera (Mosaic-3; Dey et al. 2016) with 4 k × 4 k, thick, deep-depletion CCDs were installed at prime focus specifically to obtain better quantum efficiency at red wavelengths before starting this program.

The imaging area above $\delta > -30^{\circ}$ and median $5\sigma$ point-source magnitudes for each of the three grz-band imaging surveys is found in Table 2. This imaging area defines the region accessible to DESI at a reasonable airmass.

The full processing for all imaging data follows an approach similar to that in Dey et al. (2019), with several improvements in data reduction algorithms. The grz photometric catalogs were created using common positions and profiles to model the flux for each source across all images following the Tractor (Lang et al. 2016a) methodology. These models were also used to compute new photometric measurements in all four of the WISE bandpasses, as in Lang (2014), Lang et al. (2016b), and Meisner et al. (2017). The exposure times for the 3.4 and

4.6 µm bands are 7 times longer than those used in the original WISE all-sky survey catalogs (Cutri et al. 2021). In addition, the application of Tractor to determine WISE photometry results in less confusion in extended sources due to the higher-resolution optical references used to derive source models. For targets in SV, the forced photometry on the grz and WISE images was complemented by the photometric and astrometric data from the second public data release from Gaia (Gaia Collaboration et al. 2018). The Legacy Survey images and photometric catalogs can be found in the ninth public data release of the DESI imaging surveys (D. J. Schlegel et al. 2023, in preparation).<fnref n="111" /> These catalogs were used to identify targets for the main survey, with one exception (Myers et al. 2023, Section 4.1.4): the astrometric data for all MWS targets are taken from the Gaia Early Data Release 3 catalogs (Gaia Collaboration et al. 2021). All catalog fluxes are reported without applying Galactic extinction corrections, but target selections are extinction-corrected (Schlegel et al. 1998) except where noted.

## 4.2. Milky Way Survey (MWS)

Details of the scientific motivation and selection criteria for the MWS are presented in Cooper et al. (2023). We summarize the target selection strategy here.

As shown in Figure 3, the MWS is defined by three main target classes comprising an essentially magnitude-limited sample of stars in the range $16<r<19$. We apply simple star-galaxy separation<fnref n="112" /> based on Gaia EDR3 (astrometric_excess noise <3) to Legacy Survey PSF sources and exclude sources with missing or contaminated photometry in g and r. No further selection is applied to sources bluer than $g-r=0.7$ (MWS Main Blue). Redder sources with small Gaia parallax and proper motion (MWS Main Red), consistent with giant stars in the Milky Way's stellar halo, are separated from those more likely to be dwarf stars in the Milky Way disk (MWS Main Broad). The sources lacking Gaia astrometry are assigned to Main Broad. The proper-motion separation between Main Red and Main Broad increases as the square root of flux from 5 mas yr$^{-1}$ at $r=19$ to 20 mas yr$^{-1}$, at $r=16$ (shown by the hatched region, $0.7 < \log_{10}|\mu|/\text{mas yr}^{-1} < 1.3$, in Figure 3). The separation in parallax is $3\sigma_{\pi} + 0.3$ mas where $\sigma_{\pi}$ is the parallax uncertainty reported by Gaia. We impose an additional cut on uncorrected flux at $r_{\text{obs}} < 20$.

Main Red and Main Blue targets will be given equal fiber assignment priority in the main survey program, while Main Broad targets will have lower priority. After fiber assignment, the final data set is expected to be approximately 33% complete for Main Red and Main Blue targets, with slightly lower completeness for Main Broad. By default, these MWS targets will be observed at most once during the main survey.

In addition, MWS defines several classes of targets with very low surface density and high scientific value. These comprise white dwarf stars (selected by Gaia photometry and astrometry), stars within 100 pc of the Sun (selected by Gaia parallax), Blue Horizontal Branch (BHB) stars (selected by Legacy Survey and WISE colors), and RR Lyrae variables (selected from the Gaia variability catalog). In the immediate vicinity of several dwarf galaxies, globular clusters, and open

<footnote n="111">¹¹¹ https://www.legacysurvey.org/dr9/</footnote>

<footnote n="112">¹¹² The MWS star-galaxy separation is different from that used for the Bright Galaxy Survey, described below.</footnote>
<figure><img src="image_6.png" /><figcaption>**Figure 3.** The distribution of stellar targets for the Milky Way Survey program as a function of color and proper motion. The two density peaks correspond to the thin disk (redder colors, higher proper motions) and the metal-poor halo and thick disk (bluer colors, lower proper motions). The blue-, red-, and green-shaded regions indicate the three primary MWS target classes. All stars in the magnitude range $16 < r < 19$ are selected in one of these three categories. We do not apply any proper-motion selection to stars bluer than $g - r < 0.7$ (MWS Main Blue, blue region). We divide redder stars ($g - r > 0.7$) into MWS Main Broad (higher proper-motion, green region) and MWS Main Red (lower proper-motion, red region) using a magnitude-dependent threshold, shown by the hatched region. We give MWS Main Red stars the same fiber assignment priority as those in the MWS Main Blue sample, because they are more likely to be distant giant stars in the stellar halo. Conversely, we give MS Main Broad stars a lower fiber assignment priority. We use a more stringent proper-motion threshold for fainter stars because true giants at larger distances have lower proper motions: the fiber assignment priority of a larger fraction of nearby disk stars can then be reduced without introducing a bias against high velocity giants.</figcaption></figure>

clusters in the DESI footprint, higher priority will be given to stars most likely to be associated with those objects, based on Gaia astrometry. These additional target categories are prioritized above the three main MWS target classes but have lower priority than BGS targets. An exception is made for white dwarfs, which are given higher priority than BGS galaxies because they are especially valuable both scientifically and as an additional test of flux calibration for all DESI observations. We refer to Cooper et al. (2023) for details of the selection and relative prioritization of these sparse targets.

There is significant overlap between MWS Main Blue targets and the selection of metal-poor, F-type spectrophotometric standards used for all DESI survey observations. Since standard stars will be observed in dark and bright conditions, their completeness (and the number of repeat observations) will be significantly higher than that for the Main Blue sample as a whole.

The fiber assignment efficiency on MWS targets described above is expected to be 28% from a sample with average density $1637 \text{ deg}^{-2}$ over the 14,000 deg$^2$ footprint. Stellar densities are significantly higher toward the edges of the DESI footprint, which probe lower galactic latitudes. To make use of any otherwise unallocated fibers in bright time observations, MWS further defines Faint Blue and Faint Red selections, separated at $g - r = 0.7$ as for Main Blue and Main Red, but

with magnitudes $19 < r < 20$. A weak selection for giant stars on Gaia astrometry is applied to Faint Red. The completeness of these samples, which have the lowest priority of all DESI targets, is expected to be $\lesssim 5\%$ after fiber assignment. Adding these sources and including the entire footprint brings the total spectroscopic sample to 7.2 million stars, as reported in the abstract of this paper and in Cooper et al. (2023).

### 4.3. Bright Galaxy Survey (BGS)

The details of the final BGS selection are presented in Hahn et al. (2023). The BGS sample is a two tier, flux-limited, *r*-band selected sample of galaxies. The first tier (BGS Bright) is defined by the magnitude limit $r \le 19.5$ in the DECaLs imaging areas, with a limit $r \le 19.54$ in the BASS and MzLS imaging areas to produce the same surface density of targets. This target selection is inspired by the target selection algorithm for the SDSS main galaxy sample (Strauss et al. 2002) and was chosen so that the sample includes a broad range of galaxy populations. The second tier (BGS Faint) extends the sample $r \le 20.175$.

To achieve a high completeness in galaxy targets while reducing contamination from stellar targets, we apply a star–galaxy separation for both BGS samples. The stars with apparent magnitude $r \lesssim 20$ are sufficiently bright that they are present in the Gaia DR2 catalog. The lower surface brightness targets with $r \lesssim 20$ that are not in the Gaia catalog are, therefore, likely to be galaxies and are included in the BGS sample. For the objects that are in the Gaia catalogs, we compare the *G*-band magnitude from Gaia ($G_{Gaia}$) and *r*-band magnitude from DR9 Legacy imaging. Given the similarity of the $G_{Gaia}$ bandpass to that of the *r*-band filter, the quantity $G_{Gaia} - r$ represents the difference between a PSF-fitted magnitude and a total magnitude. As shown in the left panel of Figure 4, extended galaxies have a large $G_{Gaia} - r$ color while the locus of stars appears near ($G_{Gaia} - r$) = 0, with only a weak color dependence. For both BGS samples, we include objects with ($G_{Gaia} - r$) > 0.6 as galaxy targets.

Several additional cuts were applied to the BGS samples to reduce contamination from spurious targets that do not typically produce a valuable spectrum. First, we mask regions of the sky surrounding bright stars and globular clusters since these regions are typically contaminated by features such as extended halos, bleed trails, and diffraction spikes. As an additional quality cut to remove the spurious signal, we discard targets for which there are no data in one of the three *grz* optical bands. We use a fiber-magnitude cut to suppress the spurious signal that typically arises from imaging artifacts or fragments of *shredded* galaxies (see middle panel of Figure 4). Finally, we also remove spurious objects with extreme colors from the BGS targets and very bright objects ($r > 12$ and $r_{fiber} < 15$), which may pollute neighboring faint fibers during DESI observations.

Without additional filters, the BGS Faint selection would include many faint galaxies that would significantly reduce the redshift success rate of the sample. To preferentially sample star-forming galaxies with strong emission lines (Kochanek et al. 2012) and thus maintain a high-redshift efficiency, we require a selection based on an *r*-band fiber aperture magnitude and $(z - W1) - 1.2(g - r) + 1.2$ color as shown in the right panel of Figure 4.

In addition to the BGS Bright and BGS Faint samples, BGS includes a supplementary selection to recover active galactic
<figure><img src="image_7.png" /><figcaption>**Figure 3.** The distribution of stellar targets for the Milky Way Survey program as a function of color and proper motion. The two density peaks correspond to the thin disk (redder colors, higher proper motions) and the metal-poor halo and thick disk (bluer colors, lower proper motions). The blue-, red-, and green-shaded regions indicate the three primary MWS target classes. All stars in the magnitude range 16 < *r* < 19 are selected in one of these three categories. We do not apply any proper-motion selection to stars bluer than *g* − *r* < 0.7 (MWS Main Blue, blue region). We divide redder stars (*(g* − *r*) > 0.7) into MWS Main Broad (higher proper-motion, green region) and MWS Main Red (lower proper-motion, red region) using a magnitude-dependent threshold, shown by the hatched region. We give MWS Main Red stars the same fiber assignment priority as those in the MWS Main Blue sample, because they are more likely to be distant giant stars in the stellar halo. Conversely, we give MS Main Broad stars a lower fiber assignment priority. We use a more stringent proper-motion threshold for fainter stars because true giants at larger distances have lower proper motions: the fiber assignment priority of a larger fraction of nearby disk stars can then be reduced without introducing a bias against high velocity giants.</figcaption></figure>

clusters in the DESI footprint, higher priority will be given to
stars most likely to be associated with those objects, based on
Gaia astrometry. These additional target categories are
prioritized above the three main MWS target classes but have
lower priority than BGS targets. An exception is made for
white dwarfs, which are given higher priority than BGS
galaxies because they are especially valuable both scientifically
and as an additional test of flux calibration for all DESI
observations. We refer to Cooper et al. (2023) for details of the
selection and relative prioritization of these sparse targets.

There is significant overlap between MWS Main Blue targets
and the selection of metal-poor, F-type spectrophotometric
standards used for all DESI survey observations. Since
standard stars will be observed in dark and bright conditions,
their completeness (and the number of repeat observations) will
be significantly higher than that for the Main Blue sample as a
whole.

The fiber assignment efficiency on MWS targets described
above is expected to be 28% from a sample with average
density 1637 deg⁻² over the 14,000 deg² footprint. Stellar
densities are significantly higher toward the edges of the DESI
footprint, which probe lower galactic latitudes. To make use of
any otherwise unallocated fibers in bright time observations,
MWS further defines Faint Blue and Faint Red selections,
separated at g − r = 0.7 as for Main Blue and Main Red, but

with magnitudes 19 < *r* < 20. A weak selection for giant stars
on Gaia astrometry is applied to Faint Red. The completeness
of these samples, which have the lowest priority of all DESI
targets, is expected to be ≲5% after fiber assignment. Adding
these sources and including the entire footprint brings the total
spectroscopic sample to 7.2 million stars, as reported in the
abstract of this paper and in Cooper et al. (2023).

4.3. Bright Galaxy Survey (BGS)

The details of the final BGS selection are presented in Hahn
et al. (2023). The BGS sample is a two tier, flux-limited, r-band
selected sample of galaxies. The first tier (BGS Bright) is
defined by the magnitude limit *r* ≤ 19.5 in the DECaLs
imaging areas, with a limit *r* ≤ 19.54 in the BASS and MzLS
imaging areas to produce the same surface density of targets.
This target selection is inspired by the target selection
algorithm for the SDSS main galaxy sample (Strauss et al.
2002) and was chosen so that the sample includes a broad
range of galaxy populations. The second tier (BGS Faint)
extends the sample *r* ≤ 20.175.

To achieve a high completeness in galaxy targets while
reducing contamination from stellar targets, we apply a star-
galaxy separation for both BGS samples. The stars with
apparent magnitude *r* ≲ 20 are sufficiently bright that they are
present in the Gaia DR2 catalog. The lower surface brightness
targets with *r* ≲ 20 that are not in the Gaia catalog are,
therefore, likely to be galaxies and are included in the BGS
sample. For the objects that are in the Gaia catalogs, we
compare the *G*-band magnitude from Gaia (G<sub>Gaia</sub>) and *r*-band
magnitude from DR9 Legacy imaging. Given the similarity of
the G<sub>Gaia</sub> bandpass to that of the *r*-band filter, the quantity
G<sub>Gaia</sub> − *r* represents the difference between a PSF-fitted
magnitude and a total magnitude. As shown in the left panel of
Figure 4, extended galaxies have a large G<sub>Gaia</sub> − *r* color while
the locus of stars appears near (G<sub>Gaia</sub> − *r*) = 0, with only a
weak color dependence. For both BGS samples, we include
objects with (G<sub>Gaia</sub> − *r*) > 0.6 as galaxy targets.

Several additional cuts were applied to the BGS samples to
reduce contamination from spurious targets that do not
typically produce a valuable spectrum. First, we mask regions
of the sky surrounding bright stars and globular clusters since
these regions are typically contaminated by features such as
extended halos, bleed trails, and diffraction spikes. As an
additional quality cut to remove the spurious signal, we discard
targets for which there are no data in one of the three grz
optical bands. We use a fiber-magnitude cut to suppress the
spurious signal that typically arises from imaging artifacts or
fragments of *shredded* galaxies (see middle panel of Figure 4).
Finally, we also remove spurious objects with extreme colors
from the BGS targets and very bright objects (*r* > 12 and
*r*<sub>fiber</sub> < 15), which may pollute neighboring faint fibers during
DESI observations.

Without additional filters, the BGS Faint selection would
include many faint galaxies that would significantly reduce the
redshift success rate of the sample. To preferentially sample
star-forming galaxies with strong emission lines (Kochanek
et al. 2012) and thus maintain a high-redshift efficiency, we
require a selection based on an *r*-band fiber aperture magnitude
and (z − W1) − 1.2(g − r) + 1.2 color as shown in the right
panel of Figure 4.

In addition to the BGS Bright and BGS Faint samples, BGS
includes a supplementary selection to recover active galactic
<figure><img src="image_7.png" /><figcaption>**Figure 4.** Representation of the target selection algorithm for the BGS program. Left panel: star–galaxy separation is performed using a $G_{Gaia} - r > 0.6$ cut (black dashed line) using Gaia and Legacy Survey photometry. Middle panel: The BGS Bright sample (blue) is identified using the boundaries shown by the dashed lines in the *r* and *r*<sub>fiber</sub> magnitudes. No object fainter than *r*<sub>fiber</sub> = 22.9 is included in the BGS Bright sample. Right panel: the BGS Faint sample (orange) includes objects fainter than BGS Bright, $19.5 < r < 20.175$, passing the (*r*<sub>fiber</sub> and (*z* − W1) − 1.2(*g* − *r*) + 1.2) cuts, illustrated by the black dashed lines.</figcaption></figure>

nuclei (AGN) host galaxies that are rejected by the $G_{Gaia} - r > 0.6$ star–galaxy separation cut, but would otherwise pass BGS selection criteria. The presence of AGN is inferred from optical and infrared colors that trace the signatures of hot, AGN-heated dust in the SED (see Hahn et al. 2023 for more details). The target density of this AGN sample is only ~3–4 deg⁻².

The target densities of the BGS Bright and Faint samples are 854 and 526 deg⁻², respectively. In the main survey, BGS Bright targets will be given a higher priority when assigning fibers to ensure high completeness for this primary sample, with a typical fiber assignment efficiency of 80% after failed spectra are taken into account. To facilitate corrections of the BGS Faint targets for fiber assignment incompleteness, the priority of ~20% BGS Faint targets is randomly raised to higher priority. Including these upweighted targets and the effects of failed spectra, the BGS Faint sample is predicted to have ~60% fiber assignment efficiency, providing sufficient completeness for a range of cosmological analyses.

## 4.4. Luminous Red Galaxies (LRG)

The details of the final LRG selection algorithm are provided in Zhou et al. (2023). The LRG selection is tuned independently for DECaLS and MzLS/BASS catalogs to obtain a roughly uniform comoving number density over the interval $0.4 < z < 0.8$. With a total of 624 targets deg⁻², the surface density is approximately double that from the BOSS/eBOSS programs. The selection maintains a high number density to higher redshifts, monotonically decreasing beyond $z = 0.8$ before reaching a comoving density of $6 \times 10^{-5} h^3 \text{Mpc}^{-3}$ at $z = 1.1$.

In the target selection validation program, the WISE photometry was shown to be an effective veto against stars, with stellar contamination of less than 1% with the selection shown in the upper left panel of Figure 5. No filters based on morphology are used to identify LRG targets, although Gaia photometry is used to remove bright stars.

The upper right panel of Figure 5 shows the criteria used to eliminate low-redshift and bluer objects. By requiring $g - W1 > 2.9$ (corresponding to the diagonal boundary line in the $g - r$ versus $r$-W1 plane), we remove galaxies with redshift $z < 0.3$. For higher-$z$ objects, $g$-band photometry becomes less

reliable, so we impose no limits on $g - W1$ for $r - W1 > 1.8$ galaxies.

As shown in the lower left panel of Figure 5, the *r*-W1 color is a good proxy for redshift. By imposing a sliding cut in *r*-W1 as a function of the W1 magnitude, we select the most luminous (in observed W1) galaxies at any redshift. The exact slope is trained to produce a nearly constant comoving number density of targets over $0.4 < z < 0.8$. The highest-redshift LRGs in our sample typically appear with the reddest colors. These are also the faintest LRGs in our sample. Regardless of W1 magnitude, all objects redder than $r - W1 > 3.3$ are included in order to boost the number density of the highest-redshift LRGs.

Finally, the lower right panel of Figure 5 shows the faint limit used to ensure high-quality spectra. The limiting flux is defined using a magnitude measured over an aperture matched to the DESI fiber diameter ($z_{fiber}$). This fiber magnitude is more strongly correlated with the spectroscopic S/N and is thus a better predictor for obtaining a successful redshift classification than the total magnitude.

LRG targets will be given a higher priority in fiber assignment than ELG targets but a lower priority than quasar targets. In total, the LRG sample will achieve a high completeness, with a typical fiber assignment efficiency of 89% after accounting for failed spectra.

## 4.5. Emission Line Galaxies (ELG)

The DESI ELG sample is designed to cover the redshift range $0.6 < z < 1.6$, with one of two samples selected to emphasize redshifts over the interval $1.1 < z < 1.6$. The details of the final ELG selection algorithm are provided in Raichoor et al. (2023). The targets selected by the algorithm optimized for the higher-redshift range are labeled as the ELG_LOP sample, with an average target density of 1941 deg⁻² and assigned fibers at a lower priority than the quasar and LRG targets. The ELGs in this higher-redshift range will provide distinct clustering measurements at earlier epochs than can be explored with the LRG sample. These ELG_LOP targets are assigned fibers at a lower priority than LRG or quasar targets, hence the acronym for the target class. The second sample is defined by the ELG_VLO selection and tends to have lower redshifts but a higher-redshift success rate. The ELG_VLO sample has a density 463 deg⁻² and is given fibers at a very
<figure><img src="image_8.png" /><figcaption>**Figure 4.** Representation of the target selection algorithm for the BGS program. Left panel: star–galaxy separation is performed using a $G_{Gaia} - r > 0.6$ cut (black dashed line) using Gaia and Legacy Survey photometry. Middle panel: The BGS Bright sample (blue) is identified using the boundaries shown by the dashed lines in the *r* and *r*<sub>fiber</sub> magnitudes. No object fainter than *r*<sub>fiber</sub> = 22.9 is included in the BGS Bright sample. Right panel: the BGS Faint sample (orange) includes objects fainter than BGS Bright, $19.5 < r < 20.175$, passing the (*r*<sub>fiber</sub> and (*z* − W1) − 1.2(*g* − *r*) + 1.2) cuts, illustrated by the black dashed lines.</figcaption></figure>

nuclei (AGN) host galaxies that are rejected by the $G_{Gaia} - r > 0.6$ star–galaxy separation cut, but would otherwise pass BGS selection criteria. The presence of AGN is inferred from optical and infrared colors that trace the signatures of hot, AGN-heated dust in the SED (see Hahn et al. 2023 for more details). The target density of this AGN sample is only ~3–4 deg⁻².

The target densities of the BGS Bright and Faint samples are 854 and 526 deg⁻², respectively. In the main survey, BGS Bright targets will be given a higher priority when assigning fibers to ensure high completeness for this primary sample, with a typical fiber assignment efficiency of 80% after failed spectra are taken into account. To facilitate corrections of the BGS Faint targets for fiber assignment incompleteness, the priority of ~20% BGS Faint targets is randomly raised to higher priority. Including these upweighted targets and the effects of failed spectra, the BGS Faint sample is predicted to have ~60% fiber assignment efficiency, providing sufficient completeness for a range of cosmological analyses.

## 4.4. Luminous Red Galaxies (LRG)

The details of the final LRG selection algorithm are provided in Zhou et al. (2023). The LRG selection is tuned independently for DECaLS and MzLS/BASS catalogs to obtain a roughly uniform comoving number density over the interval $0.4 < z < 0.8$. With a total of 624 targets deg⁻², the surface density is approximately double that from the BOSS/eBOSS programs. The selection maintains a high number density to higher redshifts, monotonically decreasing beyond $z=0.8$ before reaching a comoving density of $6 \times 10^{-5} h^3 \text{Mpc}^{-3}$ at $z=1.1$.

In the target selection validation program, the WISE photometry was shown to be an effective veto against stars, with stellar contamination of less than 1% with the selection shown in the upper left panel of Figure 5. No filters based on morphology are used to identify LRG targets, although Gaia photometry is used to remove bright stars.

The upper right panel of Figure 5 shows the criteria used to eliminate low-redshift and bluer objects. By requiring $g - W1 > 2.9$ (corresponding to the diagonal boundary line in the $g - r$ versus $r$-W1 plane), we remove galaxies with redshift $z < 0.3$. For higher-$z$ objects, $g$-band photometry becomes less

reliable, so we impose no limits on $g - W1$ for $r - W1 > 1.8$ galaxies.

As shown in the lower left panel of Figure 5, the *r*-W1 color is a good proxy for redshift. By imposing a sliding cut in *r*-W1 as a function of the W1 magnitude, we select the most luminous (in observed W1) galaxies at any redshift. The exact slope is trained to produce a nearly constant comoving number density of targets over $0.4 < z < 0.8$. The highest-redshift LRGs in our sample typically appear with the reddest colors. These are also the faintest LRGs in our sample. Regardless of W1 magnitude, all objects redder than $r - W1 > 3.3$ are included in order to boost the number density of the highest-redshift LRGs.

Finally, the lower right panel of Figure 5 shows the faint limit used to ensure high-quality spectra. The limiting flux is defined using a magnitude measured over an aperture matched to the DESI fiber diameter ($z_{fiber}$). This fiber magnitude is more strongly correlated with the spectroscopic S/N and is thus a better predictor for obtaining a successful redshift classification than the total magnitude.

LRG targets will be given a higher priority in fiber assignment than ELG targets but a lower priority than quasar targets. In total, the LRG sample will achieve a high completeness, with a typical fiber assignment efficiency of 89% after accounting for failed spectra.

## 4.5. Emission Line Galaxies (ELG)

The DESI ELG sample is designed to cover the redshift range $0.6 < z < 1.6$, with one of two samples selected to emphasize redshifts over the interval $1.1 < z < 1.6$. The details of the final ELG selection algorithm are provided in Raichoor et al. (2023). The targets selected by the algorithm optimized for the higher-redshift range are labeled as the ELG_LOP sample, with an average target density of 1941 deg⁻² and assigned fibers at a lower priority than the quasar and LRG targets. The ELGs in this higher-redshift range will provide distinct clustering measurements at earlier epochs than can be explored with the LRG sample. These ELG_LOP targets are assigned fibers at a lower priority than LRG or quasar targets, hence the acronym for the target class. The second sample is defined by the ELG_VLO selection and tends to have lower redshifts but a higher-redshift success rate. The ELG_VLO sample has a density 463 deg⁻² and is given fibers at a very
<figure><img src="image_8.png" /><figcaption>**Figure 5.** Selection boundaries for the LRG targets in the DECaLS footprint. Redshifts are color-coded using the DESI spectroscopic redshifts. The upper left panel shows the stellar rejection cut, with point sources (almost all of which are stars) in gray. The upper right panel shows the cuts that remove lower-redshift galaxies and bluer galaxies. The lower left panel shows the color–magnitude cut that shapes the redshift distribution. The lower right panel shows the magnitude limit in z-band fiber magnitude that ensures sufficient signal-to-noise for DESI spectroscopic observations. The objects along the diagonal line are classified as point sources in the imaging and have fixed fiber-flux to total flux ratio.</figcaption></figure>

low (VLO) priority, below that of the higher-redshift ELG_LOP sample.

In identifying ELG targets, we first impose filters on the data quality to reduce spurious signal. We require that there is at least one observation in each of the three grz bands and that the measured flux is greater than zero in all three bands. We also reject targets that are in regions around very bright stars, large galaxies, or globular clusters.

The selection algorithm relies on fluxes measured in the g-band filter. To avoid targets that are unlikely to be at a redshift $z > 0.6$, we remove all potential targets with magnitude $g < 20$. To increase the likelihood of obtaining good spectroscopic signal (Comparat et al. 2016), we remove objects with $g_{fiber} > 24.1$, where the flux aperture is matched to the aperture of the DESI fibers.

Finally, the ELG selection algorithm is tuned to identify objects over the favored redshift range with strong [O II] line strength using colors in the $(g-r)$ versus $(r-z)$ plane. The

general motivation for this selection is illustrated by the stellar evolution tracks in Figure 6 that show two evolution models of galaxies with different star formation histories. The star-forming galaxy exhibits bluer colors over $1.1 < z < 1.6$ than the passive galaxy, justifying the selection of objects that are fairly blue in both $(g-r)$ and $(r-z)$.

More specifically, as illustrated in Figure 6, the mean photometric redshift increases with decreasing $(r-z)$. The boundary of the ELG_LOP sample is set at $(r-z) > 0.15$ to exclude $z > 1.6$ objects where the [O II] emission line appears outside the DESI wavelength coverage. The stellar locus appears clearly at colors that become redder in $(g-r)$ with increasing $(r-z)$, easily separable from the higher-redshift galaxies. A color–color cut with increasing $(g-r)$ as a function of increasing $(r-z)$ is common to both the ELG_LOP and ELG_VLO selections to avoid the stellar locus and reject $z < 0.6$ galaxies. Finally, a second sliding cut with a negative slope (($g-r$) < -1.2 × (r-z) + 1.3) is applied to limit the sample
<figure><img src="image_9.png" /><figcaption>**Figure 5.** Selection boundaries for the LRG targets in the DECaLS footprint. Redshifts are color-coded using the DESI spectroscopic redshifts. The upper left panel shows the stellar rejection cut, with point sources (almost all of which are stars) in gray. The upper right panel shows the cuts that remove lower-redshift galaxies and bluer galaxies. The lower left panel shows the color–magnitude cut that shapes the redshift distribution. The lower right panel shows the magnitude limit in z-band fiber magnitude that ensures sufficient signal-to-noise for DESI spectroscopic observations. The objects along the diagonal line are classified as point sources in the imaging and have fixed fiber-flux to total flux ratio.</figcaption></figure>

low (VLO) priority, below that of the higher-redshift ELG_LOP sample.

In identifying ELG targets, we first impose filters on the data quality to reduce spurious signal. We require that there is at least one observation in each of the three grz bands and that the measured flux is greater than zero in all three bands. We also reject targets that are in regions around very bright stars, large galaxies, or globular clusters.

The selection algorithm relies on fluxes measured in the g-band filter. To avoid targets that are unlikely to be at a redshift $z > 0.6$, we remove all potential targets with magnitude $g < 20$. To increase the likelihood of obtaining good spectroscopic signal (Comparat et al. 2016), we remove objects with $g_{fiber} > 24.1$, where the flux aperture is matched to the aperture of the DESI fibers.

Finally, the ELG selection algorithm is tuned to identify objects over the favored redshift range with strong [O II] line strength using colors in the $(g-r)$ versus $(r-z)$ plane. The

general motivation for this selection is illustrated by the stellar evolution tracks in Figure 6 that show two evolution models of galaxies with different star formation histories. The star-forming galaxy exhibits bluer colors over $1.1 < z < 1.6$ than the passive galaxy, justifying the selection of objects that are fairly blue in both $(g-r)$ and $(r-z)$.

More specifically, as illustrated in Figure 6, the mean photometric redshift increases with decreasing $(r-z)$. The boundary of the ELG_LOP sample is set at $(r-z) > 0.15$ to exclude $z > 1.6$ objects where the [O II] emission line appears outside the DESI wavelength coverage. The stellar locus appears clearly at colors that become redder in $(g-r)$ with increasing $(r-z)$, easily separable from the higher-redshift galaxies. A color–color cut with increasing $(g-r)$ as a function of increasing $(r-z)$ is common to both the ELG_LOP and ELG_VLO selections to avoid the stellar locus and reject $z < 0.6$ galaxies. Finally, a second sliding cut with a negative slope (($g-r$) < −1.2 × (r−z) + 1.3) is applied to limit the sample
<figure><img src="image_9.png" /><figcaption>**Figure 6.** The density (illustrated by varying transparency) of $g_{\text{fiber}} < 24.1$ objects using Legacy imaging photometry. Each point is color-coded by the mean photometric redshift ($z_{\text{phot}}$) from the Hyper Suprime-Cam (HSC)/Gaia Data Data Release 2 (DR2; Aihara et al. 2019). The selection boundaries of the ELG\_LOP sample are illustrated by the black lines while the extended selection associated with the ELG\_VLO sample is represented by the red dashed lines. The green and red tracks demonstrate the redshift evolution (Bruzual & Charlot 2003) of a star-forming and passive galaxy, respectively. The symbols represent different epochs in that evolution: $z = 2$ (square), $z = 1.6$ (downward facing triangle), $z = 1.1$ (circle), $z = 0.6$ (upward facing triangle), and $z = 0.1$ (star).</figcaption></figure>

size and to limit the number of low-redshift galaxies. The boundary for the ELG\_LOP is set where the median redshift is approximately equal to $z = 1.1$ so that higher-redshift galaxies are favored. This boundary is shifted to redder colors for the ELG\_VLO sample to pick up objects around $z = 1$ that were shown in target selection validation to be spectroscopically classified at high efficiency.

A large part of the ELG sample overlaps in redshift with the LRG sample. This overlap will facilitate cross-correlation studies between the two tracers. However, this overlap also means that ELGs clustered with LRGs will compete for the same fibers. In the main program, we assign a higher priority to LRG targets than either the ELG\_LOP or ELG\_VLO samples, leading to a fairly high completeness in LRG targets at the cost of lower completeness in ELG targets. In order to obtain statistics on the ELG targets that typically get bumped by higher priority LRG targets, we increase the priority of 10% of all ELG targets to be identical to the priority of LRG. Fiber assignment between two objects of equal priority gets resolved by a random number generator, thus allowing a full characterization of completeness using mock catalogs as in Mohammad et al. (2020).

In the main survey, the ELG\_LOP sample will achieve a fiber assignment efficiency of 69% while the ELG\_VLO sample will achieve a fiber assignment efficiency of 42% after taking failed spectra into account.

## 4.6. Quasars (QSO)

The selection of quasar samples has historically relied on identification through excess UV emission (Richards et al. 2002; Ross et al. 2012). In DESI, we use an alternative approach that relies on flux excess in NIR bandpasses instead, as demonstrated in eBOSS (Myers et al. 2015). We use three optical bands ($g$, $r$, $z$) combined with W1 and W2 photometry to select our primary sample of quasars. The separation between stars and quasars allowed by optical and infrared

<figure><img src="image_10.png" /><figcaption>**Figure 7.** Colors in the optical and near-infrared for objects photometrically classified as stars (red) and spectroscopically classified as quasars. Quasar redshifts are color-coded as described in the sidebar. Here, $grz$ is the magnitude corresponding to the weighted flux defined as $\text{flux}(grz) = [\text{flux}(g) + 0.8 \times \text{flux}(r) + 0.5 \times \text{flux}(z)] / 2.3$. W is the magnitude corresponding to a weighted flux average defined as $\text{flux}(W) = 0.75 \times \text{flux}(W1) + 0.25 \times \text{flux}(W2)$.</figcaption></figure>

colors is illustrated in Figure 7. The relatively blue color of stars is due to the rapidly declining tail of the blackbody spectrum at infrared wavelengths. The relatively red color of quasars is due to the onset of infrared emission from the dusty torus (Hickox & Alexander 2018), leading to a flatter SED.

For BOSS observations, an algorithm based on a neural network for selecting quasars was found to increase the selection efficiency by approximately 20% compared to a selection based on strict boundaries in color and magnitude (Yèche et al. 2010). To further improve the efficiency of target selection for DESI, we use a new machine-learning algorithm based on RF. At a fixed density, the observations from the target selection validation program demonstrated that the RF produced 15% more quasars than the alternate color selection, including 21% more quasars at $z > 2.1$ for Ly-$\alpha$ forest measurements. The RF selection was therefore chosen for the main selection of quasars.

Before utilizing the RF, we only include objects that have stellar morphology (PSF) as identified in the legacy imaging. This rejection reduces contamination from extended source galaxies by an order of magnitude. We also require targets to have $16.5 < r_{AB} < 23.0$, with the bright limit set to remove residual stars and the faint limit set to ensure high-quality spectral data. Finally, to further reject stars with low infrared flux, we require $W1 < 22.3$ and $W2 < 22.3$. To obtain training samples not biased by anterior color selections, we initially used quasars selected by their intrinsic time-variability (Palanque-Delabrouille et al. 2011) in SDSS and “stars” that exhibited no significant variation in their SDSS light curves. This selection was used for the target selection validation target sample. We then retrained the RF selection on DESI spectra using 11 input parameters: the 10 possible colors using the five optical and NIR bands $grzW1W2$, and the $r$-band magnitude. By using correlated parameters in the 10 color measurements, the RF is trained to assign more or less importance to each input parameter.

Finally, the RF probabilities were tuned to obtain quasar targets, leading to a sample of targets at $\sim 310 \text{ deg}^{-2}$. To ensure a uniform target density over the full DESI footprint, we apply slightly different $r$-dependent thresholds on the RF probability in the three regions (north; south, DES; and south, non-DES; for more details, see Chaussidon et al. 2023). Because quasar targets will be given the highest priority during the 5 yr