# COSMOS Spectroscopic Redshift Compilation (First Data Release): 488,000 Redshifts Encompassing Two Decades of Spectroscopy

Ali Ahmad Khostovan, Jeyhan S Kartaltepe, Mara Salvato, Olivier Ilbert, Caitlin M Casey, Hiddo Algera, Jacqueline Antwi-Danso, Andrew Battisti, Malte Brinch, Marcella Brusa, et al.

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## ► To cite this version:

Ali Ahmad Khostovan, Jeyhan S Kartaltepe, Mara Salvato, Olivier Ilbert, Caitlin M Casey, et al. "COSMOS Spectroscopic Redshift Compilation (First Data Release): 488,000 Redshifts Encompassing Two Decades of Spectroscopy." The Astrophysical Journal Supplement Series, 2025, 282 (1), pp.6. 10.3847/1538-4365/ae1cb9. hal-05470183

HAL Id: hal-05470183

https://hal.science/hal-05470183v1

Submitted on 21 Jan 2026

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# COSMOS Spectroscopic Redshift Compilation (First Data Release): 488,000 Redshifts Encompassing Two Decades of Spectroscopy

Ali Ahmad Khostovan¹,² ID, Jeyhan S. Kartaltepe² ID, Mara Salvato³ ID, Olivier Ilbert⁴ ID, Caitlin M. Casey⁵,⁶,⁷ ID,
Hiddo Algera⁸ ID, Jacqueline Antwi-Danso⁹ ID, Andrew Battisti¹⁰,¹¹,¹² ID, Malte Brinch¹³ ID, Marcella Brusa¹⁴,¹⁵ ID,
Antonello Calabrò¹⁶ ID, Peter L. Capak⁷,¹⁷ ID, Nima Chartab¹⁸ ID, Olivia R. Cooper⁶,⁵⁶ ID, Isa G. Cox² ID, Behnam Darvish¹⁹ ID,
Nicole E. Drakos²⁰ ID, Andreas L. Faisst¹⁸ ID, Matthew R. George²¹,²² ID, Ghassem Gozialis²³,²⁴ ID, Santosh Harish² ID,
Günther Hasinger²⁵,²⁶,²⁷ ID, Hossein Hatamnia¹⁹ ID, Angela Iovino²⁸ ID, Shuowen Jin⁷,²⁹ ID, Daichi Kashino³⁰ ID,
Anton M. Koekemoer³¹ ID, Ronaldo Laishram³⁰ ID, Khee-Gan Lee³²,³³ ID, Jitrapon Lertprasertpong² ID, Simon J. Lilly³⁴ ID,
Daizhong Liu³⁵ ID, Daniel C. Masters¹⁸ ID, Bahram Mobasher¹⁹ ID, Tohru Nagao³⁶ ID, Masato Onodera³⁷,³⁸ ID, Yingjie Peng³⁹,⁴⁰ ID,
David B. Sanders⁴¹ ID, Ryan L. Sanders¹ ID, Zahra Sattari¹⁸ ID, Nick Scoville⁴² ID, Ekta A. Shah²,⁴³ ID,
John D. Silverman³²,³³,⁴⁴,⁴⁵ ID, Nao Suzuki²²,⁴⁶,⁴⁷ ID, Sina Taamoli¹⁹ ID, Masayuki Tanaka³⁰,³⁷ ID, Lidia A. M. Tasca⁴,
Sune Toft⁷,¹⁷ ID, Greta Toni¹⁴,¹⁵,⁴⁸ ID, Benny Trakhtenbrot⁴⁹ ID, Jonathan R. Trump⁵⁰ ID, Mattia Vaccari⁵¹,⁵²,⁵³ ID,
Francesco Valentino⁷,⁵⁴ ID, Brittany N. Vanderhoof²,³¹ ID, John R. Weaver⁵⁵ ID, Min S. Yun⁵⁵ ID, and Jorge A. Zavala⁵⁵ ID

¹Department of Physics and Astronomy, University of Kentucky, 505 Rose Street, Lexington, KY 40506, USA; akhostov@gmail.com

²Laboratory for Multiwavelength Astrophysics, School of Physics and Astronomy, Rochester Institute of Technology, 84 Lomb Memorial Drive, Rochester, NY 14623, USA

³Max-Planck-Institut für extraterrestrische Physik, Giessenbachstrasse 1, 85748 Garching, Germany

⁴Aix Marseille Univ, CNRS, CNES, LAM, Marseille, France

⁵Department of Physics, University of California, Santa Barbara, Santa Barbara, CA 93109, USA

⁶Department of Astronomy, The University of Texas at Austin, 2515 Speedway Boulevard, Stop C1400, Austin, TX 78712, USA

⁷Cosmic Dawn Center (DAWN), Denmark

⁸Institute of Astronomy and Astrophysics, Academia Sinica, 11F of Astronomy-Mathematics Building, No. 1, Sec. 4, Roosevelt Road, Taipei 106216, Taiwan, ROC

⁹David A. Dunlap Department of Astronomy and Astrophysics, University of Toronto, 50 St George Street, Toronto, ON M5S, Canada

¹⁰International Centre for Radio Astronomy Research, University of Western Australia, 35 Stirling Highway, Crawley 26WA 6009, Australia

¹¹ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), Australia

¹²Research School of Astronomy and Astrophysics, Australian National University, Cotter Road, Weston Creek, ACT 2611, Australia

¹³Instituto de Física y Astronomía, Universidad de Valparaíso, Avda. Gran Bretana 1111, Valparaíso, Chile

¹⁴Dipartimento di Fisica e Astronomia “Augusto Righi,” Alma Mater Studiorum Università di Bologna, via Gobetti 93/2, 40129 Bologna, Italy

¹⁵INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, via Gobetti 93/3, 40129 Bologna, Italy

¹⁶INAF-Osservatorio Astronomico di Roma, via di Frascati 33, 00078 Monte Porzio Catone, Italy

¹⁷Niels Bohr Institute, University of Copenhagen, Jagtvej 128, DK-2200, Copenhagen, Denmark

¹⁸Caltech/IPAC, MS 314-6, 1200 E. California Boulevard, Pasadena, CA 91125, USA

¹⁹Department of Physics and Astronomy, University of California, Riverside, 900 University Avenue, Riverside, CA 92521, USA

²⁰Department of Physics and Astronomy, University of Hawaii, Hilo, 200 W. Kawili Street, Hilo, HI 96720, USA

²¹Department of Astronomy, University of California, Berkeley, CA 94720, USA

²²Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA

²³Department of Computer Science, Aalto University, P.O. Box 15400, FI-00076 Espoo, Finland

²⁴Department of Physics, University of Helsinki, P.O. Box 64, FI-00014 Helsinki, Finland

²⁵TU Dresden, Institute of Nuclear and Particle Physics, 01062 Dresden, Germany

²⁶DESY, Notkestraße 85, 22607 Hamburg, Germany

²⁷Deutsches Zentrum für Astrophysik, Postplatz 1, 02826 Görlitz, Germany

²⁸INAF-Osservatorio Astronomico di Brera, via Brera 28, I-20121 Milano, Italy

²⁹DTU Space, Technical University of Denmark, Elektrovej 327, DK2800 Kgs. Lyngby, Denmark

³⁰National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo, 181-8588, Japan

³¹Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA

³²Kavli IPMU (WPI), UTIAS, The University of Tokyo, Kashiwa, Chiba 277-8583, Japan

³³Center for Data-Driven Discovery, Kavli IPMU (WPI), UTIAS, The University of Tokyo, Kashiwa, Chiba 277-8583, Japan

³⁴Department of Physics, ETH Zürich, Wolfgang-Pauli-Strasse 27, Zürich, 8093, Switzerland

³⁵Purple Mountain Observatory, Chinese Academy of Sciences, 10 Yuanhua Road, Nanjing 210023, People’s Republic of China

³⁶Research Center for Space and Cosmic Evolution, Ehime University, Bunkyo-cho 2-5, Matsuyama, Ehime 790-8577, Japan

³⁷Department of Astronomical Science, The Graduate University for Advanced Studies, SOKENDAI, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan

³⁸Subaru Telescope, National Astronomical Observatory of Japan, 650 North Aohoku Place, Hilo, HI 96720, USA

³⁹Department of Astronomy, School of Physics, Peking University, 5 Yiheyuan Road, Beijing 100871, People’s Republic of China

⁴⁰Kavli Institute for Astronomy and Astrophysics, Peking University, Beijing 100871, People’s Republic of China

⁴¹Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USA

⁴²Astronomy Department, California Institute of Technology, 1200 E. California Boulevard, Pasadena, CA 91125, USA

⁴³Department of Physics and Astronomy, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA

⁴⁴Department of Astronomy, School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-0033, Japan

⁴⁵Center for Astrophysical Sciences, Department of Physics & Astronomy, Johns Hopkins University, Baltimore, MD 21218, USA

⁴⁶Department of Physics, Florida State University, 77 Chieftan Way, Tallahassee, FL 32306, USA

⁴⁷Laboratoire de Physique Nucleaire et de Hautes-Energies, 4 Place Jussieu, 75005 Paris, France

⁴⁸Zentrum für Astronomie, Universität Heidelberg, Philosophenweg 12, D-69120, Heidelberg, Germany

⁴⁹School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel

⁵⁰Department of Physics, 196 Auditorium Road, Unit 3046, University of Connecticut, Storrs, CT 06269, USA

⁵¹Inter-University Institute for Data Intensive Astronomy, Department of Astronomy, University of Cape Town, 7701 Rondebosch, Cape Town, South Africa

⁵²Inter-University Institute for Data Intensive Astronomy, Department of Physics and Astronomy, University of the Western Cape, Robert Sobukwe Road, 7535 Bellville, Cape Town, South Africa

⁵³INAF-Istituto di Radioastronomia, via Gobetti 101, 40129 Bologna, Italy
⁵⁴ DTU Space, Technical University of Denmark, Elektrovej 327, DK-2800 Kgs. Lyngby, Denmark

⁵⁵ Department of Astronomy, University of Massachusetts, 710 North Pleasant Street, Amherst, MA 01003-9305, USA
*Received 2025 February 28; revised 2025 October 16; accepted 2025 October 21; published 2025 December 18*

**Abstract**

We present the COSMOS Spectroscopic Redshift Compilation encompassing ~20 yr of spectroscopic redshifts within a 10 deg² area centered on the 2 deg² COSMOS legacy field. This compilation contains 487,666 redshifts of 266,284 unique objects from 138 individual programs up to z ~ 8 with median stellar mass ~10⁸.⁴⁻¹⁰¹⁰ M☉ (redshift dependent). Rest-frame NUVrJ colors and star formation rate-stellar mass correlations show that the compilation primarily contains low-to-intermediate-mass star-forming and massive, quiescent galaxies at z < 1.25 and mostly low-mass bursty star-forming galaxies at z > 2. Sources in the compilation cover a diverse range of environments, including protoclusters such as “Hyperion.” The full compilation is 50% spectroscopically complete by i ~ 23.4 mag and Ks ~ 21.6 mag; however, this is redshift dependent. Spatially, the compilation is >50% (>30%) complete within the central (outer) region limited to i < 24 mag and Ks < 22.5 mag, separately. We demonstrate how the compilation can be used to validate photometric redshifts and investigate calibration metrics. By training self-organizing maps on COSMOS2020/Classic and projecting the compilation onto it, we find key subpopulations currently lacking spectroscopic coverage, including z < 1 intermediate-mass quiescent and low-/intermediate-mass bursty star-forming galaxies, z ~ 2 massive quiescent galaxies, and z > 3 massive star-forming galaxies. This highlights how combining self-organizing maps with our compilation can provide guidance for future spectroscopic observations to get a complete spectroscopic view of galaxy populations. Lastly, the compilation will undergo periodic data releases incorporating new spectroscopic redshifts and providing a lasting legacy resource for the community.

*Unified Astronomy Thesaurus concepts: Galaxy evolution (594); Galaxy formation (595); Redshift surveys (1378); Catalogs (205); Galaxy spectroscopy (2171)*

*Materials only available in the online version of record: animation*

**1. Introduction**

In the next several years, astronomy will see a large influx of both photometric and spectroscopic datasets. Planned surveys with space-based missions (e.g., Euclid, Roman) and ground-based facilities (e.g., DESI, Rubin, Subaru/Prime Focus Spectrograph (PFS), Very Large Telescope (VLT)/ MOONS, Sloan Digital Sky Survey (SDSS-V), 4MOST) will observe hundreds of millions of galaxies and active galactic nuclei (AGN) within the optical to near-infrared (NIR) and push the limits toward fainter and rarer galaxy populations. However, at the same time there are a plethora of past spectroscopic datasets and measurements that have been collected over the past 20 yr but are scattered within many archival databases (e.g., IRSA IPAC, ESO Science Portal), catalog services (e.g., VizieR), survey-specific websites, tables in peer-reviewed publications, and simply on the hard drives of the PIs (e.g., programs that have redshift measurements but were never published). Gathering, organizing, and compiling the wealth of spectroscopic redshift measurements into one uniform data product is crucial not only with regard to data management but also in supporting current and future science and calibration initiatives and programs.

The Cosmic Evolution Survey (COSMOS; α = 10⁷°00′m²8′6, δ = +2°12′m21′′0; J2000; N. Scoville et al. 2007) began as the largest contiguous area Hubble Space Telescope (HST) extragalactic imaging survey using Advanced Camera for Surveys (ACS)/F814W to observe ~2 deg² (A. M. Koekemoer et al. 2007), with the main focus on observing galaxy

populations up to z ~ 6 to study galaxy formation and evolution, mapping dark matter distributions, measuring key statistical distributions relating to stellar mass and luminosity of galaxy populations, and investigating the role of AGN, black hole growth, and large-scale structure on galaxy evolution.

Over the past 20 yr, COSMOS has been established as the premier legacy field for extragalactic astrophysics, with over 40 bands of photometry covering a wide multiwavelength space from X-ray to radio. The first published COSMOS catalog (COSMOS2009; P. Capak et al. 2007; O. Ilbert et al. 2009; M. Salvato et al. 2009) presented the photometry and derived measurements for ~2 million galaxies supported with multiwavelength imaging from programs such as Subaru/ Suprime-Cam COSMOS-20 (Y. Taniguchi et al. 2007, 2015) and Spitzer S-COSMOS (D. B. Sanders et al. 2007). Several key imaging surveys came later, including but not limited to deep CFHT *u* imaging (CLAUDS; M. Sawicki et al. 2019), Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP; H. Aihara et al. 2018), *YJHKs* and NB118 narrowband coverage from UltraVISTA (H. J. McCracken et al. 2012), Spitzer/IRAC and MIPS imaging (E. Le Floc'h et al. 2009; M. L. N. Ashby et al. 2013, 2015, 2018; C. L. Steinhardt et al. 2014), deep Galaxy Evolution Explorer (GALEX) imaging (M. A. Zamojski et al. 2007), X-ray imaging from XMM-Newton (N. Cappelluti et al. 2007; G. Hasinger et al. 2007; M. Brusa et al. 2010) and Chandra (M. Elvis et al. 2009; F. Civano et al. 2012, 2016; S. Marchesi et al. 2016), further optical and NIR HST imaging (H. Nayyeri et al. 2017), far-IR Herschel coverage (D. Lutz et al. 2011; S. J. Oliver et al. 2012), and submillimeter and radio coverage (F. Bertoldi et al. 2007; E. Schinnerer et al. 2007; K. S. Scott et al. 2008; O. Miettinen et al. 2017; V. Smolčić et al. 2017; D. Liu et al. 2019). The combination of all these imaging datasets led to updated catalogs: COSMOS2015 (C. Laigle et al. 2016) and

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<footnote n="56">⁵⁶ NSF Graduate Research Fellow.</footnote>

<footnote>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.</footnote>
COSMOS2020 (J. R. Weaver et al. 2022), resulting in ~1.2 and 1.7 million galaxies up to z ~ 6 and 10, respectively. Most recently, COSMOS now includes JWST/NIRCam and MIRI imaging from COSMOS-Web (PIs: J. S. Kartaltepe & C. Casey, C. M. Casey et al. 2023) and PRIMER (PI: J. Dunlop), providing the needed deep near/mid-IR coverage to detect and constrain the properties of high-z galaxy populations.

The wealth of multiwavelength imaging datasets available in COSMOS has also enabled a multitude of spectroscopic follow-up programs. These programs utilize a variety of both ground-based (e.g., Keck, Gemini, VLT, Subaru) and space-based (e.g., HST) facilities. Some of the larger surveys among these have published catalogs and are also included in our compilation, including zCOSMOS (S. J. Lilly et al. 2007; PI: S. Lilly), MOSDEF (M. Kriek et al. 2015), FMOS-COSMOS (D. Kashino et al. 2019), C3R2 (D. C. Masters et al. 2017, 2019), 3D-HST (G. B. Brammer et al. 2012; I. G. Momcheva et al. 2016), VUDS (L. A. M. Tasca et al. 2017), and LEGA-C (A. van der Wel et al. 2021). COSMOS is also the focus of several JWST spectroscopic programs, such as AURORA (A. E. Shapley et al. 2025), PASSAGE (PI: Matthew Malkan), and COSMOS-3D (PI: Koki Kakiichi).

Given the multitude and depth of both photometric and spectroscopic data available, COSMOS is a key focus of Euclid as an auxiliary field (Euclid Collaboration et al. 2022b) and the Vera Rubin Observatory as a deep drilling field (W. N. Brandt et al. 2018) for calibration purposes. Therefore, having a spectroscopic compilation that organizes the above-mentioned spectroscopic programs, as well as the many programs scattered within publications, catalog services, survey websites, and other sources, is critical for supporting these facilities. Furthermore, such a large compilation is also necessary to support current science initiatives, as well as help guide/design future science goals and objectives as new facilities start to achieve first light (e.g., Subaru/PFS, Roman, VLT/MOONS).

In this paper, we present the first data release of the COSMOS Spectroscopic Redshift Compilation. We describe how the data were collected in Section 2 and summarize each program included in the compilation. We describe how the data are compiled into a uniform and concise compilation in Section 3, which includes details on quality flag conversions, astrometric corrections, and the process for flagging sources that have multiple redshift measurements. The compilation also includes physical property measurements described in Section 4 for a subset of galaxies with high-quality redshifts. In Section 5, we describe the contents of the compilation, the spectroscopic completeness, stellar-mass properties, types of star-forming and quiescent galaxies in the compilation, and the diverse range of environments included in the compilation. We showcase two application examples in Section 6: validating photometric redshifts with a detailed investigation of calibration metrics (Section 6.1), and using self-organizing maps (SOMs; unsupervised machine learning) to gain insight into the galaxy subpopulations that do not currently have spectroscopic coverage (Section 6.2). In Section 7, we discuss the future of the compilation, describing how the compilation is expected to grow as more datasets become publicly available, and we also provide details on how subsequent data releases will be carried out and made available for the community.

Overall, the COSMOS Spectroscopic Redshift Compilation is intended to be a continuously evolving legacy resource for the community to be used for both calibration and scientific purposes. It can also be used to identify sources of interest that have multiple and sometimes contradicting redshifts from low signal-to-noise ratio (S/N) spectra (flagged within our compilation), thereby motivating deeper spectroscopic follow-up observations. We highly encourage users of the compilation not only to cite this work but also, if possible, to cite the relevant references (provided in the compilation) associated with the original publication, particularly for cases where a small subsample of the compilation is being used. The list of references is in the data release and will be updated as new data are included in the compilation. We also encourage the community to contact us<fnref n="57" /> and let us know about new datasets to be included in the compilation.

Throughout this paper, unless otherwise stated, we assume a ΛCDM cosmology ($H_0 = 70 \text{ km s}^{-1} \text{ Mpc}^{-1}$, $\Omega_m = 0.3$, $\Omega_\Lambda = 0.7$), a G. Chabrier (2003) initial mass function (IMF) for all stellar-mass measurements, and the AB magnitude system (J. B. Oke & J. E. Gunn 1983).

## 2. Data

There are a multitude of spectroscopic programs that observed targets within the COSMOS field from observer-frame optical to radio wavelengths. Several programs, such as zCOSMOS (S. J. Lilly et al. 2007), GOGREEN (M. L. Balogh et al. 2021), and LEGA-C (A. van der Wel et al. 2021), are publicly available through a centralized database (e.g., IRSA IPAC services, ESO Science Portal, Survey websites), allowing easy access. In many cases, program PIs opt to publish their spectroscopic redshifts and measurements within the VizieR catalog access tool (F. Ochsenbein et al. 2000), which allows any user direct access to the full dataset and also gives them the ability to select objects of interest. However, the majority of spectroscopic redshifts and associated measurements can only be found within publications (e.g., survey papers, target-specific papers) and via private communication with program PIs.

Prior to 2018, the COSMOS Collaboration had a preliminary spectroscopic redshift compilation organized and maintained by coauthor M.S. where redshifts were collected via direct contact with PIs and via a systematic search of the NASA Astrophysical Database System for papers with keywords such as “COSMOS,” “spectroscopic,” and also names of facilities (e.g., Keck, Gemini) and instruments (e.g., MOSFIRE, GMOS). We then updated this list for papers published after 2017. The search results were individually inspected for any reports of spectroscopic redshifts. In the majority of cases, redshifts are listed within a table(s) in the paper, which we then manually gathered and organized into individual files. PI(s) and/or first authors were contacted in cases in which coordinates were not made public or clarification on the datasets was needed.

All datasets included in this first release of the compilation are described in Table 1.

<footnote n="57">⁵⁷ The community can contact cosmosleadership@gmail.com for all queries and inclusion of new datasets in the compilation. Additionally, we highly encourage users to post in the “issues” and “discussion” sections of the GitHub repository at https://github.com/cosmosastro/speczcompilation with regard to queries and issues about the compilation and its usage.</footnote>
<table><caption>**Table 1**

Description of All Datasets Included in the Spectroscopic Redshift Compilation</caption><thead><tr><th>ID</th><th>Survey</th><th>Telescope/Instrument</th><th>Type</th><th>z<sub>spec</sub></th><th>N<sub>obj</sub></th><th>Reference(s) or PI</th></tr></thead><tbody><tr><td>1</td><td>2dFGRS</td><td>AAT/2dF</td><td>(G) Opt</td><td>0.00–0.33</td><td>222</td><td>M. Colless et al. (2001)</td></tr><tr><td>2</td><td>3D-HST</td><td>HST/WFC3 and HST/ACS</td><td>(S) Opt+NIR</td><td>0.00–1.34</td><td>455</td><td>G. B. Brammer et al. (2012);<br>I. G. Momcheva et al. (2016)</td></tr><tr><td>3</td><td>...</td><td>AAT</td><td>(G) Opt</td><td>0.00–3.29</td><td>636</td><td>N. Suzuki et al. (in preparation)</td></tr><tr><td>4</td><td>...</td><td>ALMA</td><td>(G) submillimeter/millimeter</td><td>4.32–4.33</td><td>2</td><td>M. Brinch et al. (2025)</td></tr><tr><td>5</td><td>...</td><td>ALMA</td><td>(G) submillimeter/millimeter</td><td>2.49–2.51</td><td>7</td><td>J. B. Champagne et al. (2021)</td></tr><tr><td>6</td><td>...</td><td>ALMA</td><td>(G) submillimeter/millimeter</td><td>2.78–4.75</td><td>9</td><td>F. Gentile et al. (2024)</td></tr><tr><td>7</td><td>...</td><td>ALMA</td><td>(G) submillimeter/millimeter</td><td>3.62–5.85</td><td>4</td><td>S. Jin et al. (2019)</td></tr><tr><td>8</td><td>...</td><td>ALMA</td><td>(G) submillimeter/millimeter</td><td>2.625</td><td>1</td><td>S. Jin et al. (2024)</td></tr><tr><td>9</td><td>...</td><td>ALMA</td><td>(G) submillimeter/millimeter</td><td>6.75–7.06</td><td>3</td><td>S. Schouws et al. (2023)</td></tr><tr><td>10</td><td>...</td><td>ALMA</td><td>(G) submillimeter/millimeter</td><td>3.71–3.72</td><td>2</td><td>C. Schreiber et al. (2018b)</td></tr><tr><td>11</td><td>...</td><td>ALMA</td><td>(G) submillimeter/millimeter</td><td>4.820</td><td>1</td><td>N. B. Sillassen et al. (2025)</td></tr><tr><td>12</td><td>...</td><td>ALMA</td><td>(G) submillimeter/millimeter</td><td>6.81–6.85</td><td>2</td><td>R. Smit et al. (2018)</td></tr><tr><td>13</td><td>...</td><td>ALMA</td><td>(G) submillimeter/millimeter</td><td>1.15–1.63</td><td>55</td><td>F. Valentino et al. (2018, 2020b, 2020a)</td></tr><tr><td>14</td><td>AS2COSPEC</td><td>ALMA</td><td>(G) submillimeter/millimeter</td><td>1.90–4.78</td><td>18</td><td>C.-C. Chen et al. (2022)</td></tr><tr><td>15</td><td>AzTEC COSMOS</td><td>LMT/RSR and SMA/SIS</td><td>(G) submillimeter/millimeter</td><td>4.34</td><td>1</td><td>M. S. Yun et al. (2015)</td></tr><tr><td>16</td><td>AzTEC COSMOS</td><td>ALMA and NOEMA</td><td>(G) submillimeter/millimeter</td><td>4.63</td><td>2</td><td>E. F. Jiménez-Andrade et al. (2020)</td></tr><tr><td>17</td><td>BRiZELS</td><td>WHT/LIRIS and<br>TNG/NICS</td><td>(G) NIR</td><td>0.84–2.28</td><td>17</td><td>D. Sobral et al. (2016)</td></tr><tr><td>18</td><td>C3R2-DR3</td><td>Keck/LRIS, DEIMOS,<br>MOSFIRE</td><td>(G) Opt+NIR</td><td>0.10–4.17</td><td>347</td><td>S. A. Stanford et al. (2021)</td></tr><tr><td>19</td><td>C3R2-LBT</td><td>LBT/LUCI1, LUCI2</td><td>(G) NIR</td><td>1.32–2.56</td><td>163</td><td>Euclid Collaboration et al. (2022a)</td></tr><tr><td>20</td><td>C3R2</td><td>Keck/LRIS, DEIMOS,<br>MOSFIRE</td><td>(G) Opt+NIR</td><td>0.00–4.50</td><td>2869</td><td>D. C. Masters et al. (2017, 2019)</td></tr><tr><td>21</td><td>CANDELSz7</td><td>VLT/FORS2</td><td>(G) Opt</td><td>0.66–7.14</td><td>18</td><td>L. Pentericci et al. (2018)</td></tr><tr><td>22</td><td>CAPERS</td><td>JWST/NIRSpec</td><td>(S) NIR</td><td>9.288</td><td>1</td><td>A. J. Taylor et al. (2025b)</td></tr><tr><td>23</td><td>CHESS</td><td>Keck/DEIMOS</td><td>(G) Opt</td><td>0.08–1.61</td><td>233</td><td>J. Lertprasertpong et al. (in preparation)</td></tr><tr><td>24</td><td>CHILES</td><td>ALMA</td><td>(G) submillimeter/millimeter</td><td>0.01–1.29</td><td>80</td><td>J. Blue Bird et al. (in preparation)</td></tr><tr><td>25</td><td>CLAMATO DR2</td><td>Keck/LRIS</td><td>(G) Opt</td><td>0.05–3.52</td><td>600</td><td>K.-G. Lee et al. (2018); B. Horowitz<br>et al. (2022)</td></tr><tr><td>26</td><td>...</td><td>MMT/Binospec</td><td>(G) Opt</td><td>6.70–6.88</td><td>9</td><td>R. Endsley & D. P. Stark (2022)</td></tr><tr><td>27</td><td>...</td><td>VLT/X-SHOOTER</td><td>(G) Opt</td><td>6.70–6.88</td><td>2</td><td>D. Sobral et al. (2015)</td></tr><tr><td>28</td><td>...</td><td>Keck/DEIMOS</td><td>(G) Opt</td><td>0.37–6.32</td><td>22</td><td>M. Brinch et al. (2024)</td></tr><tr><td>29</td><td>...</td><td>Keck/DEIMOS</td><td>(G) Opt</td><td>0.03–6.47</td><td>3158</td><td>PI: Peter Capak</td></tr><tr><td>30</td><td>...</td><td>Keck/DEIMOS</td><td>(G) Opt</td><td>0.12–1.52</td><td>156</td><td>C. M. Casey et al. (2012)</td></tr><tr><td>31</td><td>10K-DEIMOS</td><td>Keck/DEIMOS</td><td>(G) Opt</td><td>0.00–6.60</td><td>10,718</td><td>G. Hasinger et al. (2018)</td></tr><tr><td>32</td><td>...</td><td>Keck/DEIMOS</td><td>(G) Opt</td><td>5.67–5.75</td><td>6</td><td>A. L. Henry et al. (2012)</td></tr><tr><td>33</td><td>...</td><td>Keck/DEIMOS</td><td>(G) Opt</td><td>0.00–5.04</td><td>1059</td><td>J. S. Kartaltepe et al. (2010)</td></tr><tr><td>34</td><td>...</td><td>Keck/DEIMOS</td><td>(G) Opt</td><td>0.39–5.01</td><td>143</td><td>PI: Mara Salvato</td></tr><tr><td>35</td><td>...</td><td>Keck/DEIMOS</td><td>(G) Opt</td><td>0.08–1.76</td><td>225</td><td>E. A. Shah et al. (2020)</td></tr><tr><td>36</td><td>...</td><td>Keck/DEIMOS</td><td>(G) Opt</td><td>6.54–6.60</td><td>3</td><td>A. J. Taylor et al. (2025a)</td></tr><tr><td>37</td><td>DESI DR1</td><td>Mayall/DESI</td><td>(G) Opt</td><td>0.00–6.81</td><td>96,255</td><td>DESI Collaboration et al. (2025)</td></tr><tr><td>38</td><td>DESI DR2</td><td>Mayall/DESI</td><td>(G) Opt</td><td>0.00–6.65</td><td>201,445</td><td>J. Ratajczak et al. (2025)</td></tr><tr><td>39</td><td>DESI-EDR</td><td>Mayall/DESI</td><td>(G) Opt</td><td>0.00–5.81</td><td>55,327</td><td>DESI Collaboration et al. (2024)</td></tr><tr><td>40</td><td>FENIKS</td><td>Keck/MOSFIRE</td><td>(G) NIR</td><td>3.34–4.67</td><td>4</td><td>J. Antwi-Danso et al. (2025)</td></tr><tr><td>41</td><td>FMOS-COSMOS</td><td>Subaru/FMOS</td><td>(G) NIR</td><td>0.37–4.64</td><td>988</td><td>J. S. Kartaltepe et al. (2015)</td></tr><tr><td>42</td><td>FMOS-COSMOS</td><td>Subaru/FMOS</td><td>(G) NIR</td><td>0.70–2.59</td><td>5484</td><td>D. Kashino et al. (2019)</td></tr><tr><td>43</td><td>...</td><td>Subaru/FMOS</td><td>(G) NIR</td><td>0.60–1.45</td><td>539</td><td>PI: Tohru Nagao</td></tr><tr><td>44</td><td>...</td><td>Subaru/FMOS</td><td>(G) NIR</td><td>0.68–1.57</td><td>85</td><td>I. G. Roseboom et al. (2012)</td></tr><tr><td>45</td><td>...</td><td>Subaru/FOCAS</td><td>(G) Opt</td><td>0.05–3.88</td><td>114</td><td>PI: Tohru Nagao</td></tr><tr><td>46</td><td>...</td><td>Subaru/FOCAS</td><td>(G) Opt</td><td>0.828</td><td>1</td><td>C. Zhu et al. (2025)</td></tr><tr><td>47</td><td>...</td><td>VLT/FORS2</td><td>(G) Opt</td><td>0.08–5.49</td><td>1767</td><td>J. Comparat et al. (2015)</td></tr><tr><td>48</td><td>...</td><td>VLT/FORS2</td><td>(G) Opt</td><td>2.44–2.45</td><td>11</td><td>C. Diener et al. (2015)</td></tr><tr><td>49</td><td>...</td><td>VLT/FORS2</td><td>(G) Opt</td><td>0.04–1.29</td><td>530</td><td>M. R. George et al. (2011)</td></tr><tr><td>50</td><td>...</td><td>VLT/FORS1</td><td>(G) Opt</td><td>0.03–1.15</td><td>54</td><td>C. Faure et al. (2011)</td></tr><tr><td>51</td><td>GEEC2</td><td>Gemini-S/GMOS</td><td>(G) Opt</td><td>0.12–1.51</td><td>807</td><td>M. L. Balogh et al. (2011)</td></tr><tr><td>52</td><td>...</td><td>Gemini-S/GMOS</td><td>(G) Opt</td><td>0.10–1.66</td><td>124</td><td>I. Cox et al. (in preparation)</td></tr><tr><td>53</td><td>...</td><td>Gemini-S/GMOS</td><td>(G) Opt</td><td>0.8275</td><td>1</td><td>A. A. Khostovan et al. (2025)</td></tr><tr><td>54</td><td>GOGREEN-DR1</td><td>Gemini/GMOS</td><td>(G) Opt</td><td>0.50–2.32</td><td>226</td><td>M. L. Balogh et al. (2021)</td></tr><tr><td>55</td><td>HALO7D</td><td>Keck/DEIMOS</td><td>(G) Opt</td><td>0.29–1.52</td><td>533</td><td>J. Pharo et al. (2022)</td></tr><tr><td>56</td><td>...</td><td>MMT/Hectospec</td><td>(G) Opt</td><td>0.00–1.44</td><td>277</td><td>J. Sohn et al. (2019)</td></tr><tr><td>57</td><td>HETDEX-DR2</td><td>HET/VIRUS</td><td>(G) Opt</td><td>0.01–3.51</td><td>2447</td><td>E. Mentuch Cooper et al. (2023)</td></tr></tbody></table>
<table><caption>**Table 1**
(Continued)</caption><thead><tr><th>ID</th><th>Survey</th><th>Telescope/Instrument</th><th>Type</th><th>z<sub>spec</sub></th><th>N<sub>obj</sub></th><th>Reference(s) or PI</th></tr></thead><tbody><tr><td>58</td><td>HETVIPS</td><td>HET/VIRUS</td><td>(G) Opt</td><td>0.00–2.10</td><td>325</td><td>G. R. Zeimann et al. (2024)</td></tr><tr><td>59</td><td>HMS</td><td>Keck/LRIS, MOSFIRE</td><td>(G) Opt+NIR</td><td>1.36–2.24</td><td>21</td><td>M. Kriek et al. (2024)</td></tr><tr><td>60</td><td>HR-COSMOS</td><td>VLT/VIMOS</td><td>(G) Opt+NIR</td><td>0.74–1.18</td><td>82</td><td>D. Pelliccia et al. (2017)</td></tr><tr><td>61</td><td>HST-Hyperion</td><td>HST/WFC3</td><td>(S) NIR</td><td>0.00–5.99</td><td>12,814</td><td>B. Forrest et al. (2025)</td></tr><tr><td>62</td><td>...</td><td>Magellan/IMACS</td><td>(G) Opt</td><td>0.00–4.66</td><td>2858</td><td>J. R. Trump et al. (2007)</td></tr><tr><td>63</td><td>...</td><td>Spitzer/IRS</td><td>(S) MIR</td><td>0.59–0.83</td><td>52</td><td>H. Fu et al. (2010)</td></tr><tr><td>64</td><td>...</td><td>Keck/KCWI</td><td>(G) Opt</td><td>2.91–2.92</td><td>3</td><td>E. Daddi et al. (2021)</td></tr><tr><td>65</td><td>KMOS<sup>3D</sup></td><td>VLT/KMOS</td><td>(G) NIR</td><td>0.38–2.58</td><td>296</td><td>E. Wisnioski et al. (2019)</td></tr><tr><td>66</td><td>...</td><td>VLT/KMOS</td><td>(G) NIR</td><td>0.22–0.90</td><td>10</td><td>PI: Antonello Calabrò</td></tr><tr><td>67</td><td>KASH<sub>z</sub></td><td>VLT/KMOS</td><td>(G) NIR</td><td>1.10–1.63</td><td>15</td><td>C. M. Harrison et al. (2016)</td></tr><tr><td>68</td><td>KROSS</td><td>VLT/KMOS</td><td>(G) NIR</td><td>2.49–2.51</td><td>16</td><td>J. P. Stott et al. (2016)</td></tr><tr><td>69</td><td>LAGER</td><td>Keck/LRIS</td><td>(G) Opt</td><td>6.90–6.94</td><td>11</td><td>S. Harish et al. (2022)</td></tr><tr><td>70</td><td>LAGER</td><td>Magellan/IMACS</td><td>(G) Opt</td><td>6.88–6.94</td><td>6</td><td>W. Hu et al. (2017)</td></tr><tr><td>71</td><td>LAGER</td><td>Magellan/IMACS and LDSS3</td><td>(G) Opt</td><td>6.90–6.97</td><td>16</td><td>W. Hu et al. (2021)</td></tr><tr><td>72</td><td>LEGA-C DR3</td><td>VLT/VIMOS</td><td>(G) Opt</td><td>0.10–4.31</td><td>3928</td><td>A. van der Wel et al. (2021)</td></tr><tr><td>73</td><td>...</td><td>Keck/LRIS</td><td>(G) Opt</td><td>0.00–4.54</td><td>447</td><td>C. M. Casey et al. (2012)</td></tr><tr><td>74</td><td>...</td><td>Keck/LRIS</td><td>(G) Opt</td><td>2.27–3.03</td><td>58</td><td>K.-G. Lee et al. (2016)</td></tr><tr><td>75</td><td>...</td><td>LBT/LUCI1</td><td>(G) NIR</td><td>1.37–2.22</td><td>11</td><td>M. V. Maseda et al. (2014)</td></tr><tr><td>76</td><td>...</td><td>LBT/LUCI</td><td>(G) NIR</td><td>2.15–2.20</td><td>31</td><td>M. Polletta et al. (2021)</td></tr><tr><td>77</td><td>M2FS</td><td>Magellan/M2FS</td><td>(G) Opt</td><td>5.99–6.15</td><td>3</td><td>S. Fu et al. (2024)</td></tr><tr><td>78</td><td>M2FS</td><td>Magellan/M2FS</td><td>(G) Opt</td><td>5.64–5.76</td><td>52</td><td>Y. Ning et al. (2020)</td></tr><tr><td>79</td><td>M2FS</td><td>Magellan/M2FS</td><td>(G) Opt</td><td>6.54–6.63</td><td>7</td><td>Y. Ning et al. (2022)</td></tr><tr><td>80</td><td>MAGAZ3NE</td><td>Keck/MOSFIRE</td><td>(G) NIR</td><td>3.33–3.43</td><td>22</td><td>I. McConachie et al. (2022)</td></tr><tr><td>81</td><td>...</td><td>Magellan/FIRE</td><td>(G) NIR</td><td>0.37–0.95</td><td>25</td><td>A. Calabrò et al. (2018)</td></tr><tr><td>82</td><td>MAGIC</td><td>VLT/MUSE</td><td>(G) Opt</td><td>0.00–6.61</td><td>2471</td><td>B. Epinat et al. (2024)</td></tr><tr><td>83</td><td>...</td><td>ALMA</td><td>(G) submillimeter/millimeter</td><td>5.85</td><td>2</td><td>C. M. Casey et al. (2019)</td></tr><tr><td>84</td><td>...</td><td>MMT/Hectospec</td><td>(G) Opt</td><td>0.01–2.29</td><td>291</td><td>M. K. M. Prescott et al. (2006)</td></tr><tr><td>85</td><td>...</td><td>Magellan/IMACS</td><td>(G) Opt</td><td>0.19–3.26</td><td>38</td><td>J. R. Trump et al. (2009)</td></tr><tr><td>86</td><td>...</td><td>Subaru/MOIRCS</td><td>(G) NIR</td><td>1.43–1.83</td><td>18</td><td>M. Onodera et al. (2012)</td></tr><tr><td>87</td><td>...</td><td>Subaru/MOIRCS</td><td>(G) NIR</td><td>1.25–2.09</td><td>34</td><td>M. Onodera et al. (2015)</td></tr><tr><td>88</td><td>...</td><td>Subaru/MOIRCS</td><td>(G) NIR</td><td>2.19–3.60</td><td>21</td><td>M. Onodera et al. (2020)</td></tr><tr><td>89</td><td>...</td><td>Subaru/MOIRCS</td><td>(G) NIR</td><td>1.46–1.63</td><td>12</td><td>M. Tanaka et al. (2013)</td></tr><tr><td>90</td><td>MOSDEF</td><td>Keck/MOSFIRE</td><td>(G) NIR</td><td>0.80–4.49</td><td>616</td><td>M. Kriek et al. (2015)</td></tr><tr><td>91</td><td>...</td><td>Keck/MOSFIRE</td><td>(G) NIR</td><td>2.46–2.49</td><td>42</td><td>C. M. Casey et al. (2015)</td></tr><tr><td>92</td><td>...</td><td>Keck/MOSFIRE</td><td>(G) NIR</td><td>0.17–3.58</td><td>32</td><td>C. M. Casey et al. (2017)</td></tr><tr><td>93</td><td>...</td><td>Keck/MOSFIRE</td><td>(G) NIR</td><td>2.04–2.99</td><td>52</td><td>B. Darvish et al. (2020)</td></tr><tr><td>94</td><td>MAGAZ3NE</td><td>Keck/MOSFIRE</td><td>(G) NIR</td><td>2.06–3.83</td><td>19</td><td>B. Forrest et al. (2020, 2022, 2024); I. McConachie et al. (in preparation)</td></tr><tr><td>95</td><td>...</td><td>Keck/MOSFIRE</td><td>(G) NIR</td><td>2.76–2.79</td><td>4</td><td>K. Ito et al. (2023)</td></tr><tr><td>96</td><td>...</td><td>Keck/MOSFIRE</td><td>(G) NIR</td><td>4.53</td><td>1</td><td>T. Kakimoto et al. (2024)</td></tr><tr><td>97</td><td>...</td><td>Keck/MOSFIRE</td><td>(G) NIR</td><td>2.97–3.69</td><td>43</td><td>M. Onodera et al. (2016)</td></tr><tr><td>98</td><td>...</td><td>Keck/MOSFIRE</td><td>(G) NIR</td><td>2.47–3.78</td><td>6.</td><td>C. Schreiber et al. (2018a)</td></tr><tr><td>99</td><td>...</td><td>Keck/MOSFIRE</td><td>(G) NIR</td><td>0.76–2.43</td><td>33</td><td>PI: Nick Scoville</td></tr><tr><td>100</td><td>...</td><td>Keck/MOSFIRE</td><td>(G) NIR</td><td>2.14–3.62</td><td>14</td><td>B. Trakhtenbrot et al. (2016)</td></tr><tr><td>101</td><td>ZFIRE</td><td>Keck/MOSFIRE</td><td>(G) NIR</td><td>1.97–2.31</td><td>90</td><td>K.-V. H. Tran et al. (2017)</td></tr><tr><td>102</td><td>...</td><td>Keck/MOSFIRE</td><td>(G) NIR</td><td>1.24–3.42</td><td>151</td><td>B. Vanderhoof et al. (in preparation)</td></tr><tr><td>103</td><td>SMUVS</td><td>VLT/MUSE</td><td>(G) Opt</td><td>0.07–6.30</td><td>792</td><td>G. Rosani et al. (2020)</td></tr><tr><td>104</td><td>MUSE GTO</td><td>VLT/MUSE</td><td>(G) Opt</td><td>1.50–6.53</td><td>263</td><td>K. B. Schmidt et al. (2021)</td></tr><tr><td>105</td><td>...</td><td>VLT/MUSE</td><td>(G) Opt</td><td>0.36–4.61</td><td>27</td><td>E. Ventou et al. (2019)</td></tr><tr><td>106</td><td>...</td><td>JWST/NIRSpec</td><td>(S) NIR</td><td>14.44</td><td>1</td><td>R. P. Naidu et al. (2025)</td></tr><tr><td>107</td><td>NICE</td><td>NOEMA</td><td>(G) submillimeter/millimeter</td><td>1.64–3.61</td><td>21</td><td>N. B. Sillassen et al. (2024)</td></tr><tr><td>108</td><td>...</td><td>Keck/NIRSPEC</td><td>(G) NIR</td><td>2.03–2.43</td><td>6</td><td>PI: Jeyhan Kartaltepe</td></tr><tr><td>109</td><td>...</td><td>Keck/NIRSPEC</td><td>(G) NIR</td><td>3.14–3.37</td><td>2</td><td>Z. C. Marsan et al. (2017)</td></tr><tr><td>110</td><td>...</td><td>JWST/NIRSpec</td><td>(S) NIR</td><td>7.00</td><td>1</td><td>H. B. Akins et al. (2025b)</td></tr><tr><td>111</td><td>...</td><td>JWST/NIRSpec</td><td>(S) NIR</td><td>7.04</td><td>1</td><td>H. B. Akins et al. (2025a)</td></tr><tr><td>112</td><td>...</td><td>NOEMA, ALMA</td><td>(G) submillimeter/millimeter</td><td>3.55–4.10</td><td>5</td><td>S. Jin et al. (2022)</td></tr><tr><td>113</td><td>...</td><td>NOEMA, Keck/MOSFIRE</td><td>(G) NIR+submillimeter/millimeter</td><td>5.10</td><td>1</td><td>M. Shuntov et al. (2025b)</td></tr><tr><td>114</td><td>...</td><td>NOEMA, VLT/KMOS</td><td>(G) NIR+submillimeter/millimeter</td><td>2.49–2.52</td><td>17</td><td>T. Wang et al. (2016)</td></tr></tbody></table>
<table><caption>**Table 1**
(Continued)</caption><thead><tr><th>ID</th><th>Survey</th><th>Telescope/Instrument</th><th>Type</th><th>z<sub>spec</sub></th><th>N<sub>obj</sub></th><th>Reference(s) or PI</th></tr></thead><tbody><tr><td>115</td><td>PRIMUS</td><td>Magellan/IMACS</td><td>(G) Opt</td><td>0.02–4.60</td><td>29,594</td><td>A. L. Coil et al. (2011)</td></tr><tr><td>116</td><td>QUAIA</td><td>Gaia</td><td>(S) Opt</td><td>0.18–4.12</td><td>289</td><td>K. Storey-Fisher et al. (2024)</td></tr><tr><td>117</td><td>REBELS</td><td>ALMA</td><td>(G) submillimeter/millimeter</td><td>6.58–7.68</td><td>11</td><td>R. J. Bouwens et al. (2022)</td></tr><tr><td>118</td><td>SDSS DR16</td><td>SFT and du Pont</td><td>(G) Opt</td><td>0.00–6.58</td><td>1579</td><td>R. Ahumada et al. (2020)</td></tr><tr><td>119</td><td>...</td><td>VLT/SINFONI</td><td>(G) NIR</td><td>0.97–2.45</td><td>5</td><td>M. Perna et al. (2015)</td></tr><tr><td>120</td><td>...</td><td>VLT/VIMOS</td><td>(G) Opt</td><td>0.69–0.79</td><td>619</td><td>A. Iovino et al. (2016)</td></tr><tr><td>121</td><td>...</td><td>VLT/VIMOS</td><td>(G) Opt</td><td>0.89–2.09</td><td>34</td><td>R. Gobat et al. (2017)</td></tr><tr><td>122</td><td>VIS<sup>3</sup>COS</td><td>VLT/VIMOS</td><td>(G) Opt</td><td>0.02–3.59</td><td>696</td><td>A. Paulino-Afonso et al. (2018)</td></tr><tr><td>123</td><td>VUDS DR1</td><td>VLT/VIMOS</td><td>(G) Opt</td><td>0.04–6.44</td><td>384</td><td>O. Le Fèvre et al. (2015);<br>L. A. M. Tasca et al. (2017)</td></tr><tr><td>124</td><td>VUDS DR2<sup>a</sup></td><td>VLT/VIMOS</td><td>(G) Opt</td><td>0.02–6.44</td><td>4703</td><td>L. Tasca et al. (in preparation)</td></tr><tr><td>125</td><td>WERLS</td><td>Keck/MOSFIRE</td><td>(G) NIR</td><td>3.52–6.93</td><td>83</td><td>PIs: Caitlin Casey & Jeyhan Kartaltepe</td></tr><tr><td>126</td><td>WERLS</td><td>Keck/MOSFIRE</td><td>(G) NIR</td><td>0.51–8.23</td><td>192</td><td>O. R. Cooper et al. (2024)</td></tr><tr><td>127</td><td>...</td><td>HST/WFC3</td><td>(S) NIR</td><td>2.39–3.23</td><td>10</td><td>C. D'Eugenio et al. (2021);<br>P. Lustig<br>et al. (2021)</td></tr><tr><td>128</td><td>...</td><td>HST/WFC3</td><td>(S) NIR</td><td>0.68–2.18</td><td>150</td><td>Based on 3D-HST data</td></tr><tr><td>129</td><td>...</td><td>HST/WFC3</td><td>(S) NIR</td><td>1.84–2.20</td><td>14</td><td>J. K. Krogager et al. (2014)</td></tr><tr><td>130</td><td>XLS</td><td>VLT/X-SHOOTER</td><td>(G) Opt+NIR</td><td>2.07–2.47</td><td>18</td><td>J. Matthee et al. (2021)</td></tr><tr><td>131</td><td>XMM-COSMOS</td><td>VLT/X-SHOOTER</td><td>(G) Opt+NIR</td><td>1.00–1.60</td><td>10</td><td>M. Brusa et al. (2015)</td></tr><tr><td>132</td><td>CALYMHA</td><td>VLT/X-SHOOTER</td><td>(G) Opt+NIR</td><td>2.21–2.23</td><td>3</td><td>D. Sobral et al. (2018)</td></tr><tr><td>133</td><td>...</td><td>VLT/X-SHOOTER</td><td>(G) Opt+NIR</td><td>1.96–2.69</td><td>14</td><td>M. Stockmann et al. (2020)</td></tr><tr><td>134</td><td>...</td><td>VLT/X-SHOOTER</td><td>(G) Opt+NIR</td><td>3.78</td><td>1</td><td>F. Valentino et al. (2020c)</td></tr><tr><td>135</td><td>hCOSMOS</td><td>MMT/Hectospec</td><td>(G) Opt</td><td>0.00–1.27</td><td>4399</td><td>I. Damjanov et al. (2018)</td></tr><tr><td>136</td><td>zCOSMOS-Bright</td><td>VLT/VIMOS</td><td>(G) Opt</td><td>0.00–4.45</td><td>20,716</td><td>S. J. Lilly et al. (2007, 2009)</td></tr><tr><td>137</td><td>zCOSMOS-Deep</td><td>VLT/VIMOS</td><td>(G) Opt</td><td>0.00–4.25</td><td>9371</td><td>PI: Simon Lilly</td></tr><tr><td>138</td><td>zFIRE</td><td>Keck/MOSFIRE</td><td>(G) NIR</td><td>1.97–3.53</td><td>216</td><td>T. Nanayakkara et al. (2016)</td></tr></tbody></table>

Note. The numerical ID numbers assigned to each survey and associated with the “survey” column in the compilation fits files are shown. Basic information regarding the telescope and instrument used for each dataset is also shown, along with their respective redshift ranges. The type classifications refer to the general wavelength range and are defined as follows: Opt (optical; 0.35–1 µm), NIR (near-infrared; 1–5 µm), MIR (mid-infrared; 5–40 µm), FIR (far-infrared; 40–700 µm), and submillimeter/millimeter (>0.7 mm). Programs are also classified if the telescope used is space based (S) or ground based (G). The total number of objects, N<sub>obj</sub>, in each dataset is also shown, together with the appropriate reference(s). In the case in which a publication is not available for a given dataset, we provide the name of the PI for reference.

### 3. Creating a Uniform Spectroscopic Compilation

In this section, we describe in detail the various steps that are taken to generate our spectroscopic compilation, including how quality flags and confidence levels (CLs) are converted to our assumed system (Section 3.1), how astrometric corrections are applied to account for systematics in varying astrometry calibrations from program to program (Section 3.2), and how we flag objects that have multiple redshift measurements (Section 3.4). The compilation pipeline is set up so that it can easily incorporate new spectroscopic catalogs as they are made available, allowing for continuous updates.

#### 3.1. Quality Flags and Confidence Levels

Quality flag ($Q_f$) assessment schemes and their corresponding CLs vary from program to program. The most popular $Q_f$ scheme is the 0–4 system in increments of 1, with some also including a $Q_f$ = 9 in the case in which a single line is present. Some programs include an additional $10 + X$ ($X$ = 0–4, 9) for sources that exhibit broad-line features indicative of an AGN. Additional flags beyond the typical $Q_f$ described above are also included in other programs. For example, zCOSMOS (S. J. Lilly et al. 2007) includes a decimal flagging system (X. 1, X. 3, X. 4, X. 5; X being the main flag class) to describe

the spectroscopic-to-COSMOS2009 (O. Ilbert et al. 2009) photometric redshift consistency. VUDS (L. A. M. Tasca et al. 2017) has additional flags $X$ + 20, +30, +40, and −10 to highlight key features such as serendipitous objects, overlapping spectra, pair systems, etc. Other programs, such as DESI-EDR (DESI Collaboration et al. 2024), use a completely different quality assessment flagging system that does not fall within the $Q_f$ = 0–4, 9 framework.

Combining the numerous datasets into a single catalog requires normalizing the $Q_f$ and CL systems for all sources to ensure consistency. We adopt the following quality system as defined in Table 2 and convert each program’s quality flag to match our adopted system. For example, KMOS<sup>3D</sup> (E. Wisnioski et al. 2019) lists $Q_f$ = 1 (redshift/detection is uncertain), 0 (redshift is secure), and −1 (nondetection). We ignore all sources flagged as −1 and make the following conversions to our $Q_f$ system: 0 → 4 and 1 → 1. The MUSE Guaranteed Time Observations program (K. B. Schmidt et al. 2021) uses a system ranging from 0 to 3, where each flag is described as follows: Flag 0—difficult to ascertain whether the emission-line feature is real or not; Flag 1—a single trustworthy line detection of uncertain nature; Flag 2—classification with high confidence (multiple line detections S/N < 5 or unique line profiles; e.g., $\text{Ly}\alpha$); Flag 3—multiple line detections S/N > 5

<footnote><sup>a</sup> Currently proprietary; however, this dataset will be made publicly available upon the VUDS DR2 official release.</footnote>
<table><caption>**Table 2**

Adopted Quality Flags and Confidence Levels and Their Corresponding Definitions</caption><thead><tr><th>Q<sub>f</sub></th><th>CL (%)</th><th>Description</th></tr></thead><tbody><tr><td>4</td><td>97–100</td><td>Very reliable redshift</td></tr><tr><td>3</td><td>95</td><td>Reliable redshift</td></tr><tr><td>2</td><td>80</td><td>Moderate detection of emission lines</td></tr><tr><td>1</td><td>50</td><td>Tentative measurement</td></tr><tr><td>0</td><td>...</td><td>No measurement</td></tr><tr><td>9</td><td>85</td><td>Single line/ambiguous detection with good S/N</td></tr><tr><td>+10</td><td>...</td><td>Indicative of broad-line feature</td></tr></tbody></table>

**Note.** We add an additional factor of 10 to the $Q_f$ in the case of broad-line features where that information is available. For example, a $Q_f = 13$ would be indicative of a reliable redshift with broad emission line features.

and no doubt about line classification. Our conversions are as follows: $3 \to 4$, $2 \to 3$, $1 \to 9$, and $0 \to 1$. We refer to the associated paper, documentation, and/or direct communication with the author(s)/PI(s) to properly convert the original quality flags to our system. In the majority of cases, the conversion is straightforward, as most programs follow a system similar to our $Q_f$ system highlighted in Table 2. However, as seen in the subsection below, there are some cases where the conversion is not as straightforward.

### 3.1.1. DESI Quality Flag Conversion

We use the full DESI-EDR v1.0 catalog<fnref n="58" /> in our compilation and use DESI Collaboration et al. (2024) as the main documentation for applying our $Q_f$ scheme. Spectroscopic redshifts were assigned using an automated approach (Redrock<fnref n="59" />) that generates principal component analysis templates, fits them to every target, and identifies the best redshift and spectral type (galaxy, QSO, or star; labeled in SPECTYPE) based on the lowest $\chi^2$. Redrock also generates a bitmask flag, ZWARN, that identifies any key issues that may have occurred during spectroscopic redshift assignment (e.g., narrow wavelength coverage, $\chi^2$ of best fit is too close to the second best, fiber was unplugged/broken, no data in fiber). Limiting the catalog to ZWARN = 0 removes sources that have known issues in their spectroscopic redshift determination. This also corresponds to DELTACHI2 ($\Delta\chi^2$) > 9, which identifies the separation between the best and second-best models. Larger DELTACHI2 corresponds to greater statistical confidence that the template that best fits the object and the assigned redshift is correct.

DESI Collaboration et al. (2024, see their Section 4.2.2) suggest a scheme to select “good” redshifts with catastrophic failure fractions <0.5%, which we consider as $Q_f = 4$. We refer to this criterion scheme to assign $Q_f$ to the DESI-EDR sources. We first start by limiting the samples to ZWARN = 0 and $z > 0$, where the latter removes objects that are slightly negative in redshift and have SPECTYPE = GALAXY (issue with redshift determination). These sources were initially assigned a $Q_f = 2$ given that there were no known issues in the redshift determination; however, a subset of them do not have reliable $\Delta\chi^2$ to separate between the different spectral types.

<figure><img src="image_5.png" /><figcaption>**Figure 1.** Offsets in the astrometry of all sources in the compilation after astrometric corrections were applied using COSMOS2020 Classic as the reference catalog. The dashed, dashed-dotted, and dotted lines are the $1\sigma$, $2\sigma$, and $3\sigma$ CLs, respectively. The median offset is shown as the blue square and is ~69.6 mas, with a median absolute deviation of ~35 mas and standard deviation of 105 mas.</figcaption></figure>

Sources satisfying the following criteria are then assigned a $Q_f = 4$:

(1) Luminous red galaxy. $z > 0.4$ and $z < 1.1$ and $\Delta\chi^2 > 15$

(2) Bright galaxy. $z > 0.01$ and $z < 0.6$ and $\Delta\chi^2 > 40$

(3) [OII] emitter. $z > 0.6$ and $z < 1.6$ and $\Delta\chi^2 > 8$

(4) Ly$\alpha$ emitter. $z > 2$ and $\Delta\chi^2 > 8$

(5) QSO. $z > 0.6$ and $z < 3.5$ and SPECTYPE = QSO

Note that these selection criteria are taken from the DESI-EDR documentation (DESI Collaboration et al. 2024) with direct consultation with the PI. For all sources, if SPECTYPE = QSO, then an additional factor of +10 is also assigned to the final $Q_f$. The DESI DR1 (DESI Collaboration et al. 2025) and DESI DR2/COSMOS (J. Ratajczak et al. 2025) were also converted by using associated documentation citing conditions, resulting in minimal catastrophic errors, and were also validated with direct communication with the associated PI.

### 3.2. Astrometric Corrections and COSMOS Catalogs

The programs in this compilation use different astrometric calibrations, which can introduce variable systematic astrometric uncertainties when combined into a single catalog. Therefore, we require that the final compilation has astrometric corrections that bring all programs to the same calibration.

We start by first matching each program to the COSMOS2020 Classic catalog (J. R. Weaver et al. 2022) with a $1.5$ search radius that is astrometrically calibrated with Gaia DR1 data. A total of 263,823 of 274,924 sources (~96%) matched and were used to measure the astrometric offset. This was done by calculating the difference in R.A. and decl. for each source with reference to the COSMOS2020 Classic R.A. and decl. The median R.A. and decl. offset per program was then applied to the respective sources. The typical correction applied was ~88 mas, with a median absolute deviation of ~48 mas. The applied astrometric corrections are checked by

<footnote n="58">⁵⁸ https://data.desi.lbl.gov/public/edr/vac/edr/zcat/fuji/v1.0/zall-tilecumulative-edr-vac-fits</footnote>

<footnote n="59">⁵⁹ https://github.com/desihub/redrock/tree/0.15.4</footnote>
measuring the offsets per program between the corrected R.A. and decl. and the coordinates published in the COSMOS2020 Classic catalog, but this time with a 1" search radius. The typical offset per program after correction was ~69.6 mas, with a median absolute deviation of ~35 mas. Figure 1 shows the astrometric offsets for all sources in the compilation with a COSMOS2020 Classic match after the corrections described above were applied.

After applying these corrections, we match the spectroscopic compilation with both versions of the COSMOS2020 catalog (Classic and Farmer; J. R. Weaver et al. 2022) and the COSMOS2009 catalog (O. Ilbert et al. 2009) with a 1" search radius. The R.A. and decl. of each source in the ancillary COSMOS catalogs are included in the compilation (see Table 4). Photo-z information is also included for each source using LePHARE measurements in COSMOS2020 Classic.

### 3.3. Mitigating Pair Galaxy Effects

One issue that was noted in matching the compilation to COSMOS2020 Classic was the situation where line-of-sight (LOS) pairs were matched to a single COSMOS2020 galaxy (e.g., only one source detected). As a result, a single COSMOS2020 ID could potentially have wildly different redshifts. To mitigate this effect, we rely on the COSMOS ACS/F814W catalog (A. Leauthaud et al. 2007), given its higher resolution compared to the ground-based COSMOS2020 catalog, to resolve the pairs. Within the COSMOS2020 catalog are associated IDs (`ID_ACS`) that connect to the ACS catalog. As such, we crossmatch both the ACS and COSMOS2020 catalogs with the compilation. We then check to see whether for a given source in the compilation the COSMOS2020 `ID_ACS` and the ACS catalog ID equate. If not, then that specific match is flagged out by replacing the `ID_COS20_Classic` in the compilation as `−999` (no Classic counterpart). This step is done in both determining (1.5 search radius) and applying (1" search radius) astrometric corrections to mitigate the incorrect matching effects.

We note that this is not a fully guaranteed solution to this problem since some sources, especially at high z, may lack an ACS/F814W detection. As such, we do caution users to double-check for LOS pair galaxies, especially in the case where redshifts differ significantly and/or expected redshifts are not met. Future higher-resolution imaging can help alleviate this issue.

### 3.4. Flagging Duplicate Sources

Duplicate sources exist in the main compilation when two or more programs have observed the same source under different spectroscopic configurations. These sources can be of great interest for several reasons. For example, each independent program may have reported spectroscopic redshifts that are in agreement, giving more confidence that the spectroscopic redshift is accurate. There may also be cases where spectroscopic redshifts are not in agreement, and that tension, especially if $Q_f = 4$ for each reported redshift, could warrant further investigation. Duplicate sources in the compilation could also be useful to gauge what spectroscopic configurations have been used so far to observe an individual source (e.g., wavelength coverage, spectral resolution), for which the user could contact the PIs of the program and request the 1D

and 2D spectra, enabling wider wavelength coverage for spectroscopic analyses.

We identified duplicate sources by using the matched COSMOS2020 Classic and Farmer, COSMOS2015, and COSMOS2009 catalogs to identify duplicate IDs in each respective catalog that ended up in our main spectroscopic compilation. This was done by first separating the compilation to investigate only those galaxies that matched COSMOS2020 Classic. An internal match is done for these sources based on the COSMOS2020 Classic ID, where duplicate sources in our compilation would have the same Classic ID. For each source that has the same ID, we test which one has a better $Q_f$ and assign that specific compilation source a priority flag of 1 (see Table 4). We also assign a priority flag of 1 to the most recent redshift measurement in the case where two or more spectroscopic redshifts per duplicate source have the same $Q_f$.

After inspecting all COSMOS2020 Classic matches, we then repeat the same procedure but for the sources with a Farmer match but no Classic match and repeat the above procedure. This is followed by COSMOS2015 and then COSMOS2009 sources. For the sources that do not match in any of the COSMOS catalogs, we flag duplicate sources by doing an internal coordinate match of these subsets within a 1" search radius and apply the same flagging method as described above. In total, there are 37,510 unique groups corresponding to 104,681 redshifts and a total of 266,284 sources.

## 4. Constraining Physical Properties

Although sources in the published COSMOS catalogs include spectral energy distribution (SED) fits and derived physical properties, we opted to reassess these SED fits by fixing the redshift to the spectroscopic redshift. This provides a more reliable measurement of the physical properties associated with each galaxy, especially for those where the photometric redshifts are significantly different from the spectroscopic redshift. In this section, we describe how we constrain the physical properties of galaxies within the compilation using two widely used SED-fitting codes: CIGALE (M. Boquien et al. 2019; G. Yang et al. 2020) and LePHARE (S. Arnouts et al. 1999; O. Ilbert et al. 2006). Multiwavelength photometry from COSMOS2020 Classic is used when available to constrain the SEDs for each unique source, with the redshift fixed to the measured spectroscopic redshift. This corresponds to 133,916 unique sources (92% of all unique sources within COSMOS2020 coverage) with COSMOS2020 Classic photometry ranging from CFHT/MegaCam *u* to Spitzer/IRAC 4.5 µm that are used for SED fitting and are provided to the community within the data release. However, results shown below are restricted to SED fits for sources that have $Q_f = 3$ and 4, resulting in a total of 82,261 COSMOS2020 Classic–matched $0 < z < 7.7$ unique sources. This ensures that sources identified as broad-line AGN are not included in the results; however, not all programs explicitly labeled broad-line features, and the sample includes X-ray and IR AGN. As such, we caution users when interpreting the SED-derived physical properties for sources that are AGN in the subset and encourage users to apply AGN-specific templates and models in the SED-fitting process.
## 4.1. CIGALE

We use CIGALE (M. Boquien et al. 2019), a grid-based template SED-fitting suite, to derive physical properties and constrain SEDs using the best spec-z for each source. The setup files associated with our CIGALE SED fits are included in the data release, and we briefly describe them here. We assume a G. Chabrier (2003) IMF with the G. Bruzual & S. Charlot (2003) synthetic stellar population model and stellar metallicities in the range of 0.0004–0.05 (0.02–2.5 $Z_\odot$). A delayed-$\tau$ star formation history (SFH) is assumed and contains two components: a main/old stellar population, and a young population formed in a recent burst. We define the age of the main population in the range of 50 Myr–13 Gyr, with an e-folding timescale ($\tau_{\text{main}}$) between 50 Myr and 20 Gyr. The ages of the young stellar population formed in a recent burst are evaluated between 5 and 50 Myr, with $\tau_{\text{burst}}$ between 5 and 100 Myr and a fractional contribution to total stellar mass considered between 0% and 90%, where 0% would imply no recent burst.

Nebular emission-line contributions are also included and are based on the A. K. Inoue (2011) templates generated using Cloudy 13.01 (G. J. Ferland et al. 1998, 2013), with a range of ionization parameters, $\log_{10} U$, between –4 and –1, gas-phase metallicities between 0.0004 and 0.051 (0.02–2.5 $Z_\odot$), fixed electron density of 100 cm$^{-3}$, LyC escape fractions of 0% with no fraction absorbed by dust, and fixed emission line widths of 300 km s$^{-1}$. We assume a D. Calzetti et al. (2000) dust attenuation curve with no 2175 Å bump and $E(B-V) \sim 0-2$ mag and an LMC dust extinction curve (Y. C. Pei 1992) for the emission lines. Lastly, we apply the B. T. Draine et al. (2014) dust emission model assuming a constant polycyclic aromatic hydrocarbon mass fraction of 2.5%, minimum radiation field $U_{\min} = 1$ with $\alpha = 2$ defining the slope of the radiation field ($dU/dM \propto U^\alpha$), and $\gamma = 0.1$ (fraction of starlight illuminated from dust).

## 4.2. LePHARE

Physical properties such as absolute magnitudes, stellar masses, and star formation rate (SFR) are also estimated using the template-fitting code LePHARE (S. Arnouts et al. 1999; O. Ilbert et al. 2006) with the same configuration as used in COSMOS2020 (J. R. Weaver et al. 2022), except that the redshift is now fixed to the spectroscopic redshift reported in our compilation.

The model assumptions are described in O. Ilbert et al. (2015). We built our set of templates using the G. Bruzual & S. Charlot (2003) synthetic stellar population model and assume a G. Chabrier (2003) IMF. We assume six different SFHs: four exponentially declining ($\tau = 1, 3, 5, 30$ Gyr) and two delayed-$\tau$ (with $\tau = 1, 3$ Gyr). We assume two stellar metallicities (0.5 and 1 $Z_\odot$) and 43 ages ranging from 0.05 Gyr to the maximum age of the Universe at the considered redshift. We add emission lines to the G. Bruzual & S. Charlot (2003) templates by adopting the empirical relation from O. Ilbert et al. (2009) that links the UV intrinsic luminosity and the [O II] flux. We assume constant ratios between emission lines prior to applying dust attenuation. The emission-line fluxes are allowed to vary all together by 50% around the fiducial value during the fit. We include two different attenuation curves (D. Calzetti et al. 2000; S. Arnouts et al. 2013) and consider $E(B-V) = 0-0.7$ mag. The intergalactic medium (IGM)

absorption is accounted for by using the analytical correction of P. Madau (1995).

While LePHARE outputs several estimates of the physical parameters, we select the stellar masses and the SFR from the template that minimizes the $\chi^2$.

# 5. The COSMOS Spec-z Compilation

The compilation consists of two key files: _all.fits (all redshift measurements) and unique.fits (only unique sources; see Section 3.4). A total of 266,284 unique sources make up the compilation, with 184,633 and 4338 sources having $Q_f=3-4$ and $Q_f=13-14$, respectively. The full compilation consists of 487,666 individual redshift measurements, where 288,044 and 10,505 redshifts have $Q_f=3-4$ and $Q_f=13-14$, respectively. Sources with multiple measurements (groups) are also identified within the full compilation, where we find 124,211 groups consisting of three members on average, with some groups having up to 25 redshift measurements.

Table 1 highlights all 138 programs that compose the compilation within a 10 deg$^2$ area. The wider area compared to the 2 deg$^2$ coverage of COSMOS is in preparation of next-generation wide-area surveys. In total, 127 and 11 are based on ground- and space-based telescopes/observatories, respectively. The number of space-based redshifts is expected to increase as more data become available via missions such as JWST, Euclid, Roman, and SPHERE. The majority of redshifts are drawn from 71 optical (0.35–1µm) programs and 64 NIR (1–5 µm) programs. There are also 20 millimeter/submillimeter (>0.7 mm) programs primarily drawn from the Atacama Large Millimeter/submillimeter Array (ALMA) and NOEMA. Only one mid-infrared (5–40 µm) program is currently in the compilation; however, that is expected to increase as JWST/MIRI spectroscopic programs publish new redshifts.

The top panel of Figure 2 highlights the redshift distribution of the full compilation and the two high-$Q_f$ subsets. Both the full compilation and the $Q_f=3-4$ subset have a median redshift of $z \sim 0.7$, while the broad-line subset ($Q_f=13-14$) has a median redshift of $z \sim 1.8$, where the latter is most likely due to programs specifically targeting high-z AGN and quasars. Although the compilation contains primarily low-$z$ ($z < 1$) galaxies, we find that $\sim 10\% (7\%)$ of the full ($Q_f=3-4$) compilation contains $z > 2$ galaxies, which is due to the relatively recent increase in availability of NIR spectroscopy (enabling the detection of $H\alpha$, [O III], $H\beta$, and [O II] emission) and Ly$\alpha$ emission follow-up via optical spectroscopy. Given that [O III] emission can be observed from the ground up to $z \sim 3.5$ and the faint nature of [O II] emission with increasing redshift (e.g., A. A. Khostovan et al. 2015, 2016, 2020), measurements in the compilation with $z > 3-4$ are observed primarily based on Ly$\alpha$ emission (up to $z \sim 7$ within optical spectroscopy), as well as other rest-frame UV lines (e.g., C IV).

The main panel of Figure 2 highlights the range of Subaru/HSC *i* total magnitudes based on 2" apertures drawn from COSMOS2020/Classic. Each data point refers to a single survey as listed in Table 1, with error bars showing the redshift range and *i* magnitude in the survey. The majority of datasets forming the compilation are found to have *i* magnitudes of 22–25 mag at $0.5 < z < 2.5$ and *i* > 25 mag at $z > 2.5$, highlighting the observationally faint nature of high-$z$ sources
<figure><img src="image_6.png" /><figcaption>**Figure 2.** The top panel shows the published spectroscopic redshift distribution of all sources within the final compilation (gray) along with those that have high-quality spec-z ($Q_f = 3–4$) and broad-line features ($Q_f = 13–14$). The range of HSC *i* magnitudes and spec-z for each individual program is shown in the bottom panel, with each symbol corresponding to the median *i* magnitude and redshift for each program. Error bars represent the 16th and 84th percentile ranges. Programs with no COSMOS2020 Classic matches are placed at 31 mag, with a slight perturbation for visualization purposes. In total, 266,284 sources compose the total compilation, with 487,666 redshift measurements from 138 programs.</figcaption></figure>

(although intrinsically bright; e.g., Malmquist bias). We note that some of the surveys did not have COSMOS2020/Classic matches and are artificially assigned *i* = 31 mag simply to represent the datasets in Figure 2. Interestingly, the *z* < 0.5 datasets included in the compilation are primarily biased toward *i* < 22.5 mag and do not contain significantly faint sources.

## 5.1. Completeness of the Compilation

Measuring the selection completeness function of the compilation is quite difficult given the diverse range of selection functions and observational biases that encompass the full dataset. However, we can address the spectroscopic completeness of the compilation, which we define as the fraction of galaxies within a galaxy property range that has spectroscopic confirmation compared to the total population of galaxies observed within the same associated range.

We use the COSMOS2020/Classic catalog as our basis for the total galaxy population and limit our compilation to only those with matches (93.5% of all sources within the 2 deg² COSMOS coverage in the compilation). Figure 3 shows the completeness fraction in terms of HSC *i* and UltraVISTA $K_s$ magnitudes for both “all” sources with spectroscopic redshifts and those restricted to a high $Q_f$. We find that the full sample is 50% complete down to 23.4 mag (*i*) and 21.6 mag ($K_s$). Restricting this to just the higher $Q_f$ flags, we find a 50% completeness limit of 22.8 mag (*i*) and 20.7 mag ($K_s$). This does not take into account the redshift distribution, and, as shown in Figure 2, the spectroscopic compilation contains numerous *z* < 1 galaxies that typically have *i* < 25 mag, while

<figure><img src="image_7.png" /><figcaption>**Figure 3.** The spectroscopic completeness in terms of HSC *i* (blue) and UltraVISTA $K_s$ (red) magnitudes, with the full population and high-$Q_f$ subset shown as filled circles and open squares, respectively. We find 50% spectroscopic completeness for the full (high-$Q_f$) sample at *i* ~ 23.4 (22.8) mag and $K_s$ ~ 21.6 (20.7) mag. Note that this does not include any redshift dependency.</figcaption></figure>

*z* > 1 sources are, as expected, biased toward fainter *i* magnitudes.

Figure 4 shows the completeness limits as a function of redshift and magnitude. For each grid point, we measure the
<figure><img src="image_8.png" /><figcaption>**Figure 4.** The spectroscopic completeness (in percentage) based on HSC *i* (top panel) and UltraVISTA *Ks* (bottom panel) magnitudes factoring in redshift dependency. The left panels are for the full compilation, and the right panels are for the high-*Q*<sub>*f*</sub> subset. The 50% completeness limit is roughly constant at *i* ~ 23–23.5 mag but is variable in the high-*Q*<sub>*f*</sub> subset, shifting to fainter magnitudes from *z* ~ 0 to *z* ~ 2. The 50% completeness limit in *Ks* is variable in both the full compilation and the high-*Q*<sub>*f*</sub> subset. Note that >100% is expected and signifies disagreements between COSMOS2020 photometric redshifts and the spectroscopic redshifts in our compilation. Overall, this highlights the need to understand the completeness not only of the whole sample but also at various redshift and magnitude slices.</figcaption></figure>

fraction of galaxies that have similar magnitudes and redshifts
in the compilation compared to the full population of galaxies.
Given that COSMOS2020 only has photometric redshifts and
there will be disagreements between photometric and spectro-
scopic redshifts, it is therefore expected that some of the grid
points will have >100% completeness.

We find that *z* < 1 galaxies (with all *Q*<sub>*f*</sub>) show 50%
completeness at *i* < 23.5 mag (*K*<sub>s</sub> < 22.5–23 mag), consistent

with the case where redshift distributions are not factored in
the completeness measurement (see Figure 3). Limiting the
samples to high Qf, we find a 50% completeness limit of
i < 22.5–22.5 mag and Ks < 20.5–22 mag for z < 1 galaxies.
At 1 < z < 2, we find that 50% completeness to be consistent
with i ~ 23.5–24 mag and Ks ~ 20.5–21 mag for the full
compilation and i ~ 23.5 mag and Ks ~ 20 mag when limited
to high Qf.
<figure><img src="image_9.png" /><figcaption>**Figure 5.** The spatial spectroscopic completeness of the compilation limited to *i* < 24 mag (top) and *K*<sub>s</sub> < 22.5 mag (bottom), with the full (high-Q<sub>f</sub>) samples shown in the left (right) panels. Each numerical value corresponds to the spectroscopic completeness of that specific 0.01 deg<sup>2</sup> region. The central region has the highest spectroscopic completeness, due to the many follow-up programs focused on the CANDELS HST/ACS (pink) and WFC3 (red) region (e.g., 3D-HST, G. B. Brammer et al. 2012; MOSDEF, M. Kriek et al. 2015; HETDEX, E. Mentuch Cooper et al. 2023). The outer regions also have ≥30% completeness even for the high-Q<sub>f</sub> subset, reaching the edges of the HST/ACS F814W imaging (green). Future JWST spectroscopic follow-up within COSMOS-Web (orange) and PRIMER (pink) and other COSMOS-based programs, including PASSAGE (PI: Matthew Malkan) and COSMOS-3D (PI: Koki Kakiichi), will both increase the spatial spectroscopic completeness and extend it to fainter magnitudes.</figcaption></figure>

The spectroscopic completeness at $z > 2$ for the full sample is mostly consistent with $i \sim 22.5-23.5$ at the 50% level. In terms of $K_s$ magnitudes, we find that the 50% level reaches $\sim 23$ mag at $5 < z < 7$ for the full sample and is driven by spectroscopic follow-up of Ly$\alpha$ emitters in the observer-frame optical. Such sources typically have blue UV spectral slopes, with the $K_s$ band corresponding to the rest-frame

$\sim 2500-4000$ Å. However, limiting to high $Q_f$ essentially removes this $>50\%$ completeness, primarily due to the difficulty of reliably confirming high-$z$ targets. We note a spectroscopic completeness $>50\%$ for high-$Q_f$ samples, with $K_s < 22$ mag at $3 < z < 4$, which is based on confirmation of [O III]+H$\beta$ emitters in the NIR and Ly$\alpha$ emitters in the observer-frame optical.
Lastly, we investigate the spatial completeness by calculat-
ing the fraction of galaxies within the compilation relative to
the COSMOS2020/Classic catalog within a given area in
COSMOS. Figure 5 shows the spatial completeness for the full
2 deg² field limited to galaxies with *i* < 24 mag and *K*<sub>s</sub> < 22.5
mag. We apply this restriction since the full spectroscopic
completeness decreases significantly at fainter magnitudes, as
shown in Figure 3. Not applying these faint cutoffs would
result in low spatial completeness measurements, solely due to
the overall low spectroscopic completeness at fainter
magnitudes.

Figure 5 shows that the spatial completeness without limiting $Q_f$ is mostly >50% throughout, with >80% within the central area of COSMOS. This is mainly due to the many spectroscopic follow-up programs of galaxies identified within the HST/CANDELS (N. A. Grogin et al. 2011; A. M. Koeke-moer et al. 2011) region as shown by the pink (ACS) and red (WFC3/IR) footprints overlaid on Figure 5, as well as the DESI-EDR (DESI Collaboration et al. 2024), DR1 (DESI Collaboration et al. 2025), and DR2-COSMOS (J. Ratajczak et al. 2025) datasets. Limiting to high $Q_f$, we find that the CANDELS/COSMOS region has >60% completeness, while moving farther away results in decreasing completeness (~30%–40%). This is expected given that the CANDELS program focused on deep, spatially resolved imaging of galaxies serving as prime objects for spectroscopic follow-up. We do note other smaller regions that have elevated spectroscopic completeness, such as one just southwest (R.A. ~150°, decl. ~2°) of CANDELS that has ≥50% spectroscopic completeness ($Q_f$ = 3–4).

Overall, Figure 5 highlights how much spectroscopic focus
has been placed on the central regions of COSMOS given the
wealth of space-based, spatially resolved imaging to accom-
pany spectroscopic studies. At the same time, wide-field
spectroscopic surveys such as DESI have helped to increase
the spatial completeness to ~30%–45% farther from the
central regions. However, Figure 5 highlights the need to still
spectroscopically explore farther away from the central region,
as well as push toward fainter magnitudes, where the spatial
completeness decreases throughout the field. This is already
changing with the advent of JWST programs such as
COSMOS-Web (PIS: J. Kartaltepe & C. Casey, C. M. Casey
et al. 2023; orange footprint in Figure 5) and COSMOS-3D
(PI: K. Kakiichi), as well as other programs such as CLUTCH,
an HST Multi-Cycle Treasury program to obtain UV–NIR
imaging over the COSMOS-Web area (PI: J. Kartaltepe),
which will motivate future ground- and space-based spectro-
scopic observations populating regions currently lacking in
spectroscopic follow-up. Programs such as COSMOS-3D,
CAPERS (PI: M. Dickinson), PASSAGE (PI: M. Malkan),
POPIES (PI: J. Kartaltepe), and SAPPHIRES (PI. E. Egami)
are already underway to fill in some of these gaps. Large wide-
field, ground-based spectroscopic campaigns such as Subaru/
PFS (J. Greene et al. 2022), WAVES/4MOST (S. P. Driver
et al. 2019), and VLT/MOONS/MOONRISE (R. Maiolino
et al. 2020) will also significantly increase the spatial
completeness throughout COSMOS.

5.2. Stellar-mass Assessments

As described in Section 4, we use both CIGALE and LePHARE to reassess stellar-mass measurements of galaxies with $Q_f$=3 and 4 and redshifts fixed to the best spectroscopic

<figure><img src="image_10.png" /><figcaption>**Figure 6.** Comparison of stellar-mass measurements from CIGALE and LePHARE for all sources with $Q_f = 3$ and 4. We find that the vast majority of measurements have a one-to-one agreement (black dashed line), with a 95% confidence interval of (−0.28, +0.42) dex (shaded black region). We find negligible systematic offset between the two assessments.</figcaption></figure>

redshift in the compilation. Throughout this work, we refer to
the stellar mass and associated physical properties as
determined from CIGALE but also include LePHARE as a
secondary SED-fitting measurement, as well as for consis-
tency, as past COSMOS catalogs have used LePHARE photo-z
for SED measurements. Figure 6 shows the comparison
between stellar masses determined using both approaches,
where we find that 95% of measurements are in agreement
within −0.3 to +0.4 dex.

The range of stellar masses in the compilation and the median stellar mass and $1\sigma$ scatter at a given redshift are shown in Figure 7, where we find a stark difference between the $z < 1$ and $z > 1$ populations. The typical stellar mass at $z < 1$ ranges from $10^{8.4}$ to $10^{10} M_{\odot}$, with a large scatter ranging from $10^8$ to $10^{11} M_{\odot}$, highlighting the diverse population at low $z$ within the compilation. Figure 7 includes each individual galaxy color coded with its specific SFR (sSFR), where we find that the $z < 1$ subset incorporates both massive galaxies with low sSFR ($<10^{11} \text{ yr}^{-1}$) and low-mass, high-sSFR galaxies ($>10^{-9} \text{ yr}^{-1}$). Several dwarf-like systems (e.g., $<10^8 M_{\odot}$) are also included in the compilation that have relatively high sSFR, reaching as high as $10^{-7} \text{ yr}^{-1}$, which represents a subset of bursty low-$z$ star-forming galaxies.

Selection effects become increasingly noticeable at $z > 1$ as the median stellar mass decreases from ~$10^{10} M_{\odot}$ ($z \sim 1$) to $10^{9.4} M_{\odot}$ ($z \sim 7$) and the scatter tightens from $z \sim 1$ to 1.5 and is relatively constant with increasing redshift. Galaxy populations in the compilation at $z > 1$ are heavily dominated by low-mass, high-sSFR star-forming galaxies. For example, a noticeable cluster of $<10^9 M_{\odot}$ galaxies at $z \sim 5.7$ with sSFR $\sim 10^{-7}$ to $10^{-8} \text{ yr}^{-1}$ is seen in Figure 7, which corresponds to spectroscopic confirmations of narrowband-selected Ly$\alpha$ emitters mainly within 10K-DEIMOS (G. Hasinger et al. 2018) and M2FS (Y. Ning et al. 2020). We note that the former is based on a compilation of several COSMOS DEIMOS programs, including a follow-up of
<figure><img src="image_11.png" /><figcaption>**Figure 7.** The stellar mass (CIGALE)—redshift distribution of high-Q~f~ sources within the compilation, color coded by their CIGALE-determined sSFR on a 10 Myr timescale. The median and 1σ scatter in stellar mass are shown as the black solid line and black shaded region, respectively. Quiescent (low-sSFR) systems are primarily found at z < 1.25 with >10~10~M~☉~, while bursty star-forming (high-sSFR) systems are primarily at z > 2, extending to the highest redshifts in the compilation. The median stellar mass of the compilation is <10~9.4~M~☉~, at z < 0.4, with the distribution being bimodal with a high-mass peak and a low-mass peak. The median mass peaks at z ~ 0.6 and steadily decreases to z ~ 7 from ~10~10~ to 10~9.4~M~☉~, respectively.</figcaption></figure>

narrowband-selected Lyα emitters (e.g., Y. Taniguchi et al. 2015) and Lyman break galaxies (LBGs) selected via dropout technique. Indeed, at z > 3.5, the majority of spectroscopic redshift measurements in the compilation are based on Lyα emission observed within observer-frame optical wavelengths and bias the population toward low-mass, dust-free young galaxies. Future additions that will include the latest JWST/NIRspec measurements could help in diversifying the high-z galaxy population in the compilation, where studies are finding massive, quiescent galaxies at z > 3 (e.g., R. A. Sato et al. 2024; M. Slob et al. 2024; T. Nanayakkara et al. 2025).

## 5.3. Quiescent, Star-forming, and Bursty Galaxy Populations

In this subsection, we take a closer look at the various galaxy populations that encompass the compilation using two classical approaches: NUVrJ rest-frame color–color diagnostics (O. Ilbert et al. 2010, 2013), and the SFR–stellar mass correlation (SFR “main sequence”; e.g., E. Daddi et al. 2007; K. G. Noeske et al. 2007). We first focus on the former, where we use the rest-frame GALEX/NUV, Subaru HSC/r, and Paranal VISTA/J photometry that was measured by convolving the respective filters with the best-fit SED during the CIGALE fitting process. We follow O. Ilbert et al. (2013) in using NUV − r and r − J colors to classify galaxies as quiescent or star-forming, where NUV − r > 3.1 and NUV − r > 3(r − J) + 1 are considered quiescent. This is similar to the commonly used UVJ diagnostic (R. J. Williams et al. 2009); however, NUV − r is found to better trace recent star formation activity compared to U − V, where NUV is sensitive to stellar populations with a light-weighted age of 10~8~ yr and r traces >10~9~ yr stellar populations (S. Arnouts et al. 2007; D. C. Martin et al. 2007).

Figure 8 shows the rest-frame NUVrJ colors at different redshifts, with each galaxy color coded by its sSFR. We find clear, distinct populations of galaxies at varying redshifts making up the compilation. Numerous quiescent galaxies exhibiting low sSFR reaching 10⁻¹² yr⁻¹ are present from

z ~ 0 to 3, with the majority of them at z < 1. These are primarily high-mass galaxies as shown in Figure 7. However, their numbers significantly decrease, progressing from z ~ 1 to higher redshifts, where by 4 < z < 8 we find only a single object with a spectroscopic redshift that could be a quiescent galaxy, which is mainly due to a combination of selection bias within the compilation and the decrease in quiescent fractions with increasing redshift.

Populations of star-forming galaxies are also found to be present at all redshifts, with several key differences. The z > 3 population is mostly clustered around a locus in NUV − r ~ 0–2 mag and r − J ~ 0 mag that signifies a population consisting of young stellar populations (recent star formation) and relatively little dust (blue r − J). The locus also corresponds to galaxies that have sSFR > 10⁻⁸ yr⁻¹ signifying, <100 Myr mass-doubling times. This is most likely a by-product of selection functions in the high-z part of the compilation being heavily dependent on the confirmation of Lyα emitters within observer-frame optical wavelengths. At z < 3, we start to note both a population of star-forming galaxies at the same loci and an extension to redder r − J colors, signifying the inclusion of dusty star-forming galaxies. This is due to galaxies observed with strong rest-frame optical emission lines (e.g., Hα, [O III], [O II]) within observer-frame optical and NIR spectroscopy that are less susceptible to dust extinction compared to rest-frame UV lines (e.g., Lyα). In effect, dusty star-forming galaxies would be included in the compilation at z < 3, given how the selection was done for follow-up observations. Post-starburst galaxies are also within the compilation mostly at z < 2, showing similar 0 < r − J < 0.5 mag and elevated NUV − r suggesting a population of galaxies potentially transitioning from the young, dust-free star-forming galaxy loci toward the mature, passive galaxy classification.

Figure 9 shows the SFR–stellar mass correlation (“main sequence”) for the same redshift ranges in Figure 8 and highlights the diverse range of galaxies in the compilation at
<figure><img src="image_12.png" /><figcaption>**Figure 8.** Rest-frame *NUV* − *r* vs. *r* − *J* colors color coded by sSFR within a 10 Myr timescale. The black dashed line represents the quiescent (above) and star-forming (below) galaxy separation as defined by O. Ilbert et al. (2013). The compilation contains numerous *z* < 2 passive/quiescent galaxies with sSFR < 10<sup>-11</sup> yr<sup>-1</sup>, as well as star-forming galaxies, with many within the blue *NUVrJ* loci corresponding to young star-forming galaxies with sSFR > 10<sup>-8</sup> yr<sup>-1</sup>. At *z* > 2, we find that the vast majority of galaxies reside within blue *NUVrJ* colors and have sSFR reaching 10<sup>-7</sup> yr<sup>-1</sup>. Only a few galaxies fall within the passive galaxy selection area at *z* > 2; however, we do note several “star-forming” galaxies exhibiting redder *NUV* − *r* colors and low sSFR, which could be galaxies transitioning into a quiescent state (e.g., post-starburst galaxies).</figcaption></figure>

low *z*. Overlaid on each panel are measurements from previous studies (J. S. Speagle et al. 2014; B. Salmon et al. 2015; C. Schreiber et al. 2015; A. R. Tomczak et al. 2016; J. E. Thorne et al. 2021; P. Popesso et al. 2023; A. Calabrò et al. 2024) limited to their respective stellar-mass and redshift ranges. Overall, the majority of sources in the compilation at *z* < 4 fall along past studies of the SFR-stellar mass correlation, with the scatter highlighting the diverse range of galaxies in the compilation. At *z* < 1, we find many >10<sup>10</sup> *M*<sub>☉</sub> galaxies falling well below the main sequence, implying a quiescent, passive system. These sources also make up the low-sSFR quiescent cloud seen in Figure 8. Several studies also capture this turnover in the SFR-stellar mass correlation (e.g., K. E. Whitaker et al. 2014; N. Lee et al. 2015; C. Schreiber et al. 2015; A. R. Tomczak et al. 2016; S. K. Leslie et al. 2020; K. C. Cooke et al. 2023; P. Popesso et al. 2023).

At 4 < *z* < 8, we find that many galaxies lie well above the SFR-stellar mass correlation signifying populations of star-burst galaxies. This is also evident with the color coding in Figure 9, which shows the ratio between SFRs as measured on a 10 and 100 Myr timescale via CIGALE. All sources above the correlation at *z* > 4 have log<sub>10</sub> SFR<sub>10Myr</sub>/SFR<sub>100Myr</sub> > 0,

implying a recent burst of star formation activity, and are supported by the elevated sSFR, suggesting mass-doubling timescales of <100 Myr. As described above, the high-*z* regime of the compilation is biased toward Lyα emitters, which are known for their young stellar populations (recent star formation activity; e.g., S. Malhotra & J. E. Rhoads 2002; Y. Ono et al. 2010; S. Santos et al. 2020), which explains the many sources lying well above the SFR-stellar mass correlation.

The compilation also includes numerous 1 < *z* < 4 starburst galaxies exhibiting sSFR > 10<sup>-8</sup> yr<sup>-1</sup> and SFR<sub>10Myr</sub>/SFR<sub>100Myr</sub> reaching up to 10. However, the number of starburst galaxies starts to decrease at *z* < 2, with no galaxy in the compilation having an sSFR > 10<sup>-8</sup> yr<sup>-1</sup> at *z* < 0.5 (although this is partly due to selection effects; see Section 6.2). This also falls in line with the gradual decrease in the cosmic SFH (e.g., P. Madau & M. Dickinson 2014; A. A. Khostovan et al. 2015; J. A. Zavala et al. 2021) and cosmic sSFR (e.g., J. S. Speagle et al. 2014; A. L. Faisst et al. 2016; A. A. Khostovan et al. 2024). Overall, the compilation includes a wide range of galaxies ranging from bursty to typical star-forming galaxies and massive, quiescent galaxies.
<figure><img src="image_13.png" /><figcaption>**Figure 9.** The SFR-stellar mass correlation for different redshift subsets color coded by their 10-to-100 Myr SFR ratios. Included are observed (B. Salmon et al. 2015; C. Schreiber et al. 2015; A. R. Tomczak et al. 2016; S. K. Leslie et al. 2020; J. E. Thorne et al. 2021; A. Calabrò et al. 2024) and compilation (J. S. Speagle et al. 2014; P. Popesso et al. 2023) measurements limited to the stellar-mass ranges of the sample (model) limits. The compilation shows the massive, quiescent populations at >10<sup>10</sup> M<sub>☉</sub> up to z ~ 2 consistent with a turnoff in the SFR-stellar mass correlation. sSFR > 10 Gyr<sup>-1</sup> systems also corresponding to log<sub>10</sub> SFR<sub>10Myr</sub>/SFR<sub>100Myr</sub> > 0 correspond to starburst outliers in the SFR-stellar mass correlation, with the vast majority of the z > 4 sources in the compilation falling within this regime. Overall, the compilation at z < 4 is consistent with past SFR-stellar mass correlation measurements, with the spread attributed to variations in star formation activity and SFHs.</figcaption></figure>

5.4. Diversity of Environments

The large number of sources spread out with a wide, contiguous area and high-quality spectroscopic redshifts in the compilation allows us to visualize 3D large-scale structure in the field, highlighting the diverse range of environments that galaxies reside in, such as fields, filaments, and clusters. This also enables the compilation to be used in studies focused on how environment (position of a galaxy within large-scale structure) can affect the underlying evolution of galaxy populations.

There are many different density measurement approaches to map out large-scale structures. These include Voronoi tesselation (e.g., N. Scoville et al. 2013; B. Darvish et al. 2015; A. R. Tomczak et al. 2016; B. C. Lemaux et al. 2017; M. Brinch et al. 2023; D. Hung et al. 2025), kernel density estimation (e.g., B. Darvish et al. 2014, 2015; N. Chartab et al. 2020; M. Brinch et al. 2023; S. Taamoli et al. 2024), optimal filtering (e.g., F. Bellagamba et al. 2011; M. Maturi et al. 2019; G. Toni et al. 2025), friends-of-friends group finding (e.g., C. Knobel et al. 2012), and nearest neighbor algorithm (e.g., M. C. Cooper et al. 2008; D. Sobral et al. 2011; N. B. Sillassen et al. 2022, 2024). For the purposes of our simple visual demonstration, we measure the densities using a 10th-nearest neighbor approach. This analysis is limited only to those that have $Q_f = 2-4$ and $12-14$, with the inclusion of the $Q_f = 2, 12$ flags to increase the sample size to better trace large-scale structure while also not significantly increasing the uncertainties due to poorly constrained spectroscopic redshifts. We convert from angular-z space into 3D comoving space. This is done by first centering the angular space on the center of COSMOS (R.A. = 150°, decl. = 2°2) and then calculating the

traverse comoving separation from the center. The LOS comoving distance is measured using the spectroscopic redshift forming the z-axis. We then calculate the local density for each galaxy as

$$ \Sigma_{10} = \frac{11}{\frac{4}{3}\pi r_{10}^3}, \qquad (1) $$

where $\Sigma_{10}$ is the local density around the reference galaxy and the nearest 10 neighbors within a spherical region of size $r_{10}$ corresponding to the distance of the 10th neighbor (maximum distance). Note that this does not include any corrections for completeness and edge effects and is only meant to be a visual demonstration of the variety of environments that are traced within the compilation. Careful considerations need to be made for proper density measurements.

Figure 10 shows two interesting examples of large-scale structure within the compilation. Both boxes are limited in width to 1° in both R.A. and decl., which corresponds to 68 cMpc and 102 cMpc for the top and bottom panels, respectively. The z-axis of each panel corresponds to increasing redshift from bottom to top. For each box, we also label the redshift range shown, as well as the comoving dimensions where x and y are of same size.

The bottom panel of Figure 10 corresponds to the large and extended overdense region known as “Hyperion,” first fully identified as being a super-protocluster with multiple components by O. Cucciati et al. (2018) within VUDS (O. Le Fèvre et al. 2015; L. A. M. Tasca et al. 2017), zCOSMOS-Bright (S. J. Lilly et al. 2007), and zCOSMOS-Deep (PI: Simon Lilly;
<figure><img src="image_14.png" /><figcaption>**Figure 10.** Examples of large-scale structure features in the compilation traced by the 10th-nearest neighbor algorithm. Each example encompasses the full COSMOS angular area with only a cut in redshift space. The top panel shows a thin redshift slice at $z \sim 1.24-1.27$ (63 cMpc LOS distance), where we find two overdense regions separated by $\sim 40$ cMpc. The bottom panel shows the extensively studied “Hyperion” system (O. Cucciati et al. 2018), which we find to be composed of three overdense regions. Such 3D visualizations of large-scale structure are only possible given the many spectroscopic redshift measurements within the compilation and demonstrate how the compilation can be used to effectively visualize and study large-scale structure and its effects on galaxy evolution. The online animated version of this figure provides a full 360° rotation around the center of the box, allowing for a full visualization of large-scale structure.</figcaption></figure>

(An animation of this figure is available in the online article.)

C. Diener et al. 2015) spectra. Previous studies also traced this structure via Lyα emitters (e.g., Y.-K. Chiang et al. 2015; Y. Huang et al. 2022), Lyα forest tomography (e.g., K.-G. Lee et al. 2016; A. B. Newman et al. 2020), CO-emitting galaxies (e.g., T. Wang et al. 2016; J. B. Champagne et al. 2021), and submillimeter star-forming galaxies (e.g., C. M. Casey et al. 2015). Figure 10 also shows the larger density peaks correlated with three distinct clumps at $z \sim 2.44, 2.47$, and $2.51$, corresponding to a width of $\sim 80$ cMpc. Elevated $\Sigma_{10}$ in between each overdense peak could potentially be connecting filamentary structure.

The top panel of Figure 10 shows two overdense regions at $z \sim 1.25$ (R.A. = 149°8, decl. = 2°05) and $z \sim 1.26$ (R.A. = 150°1, decl. = 2°39) separated by $\sim 40$ cMpc not previously identified in the literature. Based on our simple assessment of $\Sigma_{10}$, we find that the two overdensities seem to be surrounded by somewhat elevated $\Sigma_{10}$ that could also signify both overdensities are peaks in a much larger structure. A closer look at the $z \sim 1.26$ overdensity shows an extension of $\Sigma_{10} \sim 0.01$ kpc$^{-3}$, extending down halfway to the $z \sim 1.25$ peak providing some suggestion that the two overdensities are

connected. We note that both overdensities were also identified via a separate large-scale structure tracing approach that used our compilation (G. Toni et al. 2025).

These are just two examples of overdensities that are part of the compilation, and we note that several other overdensities are present, especially toward the low-$z$ end of the compilation. Note that in both examples we have not only peaks corresponding to regions where galaxies are spatially clustered with one another but also regions with $\Sigma_{10} < 10^{-3}$ kpc$^{-3}$ that are sparsely populated, highlighting how the compilation has a wide, diverse range of environments and can be used for detailed environmental (large-scale structure) studies (e.g., comparing physical properties of galaxies residing in overdense regions and within sparsely populated fields).

## 6. Application Examples

The compilation has many practical applications for both calibration and scientific analyses. As listing and describing each would go beyond the scope of this paper, we only focus on two main applications: validation of photometric redshifts in surveys (Section 6.1), and using SOMs to infer which galaxy populations currently lack spectroscopic follow-up, thereby enabling new science and future observing campaigns (Section 6.2).

### 6.1. Validation of Photometric Redshifts

Any galaxy imaging survey requires proper assessment of each galaxy’s redshift in order for the dataset to be used for a multitude of science cases. Typically this is done by using all available multiwavelength photometry for each galaxy (see M. Salvato et al. 2019, for a review) and using at least one of the many template-based photo-$z$ codes in the literature, such as LePHARE (S. Arnouts et al. 1999; O. Ilbert et al. 2006) and EaZY (G. B. Brammer et al. 2008). However, photo-$z$ measurements are limited by the available photometry (e.g., not enough to constrain SEDs in template fitting) and the limited grid space in physical parameters that generate the templates used for photo-$z$ determination. Therefore, it is crucial to validate photo-$z$ measurements by testing how well the measured photo-$z$ matches with known spectroscopic redshifts on a population level. Compilations such as the one presented in this paper are valuable resources in testing the reliability and validity of measured photometric redshifts (e.g., C. Laigle et al. 2016; A. J. Battisti et al. 2019; J. R. Weaver et al. 2022). In this section, we will demonstrate how the compilation can be used to validate photo-$z$ measurements from various versions of the COSMOS catalogs, highlight any systematics, and also gauge the reliability of redshift measurements for different galaxy population subsets (e.g., bright vs. faint galaxies).

We first define the diagnostics that we will use to assess the photo-$z$ validation. The systematic offset between the photometric and spectroscopic redshift is measured as the redshift bias, $b$, defined as

$$b = \text{median} \left( \frac{z_{\text{phot}} - z_{\text{spec}}}{1 + z_{\text{spec}}} \right), \quad (2)$$

with $z_{\text{phot}}$ and $z_{\text{spec}}$ being the photometric and spectroscopic redshifts, respectively. A nonzero $b$ signifies a systemic offset in $z_{\text{phot}}$ relative to $z_{\text{spec}}$ for the sample as a whole. The precision
<figure><img src="image_15.png" /><figcaption>**Figure 11.** Comparison between photometric and spectroscopic redshifts for COSMOS2020 Classic (left) and Chandra/COSMOS-Legacy (right). The gray region corresponds to the condition $|(z_{phot} - z_{spec})/(1 + z_{spec})| < 0.15$, with sources outside this region making up the outlier fraction ($\eta$). The corresponding NMAD ($\sigma$) is shown for each subsample. Both COSMOS2020 and C-COSMOS use LEPHARE for photometric redshift measurements, with the difference that C-COSMOS specifically uses AGN templates (M. Salvato et al. 2011; S. Marchesi et al. 2016) and COSMOS09 photometry (O. Ilbert et al. 2009), while COSMOS2020 photo-z are measured using both galaxy and AGN templates (whichever has the best $\chi^2$). Both datasets demonstrate that improved AGN templates are required to obtain more accurate photometric redshifts, thereby reducing outlier fractions relative to spectroscopic samples. The compilation could serve as a valuable calibration dataset in such an endeavor.</figcaption></figure>

<table><caption>**Table 3**

Photometric Redshift Quality Assessment of Past COSMOS Catalogs Using Our Spec-z Compilation</caption><thead><tr><th>Catalog</th><th>References</th><th>N<sub>obj</sub></th><th>σ<sub>NMAD</sub></th><th>η<br>(%)</th><th>b</th></tr></thead><tbody><tr><td colspan="6" style="text-align: center; font-weight: bold;">Galaxies</td></tr><tr><td>COSMOS2009</td><td>O. Ilbert et al. (2009)</td><td>30,329</td><td>0.012</td><td>5.0</td><td>-0.001</td></tr><tr><td>COSMOS2015</td><td>C. Laigle et al. (2016)</td><td>37,083</td><td>0.013</td><td>8.6</td><td>-0.001</td></tr><tr><td>COSMOS2020 (Classic)</td><td>J. R. Weaver et al. (2022)</td><td>25,476</td><td>0.011</td><td>3.2</td><td>-0.003</td></tr><tr><td>COSMOS2020 (Farmer)</td><td>J. R. Weaver et al. (2022)</td><td>23,631</td><td>0.012</td><td>3.4</td><td>0.000</td></tr><tr><td colspan="6" style="text-align: center; font-weight: bold;">X-ray AGN</td></tr><tr><td>COSMOS2015</td><td>C. Laigle et al. (2016)</td><td>869</td><td>0.027</td><td>20.5</td><td>-0.003</td></tr><tr><td>COSMOS2020 (Classic)</td><td>J. R. Weaver et al. (2022)</td><td>1704</td><td>0.024</td><td>8.5</td><td>-0.009</td></tr><tr><td>COSMOS2020 (Farmer)</td><td>J. R. Weaver et al. (2022)</td><td>1207</td><td>0.030</td><td>10.2</td><td>-0.009</td></tr><tr><td>Chandra/COSMOS-Legacy (all)</td><td>S. Marchesi et al. (2016)</td><td>595</td><td>0.014</td><td>10.3</td><td>-0.001</td></tr><tr><td>Chandra/COSMOS-Legacy (type 1)</td><td>S. Marchesi et al. (2016)</td><td>429</td><td>0.012</td><td>10.7</td><td>-0.000</td></tr><tr><td>Chandra/COSMOS-Legacy (type 2)</td><td>S. Marchesi et al. (2016)</td><td>166</td><td>0.020</td><td>9.0</td><td>-0.005</td></tr></tbody></table>

**Note.** All photometric redshifts are measured using LEPHARE, with the exception of S. Marchesi et al. (2016), which used LEPHARE with an AGN template library described in M. Salvato et al. (2011). Spectroscopic matches used to validate photometric redshifts are restricted to $Q_f = 3-4$ and 13-14. For each catalog shown, we include the number of galaxies in the sample ($N_{gal}$), the NMAD ($\sigma_{NMAD}$; Equation (3)), the outlier fraction ($\eta$; Equation (4)), and bias ($b$; Equation (2)).
of $z_{phot}$ is assessed by using the normalized median absolute deviation (NMAD; D. C. Hoaglin et al. 1983), defined as

$$ \sigma_{\text{NMAD}} = 1.4821 \times \text{median} \left( \frac{|\Delta z - \text{med}(\Delta z)|}{1 + z_{\text{spec}}} \right), \quad (3) $$

where $\Delta z = z_{phot} - z_{spec}$, which has been found to be unaffected by outliers (e.g., O. Ilbert et al. 2006). The outlier fraction, $\eta$, represents the fraction of galaxies for which $z_{phot}$ deviates from $z_{spec}$ by

$$ \left| \frac{z_{phot} - z_{spec}}{1 + z_{spec}} \right| > 0.15, \quad (4) $$

where the criterion of 0.15 is based on past photo-z validation tests (e.g., H. Hildebrandt et al. 2012; J. R. Weaver et al. 2022).

For all validation tests, we crossmatch each respective photometric catalog, with the compilation limited to only those that have high-quality spectroscopic redshifts ($Q_f = 3, 4, 13, 14$). All photometric redshifts tested are based on LePHARE, and we name each column from the original released catalogs that were used in the photo-z validity tests. We refer the reader to the references listed in Table 3 for specific details on the parameters adopted for LePHARE in each case. For both the COSMOS2020 Classic and Farmer catalogs, we use the best-fit photometric redshift ($lp\_zbest$) in the case where the source is classified as a galaxy based on its best-fit template ($lp\_type=0$). If the best-fit template is associated with an X-ray source ($lp\_type=2$), then we use the AGN template (see Table 3 of M. Salvato et al. 2009; M. Salvato et al. 2011) determined photometric redshift ($lp\_zq$) only under the condition that the AGN template $\chi^2$ is less than the galaxy template $\chi^2$ ($lp\_chiq<lp\_chi2\_best$). In the case of COSMOS2015, we use the best-fit $z_{phot}$ (ZMINCHI2) under the condition that $TYPE=0$ (galaxy template) and only use the AGN template $z_{phot}$ ($ZQ$) under the same condition as in COSMOS2020 (CHIQ<CHI2\_BEST). For COSMOS2009, we only test the photo-z ($zp\_best$) from the best-fit galaxy templates ($type=0$). Lastly, we also perform validity tests of the photo-z measurements for Chandra/COSMOS (S. Marchesi et al. 2016) sources limited to only those corresponding to type 1 ($phot\_type=1$) and type 2 ($phot\_type=2$) AGN.

Table 3 shows the photo-z validation tests for all the catalogs mentioned above separated based on the redshifts being measured from galaxy and X-ray AGN templates. Overall the bias in each survey is consistent with zero, with only the COSMOS2020 catalogs for the X-ray AGN template measured $z_{phot}$ having a $b \sim -0.01$, which is still quite negligible. This suggests that past photo-z assessments do not suffer from any systematic offsets on a population level. Figure 11 also shows how the COSMOS2020 Classic and Chandra/COSMOS catalogs have no clear systematic offset from a one-to-one relation between the photometric and spectroscopic redshifts. For all surveys, $\sigma_{NMAD}$ is consistently around ~0.01 for the galaxy template $z_{phot}$, suggesting that the inclusion of newer and deeper data since the COSMOS2009 catalog has not changed the precision of photo-z measurements. However, the outlier fraction has improved significantly, with COSMOS2015 having the highest at 8.6% while the COSMOS2020 catalog has 3.2% and 3.4% for Classic and Farmer, respectively. This is also highlighted in Figure 11,

<figure><img src="image_16.png" /><figcaption>Figure 12. Photometric redshift quality assessment for different ranges of i magnitude within a 2" aperture for COSMOS2020 Classic, COSMOS2020 Farmer, and C-COSMOS (type 1 and 2 AGN only) using all reliable spec-z measurements in the compilation. Outlier fractions (top panel) and NMAD (bottom panel) are shown for each of the datasets. Both properties increase with fainter i magnitude, where ~12% of photo-z measurements with associated spec-z are invalidated by ~25 mag. This is also the magnitude range corresponding to high-z candidates, where contamination rates are higher, explaining the increase in outlier fraction and NMAD (e.g., LBG/Ly$\alpha$ emitter is found to be a low-z interloper). As demonstrated here, the compilation can easily be used for high-quality photo-z validation measurements, given the numerous spec-z measurements, and can be subdivided into magnitudes or other properties, allowing for a detailed assessment of photo-z reliability in new samples.</figcaption></figure>

which shows that the vast majority of photo-z measurements are within the 0.15 criterion (Equation (4); black shaded region in Figure 11). The outliers are found to be primarily $z < 1$ galaxies that are spectroscopically confirmed to be $z > 2$ galaxies corresponding to the Lyman/Balmer break degeneracy. Inclusion of NIR photometry is very useful in breaking this degeneracy (e.g., A. J. Battisti et al. 2019).

The validity of photo-z measurements using AGN-based templates is found to be less reliable compared to the galaxy template photo-z measurements. Table 3 shows the validity statistics for the AGN template $z_{phot}$ measurements, where we find no significant bias; however, $\sigma_{NMAD}$ is ~2-3x higher
than what is found for galaxy templates except for the full Chandra/COSMOS-Legacy sample and its type 1 AGN subset ($\sigma_{NMAD} \sim 0.01$). The outlier fractions are also significantly higher, with the COSMOS2015 catalog having the highest at $\sim 20\%$. The COSMOS2020 catalogs have similar $\eta \sim 10\%$ to the Chandra/COSMOS measurements; however, this is $\sim 3 \times$ higher than $\eta$ from galaxy-based templates. Figure 11 shows how the photo-$z$ compares with spec-$z$ for type 1 and type 2 AGN, where we find no systematic offsets from a one-to-one relation but find elevated $\eta$. This could suggest that improved AGN templates are needed to better reduce outlier fractions. In this regard, the compilation could potentially be used to derive such improved templates.

Figure 12 shows the photo-$z$ validation statistics for the COSMOS2020 Classic, COSMOS2020 Farmer, and Chandra/COSMOS catalogs as a function of HSC *i* magnitude. As we also found for the full population photo-$z$ validity tests, the galaxy-template-based photo-$z$ measurements show the best results in terms of lower $\sigma_{nmad}$ and $\eta$ at each given *i* magnitude. In all three catalogs, we find that the precision decreases and outlier fractions increase significantly toward fainter magnitudes. The outlier fractions of COSMOS2020 Classic (galaxy-template-based) reach $>10\%$ by *i* $\sim 25$ mag and $\sim 30\%$ by *i* $\sim 27$ mag. This is close to the $3\sigma$ limit (27.6 mag; Table 1 of J. R. Weaver et al. 2022) such that it is expected that $\eta$ will be elevated toward the detection limit, where SEDs will become less constrained (especially if there is sparser multiwavelength coverage; e.g., nondetections and poor S/N), increasing uncertainties in the photo-$z$ fitting. Farmer is found to best perform at fainter magnitudes compared to Classic as first reported in J. R. Weaver et al. (2022). Comparing Chandra/COSMOS and COSMOS2020 Classic (X-ray), we find that the former has smaller $\sigma_{NMAD}$, which would imply less scatter. However, we find that the outlier fractions are higher at *i* < 22 mag for Chandra/COSMOS, reaching $\sim 20\%$ at the faintest magnitude. From these tests, we can determine how reliable photo-$z$ measurements are for different subpopulations of galaxies.

Future surveys targeting COSMOS can easily use the compilation and run similar photo-$z$ validation tests to those demonstrated above. Especially with the inclusion of JWST photometry from large programs such as COSMOS-Web (C. M. Casey et al. 2023; M. Franco et al. 2025; S. Harish et al. 2025; M. Shuntov et al. 2025a) and PRIMER (PI: J. Dunlop), we may see improved photo-$z$ measurements, especially given the wealth of NIR/MIR constraints that would break degeneracies such as the Lyman/Balmer breaks as noted in Figure 11. Overall, this compilation, as well as future versions as more spectroscopic redshifts are incorporated, will serve as a valuable legacy resource in order to calibrate and validate photo-$z$ measurements for future surveys.

## 6.2. Self-organizing Maps

An SOM (T. Kohonen 1990) is an unsupervised machine learning approach that organizes higher-dimensional datasets into a 2D organized grid space while also preserving topological relationships (e.g., data points mapped out into nearby points in the grid have similar features). This makes SOM quite powerful for identifying patterns within higher-dimensional datasets. Past implementations of SOM have been used to primarily demonstrate implementation and optimization of template-based model fitting for photometric redshift

and galaxy property determinations (e.g., J. E. Geach 2012; D. Masters et al. 2015; S. Hemmati et al. 2019a, 2019b; S. Sanjaripour et al. 2024; F. Abedini et al. 2025). Such efforts are crucial in preparing the infrastructure for next-generation surveys planned with Euclid, Roman, and ELTs that will observe a significantly larger number of galaxies, making traditional photometric redshift and physical property determination methods computationally inefficient.

For the scope of this work, we use SOMs to rather understand what subpopulations of galaxies currently have spectroscopic coverage within the compilation and which galaxy types are currently lacking any past observation (e.g., D. C. Masters et al. 2017). In this way, we can use archival datasets to get a big-picture representation of what galaxy subpopulations currently are missing spectroscopic follow-up, thereby informing future survey strategies. This builds on a similar approach (D. Masters et al. 2015) that led to programs such as C3R2 (D. C. Masters et al. 2017) that used SOMs as the basis of their targeting strategy. Future programs such as 4MOST-4C3R2 (D. Gruen et al. 2023) will also use an SOM-based strategy to target underrepresented populations.

### 6.2.1. Training the Maps

We start by using the Python-based `sompy` package (V. Moosavi et al. 2014) to train the SOMs using the COSMOS2020 Classic catalog limited to *i* < 26 mag, which takes into account the incompleteness limits of both our spectroscopic compilation and the Classic catalog. The training datasets are subdivided into three redshift bins such that we have three different SOMs: low $z$ (0.1 < $z$ < 1), mid-$z$ (1 < $z$ < 3), and high $z$ (3 < $z$ < 9). Given that the low-$z$ population is so numerous, its features could potentially wash out those associated with the mid- and high-$z$ populations during the training process.

The features used in each SOM are the color combinations, $u-g, g-r, r-i, i-z, z-y, y-J, J-H$, and $H-K_s$, and the photometric redshift (lp_zbest), stellar mass (lp_mass_best), and sSFR (lp_sSFR_best). The last three are typically not included in SOM implementations; however, our main focus is not to use SOM to measure these three properties but rather to use this information to understand which populations in terms of their SED shapes (colors), stellar mass, redshift, and sSFR are missing from past COSMOS spectroscopic observations. Each feature is normalized prior to the training process by its variance.

One main problem with SOMs is the handling of missing data (e.g., nondetections). One approach is applying a detection threshold criterion (e.g., $>5\sigma$ depth) to ensure that there are no missing data; however, this would severely limit the sample size, as well as place an upper limit in redshift ($z < 3$; the Lyman Limit starts to fall into *u*, resulting in nondetections). Other approaches include linear interpolation of the data (e.g., detection in $z$ and $J$ but missing $Y$). N. Chartab et al. (2023) and V. La Torre et al. (2024) investigated how manifold learning can be used to handle missing data within photometric catalogs; however, these approaches are primarily focused on improving physical property measurements using SOMs.

Since our main objective is to assess which populations do/ do not have spectroscopic coverage, this simplifies our case compared to past approaches in handling missing data. In our specific case, $z > 3$ populations lack detection as the Lyman limit shifts to redder passbands such that including estimated data via imputation will not provide any key physical
<figure><img src="image_17.png" /><figcaption>**Figure 13.** Low-z (0.1 < z < 1) SOM, with all the features used in the training shown in each panel. Only HSC *i* and UltraVISTA *Ks* magnitudes were not included in the training process, and they are shown here only for the purpose of identifying the magnitude ranges for each point in the SOM. Each odd-numbered row shows the COSMOS2020 trained map. The spec-z compilation is projected onto the SOM and is shown on each even-numbered row, highlighting the regions for which spectral coverage currently exists in COSMOS (based on the compilation) and regions for which there is no spectral coverage as of yet. Maps such as these are crucial in identifying subpopulations that have so far gone missing in past spectroscopic observations, such as the intermediate-mass, quiescent population (x ~ 40, y ~ 45) and the various high-sSFR clouds that correspond to galaxies with strong line emission. Such visualizations of galaxy populations using all known photometric and spectroscopic datasets can provide valuable insight on how to proceed in the future in terms of observation strategy and target definitions.</figcaption></figure>

information, as it is expected that these populations have no detections blueward of the Lyman Limit due to the IGM transmission (P. Madau 1995; A. K. Inoue et al. 2014). At z < 3,

missing data are primarily associated with situations where the source was fainter than the detection threshold associated with a specific observed passband. As such, we assign the 2σ upper
<figure><img src="image_18.png" /><figcaption>Figure 14. Mid-z (1 < z < 3) SOM, with a similar description to Figure 13.</figcaption></figure>

detection limit as measured in J. R. Weaver et al. (2022), where we have a nondetection or observed photometry fainter than the $2\sigma$ limit. The latter is to take into account data that may have increased photometric uncertainties.

The SOMs are trained within a grid of 80 × 80 for the low-z and mid-z maps and a 40 × 40 grid for the high-z map (accounting for the lower number of sources). We tested other grid sizes following the approach of I. Davidzon et al. (2019). We start with

a 30 × 30 grid space and train the SOM to monitor quantization error, computational time, and average occupation. We then increment by 10 on each dimension until 100 × 100. We determined that our adopted grid sizes resulted in a minimized quantification error of 0.97, 0.99, and 1.36 for the low-z, mid-z, and high-z maps, respectively, while also not heavily impacting the computational time. The maps are trained assuming 1000 iterations in the rough training and 1000 iterations in the fine-tuning training.
<figure><img src="image_19.png" /><figcaption>**Figure 15.** High-*z* (*z* > 3) SOM, with a similar description to Figure 13. One thing to note are the *u* − *g* and *g* − *r* colors, which are dominated by our 2σ upper limit cuts. This is why *u* − *g* colors are constantly >1 mag for the *z* < 4 population, due to nondetection in *u*, and also why the *u* − *g* colors are consistently blue at *z* > 4. This is not because the populations are blue but rather because the 2σ limits for CFHT/*u* and HSC/*g* are 28.1 and 28.5, respectively (*(u* − *g*) ∼ −0.4 mag).</figcaption></figure>

Figures 13–15 show our trained COSMOS2020 Classic SOM (odd-numbered rows) and our spec-z compilation limited to $Q_f \sim 3-4$ projected within the trained maps (even-numbered rows) for all features. The trained maps inform us about which galaxy properties are currently photometrically observed within COSMOS, while the projection of the spec-z compilation

provides insights on the subpopulations of galaxies that are currently observed and have no spectroscopic coverage, at least for the COSMOS field and programs that make up the compilation. Future versions of the compilation that include new datasets and JWST/NIRSpec and NIRCam grism redshifts will help fill in these gaps.
### 6.2.2. Low-z ($0.1 < z < 1$) Map

One obvious population that we are missing across all redshifts is that of low-mass, faint systems. This is not surprising, as observing such sources, especially with ground-based observatories, is difficult, and we have already shown how the spectroscopic completeness of the compilation drops significantly approaching $i \sim 26$ mag (see Section 5.1). However, other distinct populations are also missing from the compilation, for which we provide the (x, y) coordinates of the 2D maps as a reference. Figure 13 shows the low-z maps, where we find a population of $0.1 < z < 1$ galaxies with $10^8-10^9 M_\odot$ and sSFR $< 10^{-10} \text{ yr}^{-1}$, indicative of dwarf-like, quiescent populations ($x \sim 40, y \sim 45$). Although they are quite faint in $i$ ($\sim$25–26 mag), their $K_s$ magnitudes are quite bright within the range of 22–25 mag. The color panels also show red colors for $u-g, g-r$, and $r-i$, indicating a rising SED, followed by $\sim$0 mag colors at redder passbands, indicating a flat SED. The $u-g$ colors for a smaller subset of this region ($x \sim 42, y \sim 45$) suggest a bump in its SED at bluer wavelengths. On closer inspection, this is found to be due to photometric uncertainties near the $2\sigma$ detection limit in both the CFHT/$u$ and HSC/$g$ photometry.

Two other populations of low-mass, quiescent galaxies are noted at ($x \sim 15, y \sim 5$) and ($x \sim 75, y \sim 5–10$). The fact that these are organized in different regions by SOMs means that certain features were identified that made these populations separate from the main low-mass quiescent population at ($x \sim 40, y \sim 45$). Inspecting the color suggests a drop in J flux density for ($x \sim 15, y \sim 5$) and H flux density for ($x \sim 75, y \sim 5–10$), followed by bright redder passbands. These populations of low-mass, low-z quiescent galaxies could indicate populations that may be undergoing environmental quenching at $z < 1$ (e.g., Y.-j. Peng et al. 2010; B. Darvish et al. 2016; L. Kawinwanichakij et al. 2017; N. Chartab et al. 2020). Given their red colors, it is also important to investigate whether these sources are dusty systems mimicking the SEDs of quiescent galaxies. Identifying these populations via SOMs would allow the design of a potential survey program to target these regions based on their common features and ascertain the true nature of these sources.

Another population of quiescent galaxies missed within the compilation is found in the upper left corner of the map ($x < 5, y > 60$), which corresponds to $z < 0.5$ galaxies exhibiting sSFR $< 10^{-11} \text{ yr}^{-1}$ with stellar masses of $> 10^8 M_\odot$ and extending up to $10^{11} M_\odot$. This population has $u-g$ and $g-r$ colors similar to other quiescent populations that are spectroscopically observed; however, its $r-i$ colors are significantly bluer, suggesting a flattening in its SED occurring at bluer bandpasses.

Figure 13 also shows four distinct populations of high sSFR ($> 10^{-8} \text{ yr}^{-1}$) not spectroscopically observed. In fact, the second row clearly highlights how past spectroscopic follow-up programs in COSMOS have not significantly targeted sSFR $> 10^{-8} \text{ yr}^{-1}$ systems. The four main distinct regions are located at ($x \sim 10, y \sim 30$), ($x \sim 35, y \sim 5$), ($x \sim 65, y \sim 25$), and ($x \sim 75, y \sim 35$). The clumps have different redshift ranges with ($x \sim 10, y \sim 30$) and ($x \sim 35, y \sim 5$), consisting of $< 10^8 M_\odot$ and $z \sim 0.1–0.5$ populations. The other two clumps consist of $z > 0.7$ galaxies with stellar masses of $\sim 10^8-10^9 M_\odot$. The colors of each region are unique as well, where, for example, the ($x \sim 75, y \sim 35$) clump is strongly red in $i-z$ and blue in $z-y$ colors, indicative of a high-equivalent-width (EW) emission-line contribution in HSC $z$, which at $z \sim 0.8$

would correspond to [O III] $\lambda\lambda4959, 5007$. Clump ($x \sim 35, y \sim 5$) also shows strongly red $r-i$ and $z-y$ and blue $i-z$ colors, which at $z \sim 0.25$ corresponds to a high $H\alpha$ EW emission-line contribution in HSC $i$.

These high-sSFR clumps trace extreme emission-line galaxy (EELG) populations that have been studied in the past as local analogs of reionization-era galaxy populations (e.g., C. Cardamone et al. 2009; R. Amorín et al. 2015; H. Yang et al. 2017; Y. I. Izotov et al. 2021; A. A. Khostovan et al. 2025). However, such populations are also quite low in number density as indicated in past studies of emission-line EW distributions (e.g., M. Fumagalli et al. 2012; D. Sobral et al. 2014; A. L. Faisst et al. 2016; A. A. Khostovan et al. 2016, 2021, 2024), which may also explain the lack of spectroscopic coverage of these sources within COSMOS. Using SOMs coupled with the compilation, we can inform future survey strategies on how to identify distinct EELG populations for follow-up spectroscopic observations.

### 6.2.3. Mid-z ($1 < z < 3$) Map

As shown in Figure 14, past spectroscopic coverage is spread out throughout the map; however, there are still key gaps in spectroscopic follow-up programs of different mid-z galaxy populations. For example, we find a clear concentration of $1 < z < 2$ quiescent galaxies (low sSFR) with stellar mass $\sim 10^{9.5}-10^{11.4} M_\odot$ and rising SEDs as indicated by the color panels in the upper right portion of the trained SOM. The compilation does cover a portion of this population limited to $z \sim 1$ galaxies at primarily $> 10^{10.5} M_\odot$, but we are missing the higher-redshift quiescent galaxies and lower-mass populations that make up this full region. Such quiescent sources are ideal candidates to investigate massive galaxy growth and quenching mechanisms (N. Chartab et al. 2025). We do note that spectroscopic follow-up could also reveal that a fraction of this population are dusty, star-forming galaxies at low $z$ that mimic the SED shape of high-$z$ quiescent galaxies.

Other subpopulations include the regions at ($x < 40, y < 40$) that are not as well populated compared to other parts of the map. Populations residing here are $< 10^{9.5} M_\odot$ galaxies with sSFR ranging from normal ($\sim 10^{-9} \text{ yr}^{-1}$) to bursty ($\gtrsim 10^{-8} \text{ yr}^{-1}$) star-forming galaxies. As indicated in the even-numbered row highlighting the spec-$z$ compilation project on the trained SOM, there are several points that probe these regions but only tend to cover a single grid point such that the full subpopulation around the associated regions is not well probed. These key regions could give us insight into normal and bursty star-forming conditions within low-mass galaxies at the cosmic peak of star formation activity.

### 6.2.4. High-z ($z > 3$) Map

Figure 15 shows the high-$z$ SOM where the spec-$z$ projection on the map unsurprisingly highlights the lack of spectroscopic observations covering this period of cosmic time. This is especially evident in the upper right corner, which is populated by $z > 4$ galaxies, with the projection maps showing a clear lack of spectroscopic coverage. We note that this will quickly change as JWST NIRSpec and NIRCam WFSS programs publish redshifts, which will be later included in the compilation. The main reason for much of the empty space is due to the observationally faint nature of high-$z$ galaxy populations.

The COSMOS2020 SOM highlights key regions of massive ($> 10^{10.5} M_\odot$) galaxy populations at ($x \sim 30–40, y \sim 11–22$) and ($x \sim 20–25, y \sim 19–21$) that correspond to $3 < z < 5$ galaxies.
These systems are uniquely massive for their redshifts with sSFR ~10⁻⁹ yr⁻¹ and colors indicating a rising SED toward redder wavelengths, suggesting that these may be populations of dusty, massive star-forming galaxies. Spectroscopic confirmation is needed to validate the nature of these sources; however, this can provide a great opportunity to study both dust properties and rapid galaxy growth at relatively high z.

## 7. Future of the Compilation

This spectroscopic redshift compilation will be a continuously evolving data product maintained by the COSMOS Collaboration with legacy value for the astronomical community. The infrastructure is already set within our compilation pipeline as described in Section 3 for the inclusion of new public spectroscopic datasets as they are made available. The future includes exciting programs that will provide a wealth of spectroscopic redshifts.

Other programs expected to be part of the compilation in the near future include various JWST spectroscopic programs as data start to become public and redshifts are published. These datasets will heavily populate the high-z portion of the compilation and also extend the compilation to fainter populations. JWST/NIRCam WFSS slitless spectroscopy will also provide a wealth of redshifts to the compilation. One major slitless spectroscopy program is COSMOS-3D, which will cover ~0.33 deg² of COSMOS-Web and observe ~20,000 galaxies and ~5000 AGN, with ~4000 galaxies and ~500 AGN expected to be observed at z > 5 populating the high-z end of the compilation.

Future next-generation space-based missions and ground-based observatories will provide a wealth of redshifts that will be included in the compilation when made available. COSMOS is one of six Euclid Auxiliary Fields that will provide slitless grism spectroscopic coverage of the full 2 deg² field down to $2 \times 10^{-16} \text{ erg s}^{-1} \text{ cm}^{-2}$ (3.5σ; Euclid Collaboration et al. 2022b). Roman is expected to launch in 2027 May and will observe numerous emission-line galaxies with grism spectroscopy in a single 0.28 deg² pointing (e.g., F. Valentino et al. 2017; Z. Zhai et al. 2019; Y. Wang et al. 2022; A. A. Khostovan et al. 2024). The Subaru/PFS Galaxy Evolution Survey will measure ~2400 redshifts per 1.25 deg² field of view, with coverage from 0.38 to 1.26 µm, with COSMOS being one of the targeted fields (J. Greene et al. 2022). The VLT/MOONS Redshift-Intensive Survey Experiment (MOONRISE) will target 1 deg² of COSMOS as one of its three main fields, with ~1000 redshifts per 0.14 deg² field of view (R. Maiolino et al. 2020). WAVES/4MOST will observe COSMOS as one of its Deep Drilling Fields, covering ~4 deg² in a single pointing and observing 45,000 sources within 0.37–0.95 µm (S. P. Driver et al. 2019).

It is expected, as described above, that the compilation will grow significantly given the volume of data expected to be available in the near future. As such, the compilation will have periodic data releases made on a yearly basis and will be available via our GitHub repository, https://github.com/cosmosastro/speczcompilation, facilitating quick access to the latest catalogs.

## 8. Summary and Conclusions

We present the first data release of the COSMOS Spectroscopic Redshift Compilation, which represents ~20 yr worth of spectroscopic studies within this single legacy field. The

work not only encompasses observations and subsequent analysis by various groups but also reflects the many years of work gathering and compiling all published datasets into a single, concise, and uniform data product. Our main results are as follows:

(i) Redshifts from 138 programs were gathered via mining catalog database systems (e.g., IRSA, ESO Science Portal, VizieR), survey websites, publications, and direct contact with PIs. The compilation covers 10 deg² centered on COSMOS (2 deg² legacy field) in preparation for next-generation wide-area surveys. A total of 487,666 redshifts for 266,284 unique sources make up the total compilation across all assigned quality flags.

(ii) We designed a compilation pipeline that gathers all our identified datasets, corrects for varied astrometric calibrations on a program-by-program basis, converts quality assessment flags to our uniform system for each individual dataset, and compiles it all in a master catalog. This catalog is then used to identify and flag duplicates (e.g., sources that have multiple redshift measurements). The pipeline is also set up in such a way that new data can be efficiently appended to the compilation, allowing for periodic updates.

(iii) Included in the compilation are physical properties measured from CIGALE and LePHARE SED fitting using the best spectroscopic redshift per source and limited to the high-$Q_f$ subset.

(iv) The compilation is complete at the 50% level down to $i \sim 23.4$ mag and $K_s \sim 21.6$ mag and is most spectroscopically complete toward the central regions of COSMOS corresponding to the CANDELS region, which is expected given the number of spectroscopic programs covering this one key area.

(v) We find that the compilation has a median stellar mass that varies with redshift. At $z < 0.4$, the median stellar mass rises from $10^{8.4}$ to ~$10^{10} M_\odot$ by $z \sim 0.6$ and then decreases up to $z \sim 7$, reaching a median stellar mass of ~$10^{9.4} M_\odot$.

(vi) The stellar-mass distributions suggest a bimodal population of low- and intermediate-mass star-forming galaxies and massive, quiescent galaxies up to $z \sim 1.25$. Rest-frame *NUVrJ* colors also highlight both the quiescent and star-forming populations, as well as how the population progressively shifts toward the blue loci associated with dust-poor, young population-dominated star-forming galaxies at $z > 2$.

(vii) The SFR-stellar mass correlation shows how our compilation aligns well with past measurements, as well as the turnoff that occurs at $>10^{10} M_\odot$ associated with quenching populations. At $z > 3$, we find that the compilation shifts toward galaxies with high sSFR (>10 Gyr⁻¹) and 10-to-100 Myr SFR ratios >1, indicating recent and potentially bursty star formation activity.

(viii) The compilation covers a diverse range of environments where we demonstrate how the compilation can be used to visualize the multicomponent structure of large overdense regions such as “Hyperion” (three overdensities) and a $z \sim 1.25–1.26$ structure (two overdensities).

(ix) We demonstrate how the compilation can be used for validating photometric redshifts in galaxy survey
programs. We used several versions of the COSMOS catalogs and demonstrated how we can get further information by looking at photometric redshift calibration metrics (e.g., outlier fractions, $\sigma_{NMAD}$) as dependent on other properties such as HSC *i* magnitudes.

(x) We demonstrate how SOMs can be used to gain valuable insight into how well we are spectroscopically covering various galaxy subpopulations. Surprisingly, our coverage of the $z < 1$ population is still incomplete, as we are missing a population of intermediate-mass quiescent galaxies and high-sSFR systems. Other populations include $z \sim 2$ massive quiescent galaxies and $<10^{9.5} M_{\odot}$ high-sSFR systems. Overall, SOMs combined with our spec-z compilation can provide us with valuable insight into identifying which populations we require to get a complete spectroscopic view of galaxy populations, thereby informing future observing strategies.

The future of astronomy will be heavily dominated by the introduction of new and large data volumes coming from facilities such as Roman, Euclid, Subaru/PFS, VLT/MOONS, and many others. It is therefore of great importance to keep track of not only what new data are coming in but also what data we currently have on hand. Data products such as the COSMOS Spectroscopic Redshift Compilation are crucial legacy resources for the community that will be periodically updated to keep track of the latest redshift measurements.

## Acknowledgments

The authors thank the anonymous referee for their comments and suggestions that aided in enhancing the quality of this work. The authors acknowledge the hard work of all members of each respective program that have made this compilation possible. This material is based on work supported by the National Science Foundation under grant No. 2009572 and by NASA under award No. 80NSSC22K0854. The French contingent of the COSMOS team is partly supported by the Centre National d'Etudes Spatiales (CNES). O.I. acknowledges the funding of the French Agence Nationale de la Recherche for the project iMAGE (grant ANR-22-CE31-0007). This research has made use of the VizieR catalog access tool, CDS, Strasbourg, France (F. Ochsenbein et al. 2000). This research has made use of NASA's Astrophysics Data System.

Some of the data presented herein were obtained at Keck Observatory, which is a private 501(c)3 nonprofit organization operated as a scientific partnership among the California

Institute of Technology, the University of California, and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the Native Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain.

Based on observations obtained at the international Gemini Observatory, a program of NSF NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the US National Science Foundation on behalf of the Gemini Observatory partnership: the US National Science Foundation (United States), National Research Council (Canada), Agencia Nacional de Investigación y Desarrollo (Chile), Ministerio de Ciencia, Tecnología e Innovación (Argentina), Ministério da Ciência, Tecnologia, Inovações e Comunicações (Brazil), and Korea Astronomy and Space Science Institute (Republic of Korea).

*Facilities:* AAT, HST (ACS, WFC3-IR), ALMA, LMT, SMA, NOEMA, ING:Herschel (LIRIS), TNG (NICS), Keck:I (LRIS, MOSFIRE), Keck:II (DEIMOS, NIRSPEC), LBT (LUCI1, LUCI2), VLT:Antu (FORS1, FORS2, KMOS), VLT:Kueyen (X-SHOOTER), VLT:Melipal (VIMOS), VLT: Yepun (MUSE, SINFONI), MMT (Binospec, Hectospec), Mayall (DESI), Subaru (FMOS, FOCAS, MOIRCS), Gemini: South (GMOS), HET (VIRUS), Magellan:Baade (IMACS, FIRE), Magellan:Clay (LDSS3, M2FS), Spitzer, Gaia, Sloan, Du Pont

*Software:* astropy (Astropy Collaboration et al. 2013, 2018, 2022), CIGALE (M. Boquien et al. 2019), LePHARE (S. Arnouts et al. 1999; O. Ilbert et al. 2006), numpy (C. R. Harris et al. 2020), sompy (V. Moosavi et al. 2014).

## Appendix

### Column Definitions of Compilation Files

As described in Section 5, the compilation consists of two files, _all.fits and _unique.fits, with the former containing all redshift measurements and the latter being limited to the best redshift per galaxy (see Section 3.4). Both files are structured in the same way following the same column names, formats, and definitions as presented in detail in Table A1.
<table><caption>**Table A1**

Description of All Columns within the Spec-z Compilation FITS Files</caption><thead><tr><th>Column</th><th>Format</th><th>Units</th><th>Description</th></tr></thead><tbody><tr><td>Id_specz</td><td>long</td><td>...</td><td>Assigned unique identification in the compilation</td></tr><tr><td>IdOriginal</td><td>string</td><td>...</td><td>Original identification of the source</td></tr><tr><td>raOriginal</td><td>float</td><td>deg</td><td>Original R.A.</td></tr><tr><td>decOriginal</td><td>float</td><td>deg</td><td>Original decl.</td></tr><tr><td>raCorrected</td><td>float</td><td>deg</td><td>Astrometry-corrected R.A.</td></tr><tr><td>decCorrected</td><td>float</td><td>deg</td><td>Astrometry-corrected decl.</td></tr><tr><td>Priority</td><td>float</td><td>...</td><td>Flag 1: use this redshift, especially in case of duplicate sources</td></tr><tr><td>specz</td><td>float</td><td>...</td><td>Spectroscopic redshift</td></tr><tr><td>flag</td><td>long</td><td>...</td><td>Quality assessment flag (see Section 3.1)</td></tr><tr><td>Confidence_level</td><td>long</td><td>...</td><td>Confidence level (see Section 3.1)</td></tr><tr><td>survey</td><td>integer</td><td>...</td><td>Survey ID in _surveys.list and Table 1</td></tr><tr><td>compilation_year</td><td>long</td><td>...</td><td>Past versions of the compilation where these sources were introduced</td></tr><tr><td>Id_COS20_Classic</td><td>long</td><td>...</td><td>COSMOS2020 Classic identification</td></tr><tr><td>ra_COS20_Classic</td><td>float</td><td>deg</td><td>COSMOS2020 Classic R.A.</td></tr><tr><td>dec_COS20_Classic</td><td>float</td><td>deg</td><td>COSMOS2020 Classic decl.</td></tr><tr><td>Id_COS20_Farmer</td><td>long</td><td>...</td><td>COSMOS2020 Farmer identification</td></tr><tr><td>ra_COS20_Farmer</td><td>float</td><td>deg</td><td>COSMOS2020 Farmer R.A.</td></tr><tr><td>dec_COS20_Farmer</td><td>float</td><td>deg</td><td>COSMOS2020 Farmer decl.</td></tr><tr><td>Id_COSMOS25</td><td>long</td><td>...</td><td>COSMOS2025 identification</td></tr><tr><td>ra_COSMOS25</td><td>float</td><td>deg</td><td>COSMOS2025 R.A.</td></tr><tr><td>dec_COSMOS25</td><td>float</td><td>deg</td><td>COSMOS2025 decl.</td></tr><tr><td>Id_COSMOS15</td><td>long</td><td>...</td><td>COSMOS2015 identification</td></tr><tr><td>ra_COSMOS15</td><td>float</td><td>deg</td><td>COSMOS2015 R.A.</td></tr><tr><td>dec_COSMOS15</td><td>float</td><td>deg</td><td>COSMOS2015 decl.</td></tr><tr><td>Id_COSMOS09</td><td>long</td><td>...</td><td>COSMOS2009 i-band identification</td></tr><tr><td>ra_COSMOS09</td><td>float</td><td>deg</td><td>COSMOS2009 i-band R.A.</td></tr><tr><td>dec_COSMOS09</td><td>float</td><td>deg</td><td>COSMOS2009 i-band decl.</td></tr><tr><td>photoz</td><td>float</td><td>...</td><td>COSMOS2020 Classic photo-z LePhare</td></tr><tr><td>photoz_type</td><td>long</td><td>...</td><td>0: Galaxy; 1: star; 2: X-ray source; -9: failure in fit</td></tr><tr><td>GroupID</td><td>long</td><td>...</td><td>ID corresponding to groups of redshifts per unique source (no duplicates = -1)</td></tr><tr><td>GroupSize</td><td>long</td><td>...</td><td>Number of duplicate redshifts in a given group</td></tr></tbody></table>

ORCID iDs

Ali Ahmad Khostovan https://orcid.org/0000-0002-0101-336X

Jeyhan S. Kartaltepe https://orcid.org/0000-0001-9187-3605

Mara Salvato https://orcid.org/0000-0001-7116-9303

Olivier Ilbert https://orcid.org/0000-0002-7303-4397

Caitlin M. Casey https://orcid.org/0000-0002-0930-6466

Hiddo Algera https://orcid.org/0000-0002-4205-9567

Jacqueline Antwi-Danso https://orcid.org/0000-0002-0243-6575

Andrew Battisti https://orcid.org/0000-0003-4569-2285

Malte Brinch https://orcid.org/0000-0002-0245-6365

Marcella Brusa https://orcid.org/0000-0002-5059-6848

Antonello Calabrò https://orcid.org/0000-0003-2536-1614

Peter L. Capak https://orcid.org/0000-0003-3578-6843

Nima Chartab https://orcid.org/0000-0003-3691-937X

Olivia R. Cooper https://orcid.org/0000-0003-3881-1397

Isa G. Cox https://orcid.org/0000-0002-1803-794X

Behnam Darvish https://orcid.org/0000-0003-4919-9017

Nicole E. Drakos https://orcid.org/0000-0003-4761-2197

Andreas L. Faisst https://orcid.org/0000-0002-9382-9832

Ghassem Gozaliasl https://orcid.org/0000-0002-0236-919X

Santosh Harish https://orcid.org/0000-0003-0129-2079

Günther Hasinger https://orcid.org/0000-0002-0797-0646

Hossein Hatamnia https://orcid.org/0009-0007-3673-4523

Angela Iovino https://orcid.org/0000-0001-6958-0304

Shuowen Jin https://orcid.org/0000-0002-8412-7951

Daichi Kashino https://orcid.org/0000-0001-9044-1747

Anton M. Koekemoer https://orcid.org/0000-0002-6610-2048

Ronaldo Laishram https://orcid.org/0000-0002-0322-6131

Khee-Gan Lee https://orcid.org/0000-0001-9299-5719

Jitrapon Lertprasertpong https://orcid.org/0000-0002-3535-4066

Simon J. Lilly https://orcid.org/0000-0002-6423-3597

Daizhong Liu https://orcid.org/0000-0001-9773-7479

Daniel C. Masters https://orcid.org/0000-0001-5382-6138

Bahram Mobasher https://orcid.org/0000-0001-5846-4404

Tohru Nagao https://orcid.org/0000-0002-7402-5441

Masato Onodera https://orcid.org/0000-0003-3228-7264

Yingjie Peng https://orcid.org/0000-0003-0939-9671

David B. Sanders https://orcid.org/0000-0002-1233-9998

Ryan L. Sanders https://orcid.org/0000-0003-4792-9119

Zahra Sattari https://orcid.org/0000-0002-0364-1159

Nick Scoville https://orcid.org/0000-0002-0438-3323

Ekta A. Shah https://orcid.org/0000-0001-7811-9042

John D. Silverman https://orcid.org/0000-0002-0000-6977

Nao Suzuki https://orcid.org/0000-0001-7266-930X

Sina Taamoli https://orcid.org/0000-0003-0749-4667

Masayuki Tanaka https://orcid.org/0000-0002-5011-5178

Sune Toft https://orcid.org/0000-0003-3631-7176

Greta Toni https://orcid.org/0009-0005-3133-1157
Benny Trakhtenbrot https://orcid.org/0000-0002-3683-7297

Jonathan R. Trump https://orcid.org/0000-0002-1410-0470

Mattia Vaccari https://orcid.org/0000-0002-6748-0577

Francesco Valentino https://orcid.org/0000-0001-6477-4011

Brittany N. Vanderhoof https://orcid.org/0000-0002-8163-0172

John R. Weaver https://orcid.org/0000-0003-1614-196X

Min S. Yun https://orcid.org/0000-0001-7095-7543

Jorge A. Zavala https://orcid.org/0000-0002-7051-1100

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