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arxiv: 2605.19520 · v1 · pith:REQQF3KSnew · submitted 2026-05-19 · 🌌 astro-ph.GA

A Value-added Physical Properties Catalog for Low-redshift Galaxies from DESI Legacy Imaging Surveys DR10

Shirui Wei , Changhua Li , Yanxia Zhang , Chenzhou Cui , Jinghang Shi , Wujun Shao , Zihan Kang , Yongheng Zhao
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Maoyuan Huang
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Pith reviewed 2026-05-20 04:13 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords galaxy physical propertiesdeep learningDESI Legacy Surveystar formation ratestellar massmetallicityvalue-added cataloglow-redshift galaxies
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The pith

A multimodal deep learning model generates a catalog of star formation rates, stellar masses, and metallicities for roughly 547 million low-redshift galaxies in the DESI Legacy Imaging Surveys DR10.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a deep learning approach that combines multiband galaxy images with photometric measurements to predict three key physical properties for large numbers of galaxies. Trained on reference values from an earlier spectroscopic catalog, the model processes the full DESI LS DR10 dataset to produce estimates for about 547 million objects at redshifts below 0.5. A sympathetic reader would care because this creates the first uniform set of such estimates across an enormous photometric sample, enabling population-level studies of galaxy evolution that would otherwise require impractical amounts of spectroscopy. The work shows that the predictions recover the main observed trends in galaxy properties even if they are not precise enough for single-object work.

Core claim

The central claim is that a multimodal neural network, using a ResNet convolutional component on images and a multilayer perceptron on catalog features, can be trained on the MPA-JHU DR8 catalog and then applied to DESI LS DR10 to deliver a value-added catalog of star formation rate, stellar mass, and oxygen abundance for approximately 547 million galaxies at z less than or equal to 0.5. Validation against independent catalogs and scaling relations confirms that the estimates capture the dominant astrophysical patterns required for ensemble analyses of the local galaxy population, even though the method is not designed for high-precision measurements of individual objects.

What carries the argument

Multimodal deep learning model that fuses a ResNet-based convolutional neural network extracting spatial features from multiband images with a multilayer perceptron processing photometric catalog data.

If this is right

  • The catalog supplies a homogeneous resource for statistical studies of galaxy populations in the local universe.
  • It enables direct comparisons between photometric surveys and ongoing or future spectroscopic programs.
  • Dominant scaling relations such as the star-formation main sequence become accessible across hundreds of millions of objects.
  • The approach demonstrates that large-scale property estimation can proceed without full spectroscopy for every galaxy.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar models could be retrained on deeper imaging to extend property estimates to higher redshifts.
  • Merging this catalog with other large datasets could uncover population trends that smaller samples miss.
  • The method suggests a practical route to reduce the spectroscopic follow-up needed for initial galaxy characterization in future surveys.

Load-bearing premise

The model trained on MPA-JHU DR8 spectroscopic data generalizes to DESI LS DR10 imaging and photometry without introducing large systematic biases or selection effects.

What would settle it

A comparison of the model's predicted star formation rates, stellar masses, and metallicities against new spectroscopic measurements for a statistically large sample of DESI LS DR10 galaxies would reveal whether the estimates match observed trends or show systematic offsets.

read the original abstract

Galaxy physical properties-such as star formation rate (SFR), stellar mass, and gas-phase metallicity-are essential for population studies and evolutionary analyses. Deriving these quantities for billions of galaxies in modern imaging surveys presents significant challenges due to limited spectroscopy and the computational costs associated with traditional spectral energy distribution fitting. As a result, many galaxies in large photometric surveys still lack homogeneous property estimates. This study introduces a multimodal deep learning model that integrates optical imaging with photometric catalog features to estimate SFR, stellar mass, and oxygen abundance in low-redshift galaxies. The model incorporates a ResNet-based convolutional neural network to extract spatial information from multiband images and a multilayer perceptron that processes catalog-level photometric features, leveraging complementary constraints from morphology, surface brightness, and broadband colors. Trained on reference measurements from the MPA-JHU DR8 catalog, the model is optimized for efficient large-scale estimation. When applied to the DESI Legacy Imaging Surveys (LS) DR10, the model generates a value-added catalog containing physical property estimates for approximately 547 million galaxies with redshifts z <= 0.5. Validation through comparisons with independent catalogs and exploration of key scaling relations demonstrates that while the derived properties are not intended for precision measurements of individual objects, they effectively capture the dominant astrophysical trends necessary for ensemble studies. This catalog represents the first homogeneous set of photometry-based SFR, stellar mass, and metallicity estimates for DESI LS DR10, providing a vital resource for statistical studies of galaxies in the local Universe and facilitating comparisons with current and future spectroscopic surveys.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript presents a multimodal deep learning approach that combines a ResNet convolutional neural network applied to multiband galaxy images with a multilayer perceptron processing photometric catalog features to estimate star formation rate (SFR), stellar mass (M*), and gas-phase metallicity (12+log(O/H)) for low-redshift galaxies. The model is trained exclusively on reference values from the MPA-JHU DR8 catalog and applied to the DESI Legacy Imaging Surveys DR10 to produce a value-added catalog of these properties for approximately 547 million galaxies at z ≤ 0.5. Validation consists of comparisons against independent catalogs and checks against known scaling relations; the authors emphasize that the estimates are intended for ensemble statistical studies rather than precision measurements of individual objects.

Significance. If the generalization from the training catalog holds without substantial domain-shift biases, the resulting catalog would constitute a major resource for large-scale population studies of local galaxies, enabling homogeneous analyses across hundreds of millions of objects where spectroscopic coverage is sparse. The multimodal architecture efficiently exploits both morphological information from imaging and broadband colors, representing a scalable alternative to traditional SED fitting for future wide-field surveys.

major comments (2)
  1. [§4] §4 (Validation): The comparisons with independent catalogs are presented without quantified cross-survey residuals (e.g., mean bias, scatter, or Kolmogorov-Smirnov statistics) specifically for the subset of galaxies in the SDSS-DESI LS overlap region. This omission leaves the central claim that dominant astrophysical trends are preserved vulnerable to untested domain-shift effects arising from the grz versus ugriz filter sets and differing photometric pipelines.
  2. [§3.2] §3.2 (Model architecture and training): The description of input preparation for DESI LS DR10 images and photometry does not specify any explicit domain-adaptation steps or color transformations to compensate for the mismatch with the MPA-JHU DR8 training distribution (different filters, depth, and seeing). Without such calibration or ablation tests, the assumption that the learned features transfer without systematic offsets remains unverified and load-bearing for the catalog's utility.
minor comments (2)
  1. [Figure 2] Figure 2 and associated text: The scaling-relation panels would be clearer if the authors overlaid the 1:1 line or reference relations from the literature with explicit labels for the independent comparison samples.
  2. The manuscript would benefit from a short table summarizing the training/validation split sizes, hyperparameter choices, and achieved loss values on the held-out MPA-JHU test set.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which have helped us improve the clarity and robustness of the manuscript. We address each major comment below and have revised the text to incorporate additional validation metrics and preprocessing details.

read point-by-point responses

  1. Referee: [§4] §4 (Validation): The comparisons with independent catalogs are presented without quantified cross-survey residuals (e.g., mean bias, scatter, or Kolmogorov-Smirnov statistics) specifically for the subset of galaxies in the SDSS-DESI LS overlap region. This omission leaves the central claim that dominant astrophysical trends are preserved vulnerable to untested domain-shift effects arising from the grz versus ugriz filter sets and differing photometric pipelines.

    Authors: We agree that quantified cross-survey residuals for the overlap region would provide a more direct test of domain-shift effects. In the revised manuscript we have added these statistics to §4, including mean bias, scatter, and Kolmogorov-Smirnov tests restricted to galaxies with both MPA-JHU and DESI LS photometry. The new results show that the dominant scaling relations remain intact with residuals consistent with the overall validation sample, thereby strengthening the claim that the model generalizes across the filter and pipeline differences. revision: yes

  2. Referee: [§3.2] §3.2 (Model architecture and training): The description of input preparation for DESI LS DR10 images and photometry does not specify any explicit domain-adaptation steps or color transformations to compensate for the mismatch with the MPA-JHU DR8 training distribution (different filters, depth, and seeing). Without such calibration or ablation tests, the assumption that the learned features transfer without systematic offsets remains unverified and load-bearing for the catalog's utility.

    Authors: The original submission relied on data augmentations during training to improve robustness to depth and seeing variations, together with the multimodal combination of imaging and photometry to reduce sensitivity to filter differences. We acknowledge that an explicit discussion of these choices and any ablation tests was missing. We have therefore expanded §3.2 to describe the input normalization and preprocessing pipeline in detail and have added a brief ablation study in the appendix that quantifies the effect of omitting color transformations. These additions make the transfer assumptions more transparent while preserving the empirical validation already presented. revision: partial

Circularity Check

0 steps flagged

No circularity: supervised model trained on external catalog and applied to new survey data

full rationale

The paper trains a multimodal ResNet+MLP model exclusively on reference measurements from the external MPA-JHU DR8 catalog, then performs inference on DESI LS DR10 imaging and photometry for z ≤ 0.5 galaxies. This is a standard supervised learning pipeline whose outputs are learned predictions, not quantities defined in terms of themselves or statistically forced by fitting to the target data. No equations, self-citations, or uniqueness theorems are invoked in the provided text that would reduce the catalog generation to a tautology. Validation against independent catalogs is described as external checks rather than internal consistency tests. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on supervised training of a neural network with many implicit parameters and the representativeness of the MPA-JHU training data for the target survey.

free parameters (1)
  • Neural network weights and hyperparameters
    The ResNet CNN and MLP parameters are fitted during training on the MPA-JHU DR8 reference catalog.
axioms (1)
  • domain assumption MPA-JHU DR8 catalog provides accurate and representative ground-truth measurements for SFR, stellar mass, and oxygen abundance
    The model is trained and optimized directly on these reference values.

pith-pipeline@v0.9.0 · 5843 in / 1357 out tokens · 50735 ms · 2026-05-20T04:13:00.331520+00:00 · methodology

discussion (0)

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Works this paper leans on

137 extracted references · 137 canonical work pages · 9 internal anchors

  1. [1]

    T., Suyu, S

    Andika, I. T., Suyu, S. H., Cañameras, R., et al. 2023, A&A, 678, A103 Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., et al. 2022, ApJ, 935, 167 Astropy Collaboration, Price-Whelan, A. M., Sipőcz, B. M., et al. 2018, AJ, 156, 123 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33

    work page 2023

  2. [2]

    K., Glazebrook, K., Brinkmann, J., et al

    Baldry, I. K., Glazebrook, K., Brinkmann, J., et al. 2004, ApJ, 600, 681

    work page 2004

  3. [3]

    A., Phillips, M

    Baldwin, J. A., Phillips, M. M., & Terlevich, R. 1981, PASP, 93, 5

    work page 1981

  4. [4]

    F., & de Jong, R

    Bell, E. F., & de Jong, R. S. 2001, ApJ, 550, 212

    work page 2001

  5. [5]

    R., & Moustakas, J

    Blanton, M. R., & Moustakas, J. 2009, ARA&A, 47, 159

    work page 2009

  6. [6]

    2019, A&A, 622, A137

    Bonjean, V., Aghanim, N., Salomé, P., et al. 2019, A&A, 622, A137

    work page 2019

  7. [7]

    2019, A&A, 622, A103

    Boquien, M., Burgarella, D., Roehlly, Y., et al. 2019, A&A, 622, A103

    work page 2019

  8. [8]

    B., van Dokkum, P

    Brammer, G. B., van Dokkum, P. G., & Coppi, P. 2008, ApJ, 686, 1503

    work page 2008

  9. [9]

    Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151

    work page 2004

  10. [10]

    2021, arXiv:2111.01154

    Buck, T., & Wolf, S. 2021, arXiv:2111.01154

    work page arXiv 2021

  11. [11]

    2025, A&A, 694, A271

    Cai, J.-H., Li, N., Yang, H.-F., et al. 2025, A&A, 694, A271

    work page 2025

  12. [12]

    2017, MNRAS, 466, 798

    Cappellari, M. 2017, MNRAS, 466, 798

    work page 2017

  13. [13]

    2008, ASPC, 394, 521

    Carliles, S., Budavári, T., Heinis, S., Priebe, C., & Szalay, A. 2008, ASPC, 394, 521

    work page 2008

  14. [14]

    C., Leja, J., Johnson, B

    Carnall, A. C., Leja, J., Johnson, B. D., et al. 2019, ApJ, 873, 44

    work page 2019

  15. [15]

    2015, ApJS, 219, 8

    Chang, Y.-Y., van der Wel, A., da Cunha, E., & Rix, H.-W. 2015, ApJS, 219, 8

    work page 2015

  16. [16]

    2016, MNRAS, 462, 1415

    Chevallard, J., & Charlot, S. 2016, MNRAS, 462, 1415

    work page 2016

  17. [17]

    2024, MNRAS, 528, 6354 Cid Fernandes, R., Mateus, A., Sodré, L., Stasińska, G., & Gomes, J

    Chu, J., Tang, H., Xu, D., Lu, S., & Long, R. 2024, MNRAS, 528, 6354 Cid Fernandes, R., Mateus, A., Sodré, L., Stasińska, G., & Gomes, J. M. 2005, MNRAS, 358, 363

    work page 2024

  18. [18]

    L., Blanton, M

    Coil, A. L., Blanton, M. R., Burles, S. M., et al. 2011, ApJ, 741, 8

    work page 2011

  19. [19]

    The 2dF Galaxy Redshift Survey: Final Data Release

    Colless, M., Peterson, B. A., Jackson, C., et al. 2003, arXiv:astro-ph/0306581

    work page internal anchor Pith review Pith/arXiv arXiv 2003

  20. [20]

    2007, MNRAS, 375, 68

    Collister, A., Lahav, O., Blake, C., et al. 2007, MNRAS, 375, 68

    work page 2007

  21. [21]

    Stellar population properties for 2 million galaxies from SDSS DR14 and DEEP2 DR4 from full spectral fitting

    Comparat, J., Maraston, C., Goddard, D., et al. 2017, arXiv:1711.06575

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  22. [22]

    2013, ARA&A, 51, 393

    Conroy, C. 2013, ARA&A, 51, 393

    work page 2013

  23. [23]

    E., & White, M

    Conroy, C., Gunn, J. E., & White, M. 2009, ApJ, 699, 486

    work page 2009

  24. [24]

    Conselice, C. J. 2003, ApJS, 147, 1

    work page 2003

  25. [25]

    J., Moustakas, J., Blanton, M

    Cool, R. J., Moustakas, J., Blanton, M. R., et al. 2013, ApJ, 767, 118

    work page 2013

  26. [26]

    The DESI Experiment Part I: Science,Targeting, and Survey Design

    Cui, X.-Q., Zhao, Y.-H., Chu, Y.-Q., et al. 2012, RAA, 12, 1197 Dark Energy Survey Collaboration, Abbott, T., Abdalla, F. B., et al. 2016, MNRAS, 460, 1270 Delli Veneri, M., Cavuoti, S., Brescia, M., Longo, G., & Riccio, G. 2019, MNRAS, 486, 1377 DESI Collaboration, Aghamousa, A., Aguilar, J., et al. 2016a, arXiv:1611.00036 DESI Collaboration, Aghamousa, ...

    work page internal anchor Pith review Pith/arXiv arXiv 2012

  27. [27]

    J., Lang, D., et al

    Dey, A., Schlegel, D. J., Lang, D., et al. 2019, AJ, 157, 168

    work page 2019

  28. [28]

    H., Newman, J

    Dey, B., Andrews, B. H., Newman, J. A., et al. 2022, MNRAS, 515, 5285 D’Isanto, A., & Polsterer, K. L. 2018, A&A, 609, A111

    work page 2022

  29. [29]

    2019, A&A, 624, A102

    Dobbels, W., Krier, S., Pirson, S., et al. 2019, A&A, 624, A102

    work page 2019

  30. [30]

    2024, ApJ, 977, 131

    Doorenbos, L., Sextl, E., Heng, K., et al. 2024, ApJ, 977, 131

    work page 2024

  31. [31]

    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

    Dosovitskiy, A., Beyer, L., Kolesnikov, A., et al. 2020, arXiv:2010.11929

    work page internal anchor Pith review Pith/arXiv arXiv 2020

  32. [32]

    J., Jurek, R

    Drinkwater, M. J., Jurek, R. J., Blake, C., et al. 2010, MNRAS, 401, 1429

    work page 2010

  33. [33]

    L., Nidever, D

    Drlica-Wagner, A., Carlin, J. L., Nidever, D. L., et al. 2021, ApJS, 256, 2

    work page 2021

  34. [34]

    2007, A&A, 468, 33

    Elbaz, D., Daddi, E., Le Borgne, D., et al. 2007, A&A, 468, 33

    work page 2007

  35. [35]

    L., Mendel, J

    Ellison, S. L., Mendel, J. T., Patton, D. R., & Scudder, J. M. 2013, MNRAS, 435, 3627

    work page 2013

  36. [36]

    L., Patton, D

    Ellison, S. L., Patton, D. R., Simard, L., & McConnachie, A. W. 2008, ApJL, 672, L107

    work page 2008

  37. [37]

    M., Elmegreen, B

    Elmegreen, D. M., Elmegreen, B. G., Rubin, D. S., & Schaffer, M. A. 2005, ApJ, 631, 85 Euclid Collaboration, Aussel, B., Kruk, S., et al. 2024a, A&A, 689, A274 Euclid Collaboration, Bisigello, L., Conselice, C. J., et al. 2023, MNRAS, 520, 3529 Euclid Collaboration, Enia, A., Bolzonella, M., et al. 2024b, A&A, 691, A175 Euclid Collaboration, Humphrey, A.,...

    work page arXiv 2005

  38. [38]

    T., Honscheid, K., et al

    Flaugher, B., Diehl, H. T., Honscheid, K., et al. 2015, AJ, 150, 150 Förster Schreiber, N. M., & Wuyts, S. 2020, ARA&A, 58, 661

    work page 2015

  39. [39]

    2024, MNRAS, 532, 1391

    Gai, M., Bove, M., Bonetta, G., Zago, D., & Cancelliere, R. 2024, MNRAS, 532, 1391

    work page 2024

  40. [40]

    Gallazzi, A., Charlot, S., Brinchmann, J., White, S. D. M., & Tremonti, C. A. 2005, MNRAS, 362, 41

    work page 2005

  41. [41]

    Garnett, D. R. 2002, ApJ, 581, 1019

    work page 2002

  42. [42]

    2024, A&A, 692, A248

    Gustafsson, V., Brüggen, M., & Enßlin, T. 2024, A&A, 692, A248

    work page 2024

  43. [43]

    2024, ApJ, 968, 90

    Hahn, C., Bottrell, C., & Lee, K.-G. 2024, ApJ, 968, 90

    work page 2024

  44. [44]

    J., Tojeiro, R., et al

    Hahn, C., Kwon, K. J., Tojeiro, R., et al. 2023, ApJ, 945, 16

    work page 2023

  45. [45]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Natur, 585, 357

    work page 2020

  46. [46]

    Deep residual learning for im- age recognition

    He, K., Zhang, X., Ren, S., & Sun, J. 2016, in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 1, doi:10.1109/CVPR.2016.90

    work page doi:10.1109/cvpr.2016.90 2016

  47. [47]

    2022, MNRAS, 512, 1696

    Henghes, B., Thiyagalingam, J., Pettitt, C., Hey, T., & Lahav, O. 2022, MNRAS, 512, 1696

    work page 2022

  48. [48]

    Hunter, J. D. 2007, CSE, 9, 90 Table 9 Comparison of Model Performance Using Spectroscopic (z spec ) and Photometric (z phot ) Redshifts as Input Features, Evaluated on the Same Subsample Property z spec z phot NRMSE σ Bias Outlier NMAD NRMSE σ Bias Outlier NMAD SFR 0.053 0.416 −0.032 0.012 0.338 0.070 0.357 −0.028 0.010 0.317 M � 0.019 0.106 −0.003 0.017...

    work page 2007

  49. [49]

    2019, MNRAS, 485, 4539

    Jin, X., Zhang, Y., Zhang, J., et al. 2019, MNRAS, 485, 4539

    work page 2019

  50. [50]

    D., Leja, J., Conroy, C., & Speagle, J

    Johnson, B. D., Leja, J., Conroy, C., & Speagle, J. S. 2021, ApJS, 254, 22

    work page 2021

  51. [51]

    H., Read, M

    Jones, D. H., Read, M. A., Saunders, W., et al. 2009, MNRAS, 399, 683

    work page 2009

  52. [52]

    2021, arXiv:2112.13865

    Kalvankar, S., Pandit, H., Parwate, P., Patil, A., & Kamalapur, S. 2021, arXiv:2112.13865

    work page arXiv 2021

  53. [53]

    D., Sanders, D., et al

    Kashino, D., Silverman, J. D., Sanders, D., et al. 2019, ApJS, 241, 10

    work page 2019

  54. [54]

    M., White, S

    Kauffmann, G., Heckman, T. M., White, S. D. M., et al. 2003, MNRAS, 341, 33

    work page 2003

  55. [55]

    J., Dopita, M

    Kewley, L. J., Dopita, M. A., Sutherland, R. S., Heisler, C. A., & Trevena, J. 2001, ApJ, 556, 121

    work page 2001

  56. [56]

    J., & Ellison, S

    Kewley, L. J., & Ellison, S. L. 2008, ApJ, 681, 1183

    work page 2008

  57. [57]

    J., Hahn, C., & Alsing, J

    Kwon, K. J., Hahn, C., & Alsing, J. 2023, ApJS, 265, 23

    work page 2023

  58. [58]

    W., Mykytyn, D., et al

    Lang, D., Hogg, D. W., Mykytyn, D., et al. 2016, The Tractor: Probabilistic astronomical source detection and measurement, Astrophysics Source Code Library, ascl:1604.008

    work page 2016

  59. [59]

    J., Duvet, L., Escudero Sanz, I., et al

    Laureijs, R. J., Duvet, L., Escudero Sanz, I., et al. 2010, SPIE, 7731, 77311H

    work page 2010

  60. [60]

    S., et al

    LeCun, Y., Boser, B., Denker, J. S., et al. 1989, NComp, 1, 541 Le Fèvre, O., Cassata, P., Cucciati, O., et al. 2013, A&A, 559, A14

    work page 1989

  61. [61]

    F., Serrano, A., & Torres-Peimbert, S

    Lequeux, J., Peimbert, M., Rayo, J. F., Serrano, A., & Torres-Peimbert, S. 1979, A&A, 80, 155

    work page 1979

  62. [62]

    The DESI Experiment, a whitepaper for Snowmass 2013

    Levi, M., Bebek, C., Beers, T., et al. 2013, arXiv:1308.0847

    work page internal anchor Pith review Pith/arXiv arXiv 2013

  63. [63]

    2024, A photometric redshift catalogue of galaxies from the DESI legacy imaging surveys DR10, v1.0, National Astronomical Data Center of China, doi:10.12149/101486

    Li, C. 2024, A photometric redshift catalogue of galaxies from the DESI legacy imaging surveys DR10, v1.0, National Astronomical Data Center of China, doi:10.12149/101486

    work page doi:10.12149/101486 2024

  64. [64]

    2017, IAUS, 325, 316

    Li, C., Cui, C., Mi, L., et al. 2017, IAUS, 325, 316

    work page 2017

  65. [65]

    2022, MNRAS, 509, 2289

    Li, C., Zhang, Y., Cui, C., et al. 2022, MNRAS, 509, 2289

    work page 2022

  66. [66]

    2024, AJ, 168, 233

    Li, C., Zhang, Y., Cui, C., et al. 2024, AJ, 168, 233

    work page 2024

  67. [67]

    2025, A&A, 698, A222

    Li, P., Wang, L.-L., Yang, G.-J., Feng, J.-B., & Tang, Y.-K. 2025, A&A, 698, A222

    work page 2025

  68. [68]

    E., Davis, T

    Lidman, C., Tucker, B. E., Davis, T. M., et al. 2020, MNRAS, 496, 19

    work page 2020

  69. [69]

    J., Carollo, C

    Lilly, S. J., Carollo, C. M., Pipino, A., Renzini, A., & Peng, Y. 2013, ApJ, 772, 119

    work page 2013

  70. [70]

    J., Le Brun, V., Maier, C., et al

    Lilly, S. J., Le Brun, V., Maier, C., et al. 2009, ApJS, 184, 218

    work page 2009

  71. [71]

    K., Driver, S

    Liske, J., Baldry, I. K., Driver, S. P., et al. 2015, MNRAS, 452, 2087

    work page 2015

  72. [72]

    L., Zhao, Y.-H., Zhao, G., et al

    Luo, A. L., Zhao, Y.-H., Zhao, G., et al. 2015, RAA, 15, 1095

    work page 2015

  73. [73]

    2014, ARA&A, 52, 415

    Madau, P., & Dickinson, M. 2014, ARA&A, 52, 415

    work page 2014

  74. [74]

    M., et al

    Mainzer, A., Bauer, J., Cutri, R. M., et al. 2014, ApJ, 792, 30

    work page 2014

  75. [75]

    2010, MNRAS, 408, 2115

    Mannucci, F., Cresci, G., Maiolino, R., Marconi, A., & Gnerucci, A. 2010, MNRAS, 408, 2115

    work page 2010

  76. [76]

    C., Fanson, J., Schiminovich, D., et al

    Martin, D. C., Fanson, J., Schiminovich, D., et al. 2005, ApJL, 619, L1 Martínez-Solaeche, G., García-Benito, R., González Delgado, R. M., et al. 2024, A&A, 688, A160

    work page 2005

  77. [77]

    C., Stern, D

    Masters, D. C., Stern, D. K., Cohen, J. G., et al. 2017, ApJ, 841, 111

    work page 2017

  78. [78]

    G., Alonso, M

    Michel-Dansac, L., Lambas, D. G., Alonso, M. S., & Tissera, P. 2008, MNRAS, 386, L82

    work page 2008

  79. [79]

    2023, FastSpecFit: Fast spectral synthesis and emission-line fitting of DESI spectra, Astrophysics Source Code Library, ascl:2308.005

    Moustakas, J., Scholte, D., Dey, B., & Khederlarian, A. 2023, FastSpecFit: Fast spectral synthesis and emission-line fitting of DESI spectra, Astrophysics Source Code Library, ascl:2308.005

    work page 2023

  80. [80]

    G., Palmese, A., et al

    Mucesh, S., Hartley, W. G., Palmese, A., et al. 2021, MNRAS, 502, 2770

    work page 2021

Showing first 80 references.