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arxiv: 2605.01353 · v1 · submitted 2026-05-02 · 🌌 astro-ph.GA · astro-ph.CO

Cross-Comparison of Galaxies Detected in the CSST Spectroscopic Survey and the SKA HI Survey

Yingfeng Liu , Furen Deng , Wenxiang Pei , Haitao Miao , Qi Xiong , Shuanghao Shu , Xingchen Zhou , Qi Guo
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Yan Gong Yougang Wang Xuelei Chen
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Pith reviewed 2026-05-09 18:16 UTC · model grok-4.3

classification 🌌 astro-ph.GA astro-ph.CO
keywords galaxy surveysHI contentemission line galaxiesforward modelingcross-correlationCSSTSKAsemi-analytic models
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The pith

A forward-modeling framework generates matched mock catalogs to cross-compare galaxies detected by the CSST spectroscopic survey and the SKA HI survey.

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

The paper develops a simulation pipeline that starts from a semi-analytic galaxy formation model and produces mock observations for two upcoming surveys. It applies selection functions and noise models to create catalogs of emission-line galaxies for CSST and HI galaxies for SKA. This setup allows the authors to study how the same galaxies appear in optical emission lines versus neutral hydrogen signals, and to measure their correlations with mass and star formation. Such cross-comparisons matter because they test how well we understand the link between star formation and cold gas in galaxies over cosmic time. If successful, it provides a way to interpret joint data from these facilities before they fully operate.

Core claim

The authors construct mock lightcones from a semi-analytic galaxy formation model on an N-body simulation, partition cold gas into atomic and molecular components, post-process emission lines for the CSST spectrograph, simulate HI data cubes for the SKA with a source-finding package, and apply the CSST selection function to emission-line galaxies. This produces catalogs that permit cross-comparison, including correlations of HI and emission-line signals with halo, HI, and stellar mass, the baryonic Tully-Fisher relation, stacking of HI from CSST-selected galaxies, and the optical-HI cross-correlation power spectrum with galaxy bias.

What carries the argument

The forward-modeling framework that combines a semi-analytic galaxy formation model with post-processed emission lines, lightcone construction, and SKA-specific source finding to generate matched mock catalogs for both surveys.

Load-bearing premise

The semi-analytic galaxy formation model with its gas partitioning and the applied survey selection functions and noise models together produce mock catalogs that statistically match what the real CSST and SKA surveys will observe.

What would settle it

When actual CSST and SKA observations become available, comparing the predicted number of overlapping galaxies, the measured cross-correlation amplitude, or the stacked HI signals against the real data would test if the model holds.

read the original abstract

We present a forward-modeling framework to forecast the galaxies detected in the Chinese Space Station Survey Telescope (CSST) spectroscopic survey and the Square Kilometre Array (SKA) HI survey. Starting from the L-Galaxies 2020 semi-analytic model run on the Millennium-II N-body simulation (MS-II), the cold gas in galaxies is partitioned into atomic and molecular components self-consistently within the model. We further model the emission-lines (H $\alpha$, H $\beta$, O III) relevant for the slitless spectrograph of the CSST in a post-processing step. We construct mock lightcones using the Mock Map Facility (MoMaF) approach, simulating the neutral hydrogen (HI) data cubes representing a 2000 hour SKA-Mid spectral line observation from redshifts 0.25--0.5, and employ the Source Finding Application 2(SOFIA-2) source-finding package to generate an HI galaxy catalog. In parallel, we apply the CSST selection function and noise model to obtain a realistic catalog of emission-line galaxies; the emission-line signal is proportional to the star formation rate. These products allow us to cross compare the galaxy samples and assess the synergy between CSST and SKA. We study the correlations of the HI and the emission-line signal with the halo mass, HI mass, and the stellar mass, and the baryonic Tully-Fisher relation (BTFR). We also perform stacking analysis of the HI signal from the CSST-selected sample, which probes the HI content in galaxies with low HI mass. Finally, we derive the optical-HI cross-correlation power spectrum of the galaxies, and measure the bias of these galaxies. These results can provide useful insight on the cold gas and stellar content of the galaxies.

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

0 major / 5 minor

Summary. The paper presents a forward-modeling framework to forecast galaxies detected in the CSST spectroscopic survey and the SKA HI survey. Starting from the L-Galaxies 2020 semi-analytic model on the Millennium-II N-body simulation, cold gas is partitioned self-consistently into atomic and molecular components. Emission lines (Hα, Hβ, [O III]) are modeled in post-processing with luminosities proportional to star formation rate. Mock lightcones are constructed via MoMaF; SKA-Mid HI data cubes for a 2000-hour observation at 0.25 < z < 0.5 are generated and processed with SOFIA-2 source finding. CSST selection functions and noise models are applied to produce an emission-line galaxy catalog. The resulting mocks are used to examine correlations of HI and emission-line signals with halo mass, HI mass and stellar mass, the baryonic Tully-Fisher relation, HI stacking on the CSST-selected sample, and the optical-HI cross-correlation power spectrum together with the associated bias.

Significance. If the modeling assumptions hold, the work supplies a practical forecasting pipeline that quantifies the overlap and complementarity between CSST emission-line and SKA HI samples. The reported correlations, BTFR, stacking analysis and cross-power spectrum measurements can inform expectations for cold-gas and stellar-mass relations in multi-wavelength surveys. The adoption of self-consistent gas partitioning within a published semi-analytic model and the use of standard tools (MoMaF, SOFIA-2) are strengths that support reproducibility of the mock catalogs.

minor comments (5)
  1. The abstract states that emission-line luminosities are proportional to SFR; a brief quantitative statement on the scatter or additional dependencies (metallicity, dust) assumed in the post-processing step would help readers assess possible biases in the CSST sample.
  2. The redshift range 0.25–0.5 is specified for the SKA simulation; confirm whether the CSST mock catalog is restricted to the same interval or spans a broader range, and state the impact on the cross-comparison statistics.
  3. In the sections describing the correlation and power-spectrum analyses, include the number of galaxies contributing to each measurement and the method used to estimate uncertainties (bootstrap, jackknife, or analytic).
  4. Figure captions and axis labels should explicitly note the units and any applied cuts (e.g., minimum HI mass or SFR threshold) so that the plotted relations can be reproduced from the text alone.
  5. The manuscript uses both “HI” and “H I”; adopt a single convention throughout for consistency.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive summary of the manuscript, recognition of its strengths in reproducibility and the use of standard tools, and recommendation for minor revision. No specific major comments were raised in the report.

Circularity Check

0 steps flagged

No significant circularity in forward-modeling pipeline

full rationale

The paper's derivation chain consists of running the published L-Galaxies 2020 semi-analytic model on the external Millennium-II N-body simulation, partitioning cold gas self-consistently within that model, post-processing emission-line luminosities scaled to SFR, constructing lightcones with MoMaF, applying SOFIA-2 source finding, and imposing survey-specific selection and noise functions. All subsequent analyses (correlations with halo/HI/stellar mass, BTFR, stacking, and cross-power spectrum) operate on the resulting mock catalogs. No step fits parameters to the target CSST or SKA observables, renames a known result, or reduces a claimed prediction to a self-definition or self-citation chain. The framework is self-contained as a forecasting exercise whose outputs are generated from independent external inputs rather than being forced by construction.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The forecasts rest on the accuracy of the L-Galaxies 2020 model and its gas-partitioning scheme, the Millennium-II simulation, the MoMaF lightcone construction, and the adopted CSST and SKA selection and noise models; all of these are imported from earlier work.

free parameters (1)
  • L-Galaxies 2020 model parameters
    The semi-analytic model contains numerous tunable parameters that control star formation, feedback, and gas partitioning; the mock catalogs inherit these values.
axioms (2)
  • domain assumption The Millennium-II N-body simulation provides a sufficiently accurate representation of the dark-matter halo population at z=0.25-0.5.
    All galaxy properties are built on top of this simulation.
  • domain assumption The post-processed emission-line luminosities scale linearly with star-formation rate and the adopted CSST noise model captures the dominant observational effects.
    These steps determine which galaxies enter the CSST catalog.

pith-pipeline@v0.9.0 · 5667 in / 1525 out tokens · 64951 ms · 2026-05-09T18:16:58.131191+00:00 · methodology

discussion (0)

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Works this paper leans on

51 extracted references · 51 canonical work pages

  1. [1]

    G., & Santos, M

    Alonso, D., Bull, P., Ferreira, P. G., & Santos, M. G. 2015, MNRAS, 447, 400 1

    work page 2015

  2. [2]

    E., & White, S

    Angulo, R. E., & White, S. D. M. 2010, MNRAS, 405, 143 3

    work page 2010

  3. [3]

    2005, MNRAS, 360, 159 2, 5

    Blaizot, J., Wadadekar, Y ., Guiderdoni, B., et al. 2005, MNRAS, 360, 159 2, 5

    work page 2005

  4. [4]

    2009, MNRAS, 398, 1150 2

    Jenkins, A., & Lemson, G. 2009, MNRAS, 398, 1150 2

    work page 2009

  5. [5]

    2003, MNRAS, 344, 1000 4

    Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000 4

    work page 2003

  6. [6]

    2003, PASP, 115, 763 4

    Chabrier, G. 2003, PASP, 115, 763 4

    work page 2003

  7. [7]

    Chen, S.-F., Castorina, E., White, M., & Slosar, A. 2019, J. Cosmol. Astropart. Phys., 2019, 023 1

    work page 2019

  8. [8]

    D., White, M., Chang, T.-C., et al

    Cohn, J. D., White, M., Chang, T.-C., et al. 2016, MNRAS, 457, 2068 1

    work page 2016

  9. [9]

    2023, Accurate Fourier-space Statistics for Line Intensity Mapping: Cartesian Grid Sampling without Aliased Power 2

    Cunnington, S., & Wolz, L. 2023, Accurate Fourier-space Statistics for Line Intensity Mapping: Cartesian Grid Sampling without Aliased Power 2

    work page 2023

  10. [10]

    2019, MNRAS, 488, 5452 2

    Cunnington, S., Wolz, L., Pourtsidou, A., & Bacon, D. 2019, MNRAS, 488, 5452 2

    work page 2019

  11. [11]

    2022, MNRAS, 515, 5894 2, 9

    Deng, F., Gong, Y ., Wang, Y ., et al. 2022, MNRAS, 515, 5894 2, 9

    work page 2022

  12. [12]

    R., Meyer, M

    Duffy, A. R., Meyer, M. J., Staveley-Smith, L., et al. 2012, MNRAS, 426, 3385 6 Euclid Collaboration, Scharr ´e, L., Hirschmann, M., et al. 2024, Astronomy and Astrophysics, 689, A276 4

    work page 2012

  13. [13]

    J., Chatzikos, M., Guzm ´an, F., et al

    Ferland, G. J., Chatzikos, M., Guzm ´an, F., et al. 2017, Revista Mexicana de Astronomia y Astrofisica, 53, 385 4

    work page 2017

  14. [14]

    2013, MNRAS, 434, 1531 3, 4

    Fu, J., Kauffmann, G., Huang, M.-l., et al. 2013, MNRAS, 434, 1531 3, 4

    work page 2013

  15. [15]

    2019, ApJ, 883, 203 2

    Gong, Y ., Liu, X., Cao, Y ., et al. 2019, ApJ, 883, 203 2

    work page 2019

  16. [16]

    2025, Science China

    Gong, Y ., Miao, H., Zhou, X., et al. 2025, Science China

    work page 2025

  17. [17]

    2026, Science China

    Gong, Y ., Miao, H., Zhan, H., et al. 2026, Science China

    work page 2026

  18. [18]

    2023, MNRAS, 523, 1967 6, 7

    Hartley, P., Bonaldi, A., Braun, R., et al. 2023, MNRAS, 523, 1967 6, 7

    work page 2023

  19. [19]

    2011, A&A, 526, A118 6

    Heald, G., J´ozsa, G., Serra, P., et al. 2011, A&A, 526, A118 6

    work page 2011

  20. [20]

    Henriques, B. M. B., White, S. D. M., Lemson, G., et al. 2012, MNRAS, 421, 2904 5

    work page 2012

  21. [21]

    Henriques, B. M. B., White, S. D. M., Thomas, P. A., et al. 2015, MNRAS, 451, 2663 2, 4

    work page 2015

  22. [22]

    Henriques, B. M. B., Yates, R. M., Fu, J., et al. 2020, MNRAS, 491, 5795 2, 3, 4

    work page 2020

  23. [23]

    2023, Monthly Notices of the Royal Astronomical Society, 526, 3610 4

    Hirschmann, M., Charlot, S., Feltre, A., et al. 2023, Monthly Notices of the Royal Astronomical Society, 526, 3610 4

    work page 2023

  24. [24]

    2025, ApJ, 978, 159 14

    Ibitoye, A., Deng, F., Li, Y ., et al. 2025, ApJ, 978, 159 14

    work page 2025

  25. [25]

    2023, Research in Astronomy and Astrophysics, 23, 075003 2

    Jiang, Y .-E., Gong, Y ., Zhang, M., et al. 2023, Research in Astronomy and Astrophysics, 23, 075003 2

    work page 2023

  26. [26]

    G., Haynes, M

    Jones, M. G., Haynes, M. P., Giovanelli, R., & Moorman, C. 2018, MNRAS, 477, 2 3

    work page 2018

  27. [27]

    G., & White, S

    Kitzbichler, M. G., & White, S. D. M. 2007, MNRAS, 376, 2 5

    work page 2007

  28. [28]

    R., McKee, C

    Krumholz, M. R., McKee, C. F., & Tumlinson, J. 2009, ApJ, 693, 216 3 16

    work page 2009

  29. [29]

    S., & Schombert, J

    Lelli, F., McGaugh, S. S., & Schombert, J. M. 2016, ApJL, 816, L14 11

    work page 2016

  30. [30]

    S., Schombert, J

    McGaugh, S. S., Schombert, J. M., Bothun, G. D., & de Blok, W. J. G. 2000, ApJ, 533, L99 11

    work page 2000

  31. [31]

    F., & Krumholz, M

    McKee, C. F., & Krumholz, M. R. 2010, ApJ, 709, 308 3

    work page 2010

  32. [32]

    I., Baugh, C

    Merson, A. I., Baugh, C. M., Helly, J. C., et al. 2013, MNRAS, 429, 556 5

    work page 2013

  33. [33]

    Modi, C., White, M., Castorina, E., & Slosar, A. 2021, J. Cosmol. Astropart. Phys., 2021, 056 1

    work page 2021

  34. [34]

    2009, The Astrophysical Journal, 698, 1467 3

    Obreschkow, D., Croton, D., De Lucia, G., Khochfar, S., & Rawlings, S. 2009, The Astrophysical Journal, 698, 1467 3

    work page 2009

  35. [35]

    2020, MNRAS, 495, 3935 1

    Padmanabhan, H., Refregier, A., & Amara, A. 2020, MNRAS, 495, 3935 1

    work page 2020

  36. [36]

    2024, MNRAS, 529, 4958 4 Planck Collaboration, Ade, P

    Pei, W., Guo, Q., Li, M., et al. 2024, MNRAS, 529, 4958 4 Planck Collaboration, Ade, P. A. R., Aghanim, N., et al. 2014, A&A, 571, A16 3

    work page 2024

  37. [37]

    Pourtsidou, A., Bacon, D., Crittenden, R., & Metcalf, R. B. 2016, MNRAS, 459, 863 1

    work page 2016

  38. [38]

    2020, MNRAS, 499, 4613 1 SKA Cosmology SWG , Bacon, D

    Shi, F., Song, Y .-S., Asorey, J., et al. 2020, MNRAS, 499, 4613 1 SKA Cosmology SWG , Bacon, D. J., Battye, R. A., et al. 2020, PASA, 37 2

    work page 2020

  39. [39]

    Springel, V ., White, S. D. M., Jenkins, A., et al. 2005, Nature, 435, 629 2, 3

    work page 2005

  40. [40]

    B., & Fisher, J

    Tully, R. B., & Fisher, J. R. 1977, Astronomy and Astrophysics, 54, 661 11

    work page 1977

  41. [41]

    2011, STScI, Baltimore 9

    Ubeda, L., et al. 2011, STScI, Baltimore 9

    work page 2011

  42. [42]

    Walter, F., Brinks, E., de Blok, W. J. G., et al. 2008, AJ, 136, 2563 6

    work page 2008

  43. [43]

    2021, MNRAS, 506, 3962 2, 15

    Westmeier, T., Kitaeff, S., Pallot, D., et al. 2021, MNRAS, 506, 3962 2, 15

    work page 2021

  44. [44]

    2001, The Astrophysical Journal, 550, L129 3

    White, M., Hernquist, L., & Springel, V . 2001, The Astrophysical Journal, 550, L129 3

    work page 2001

  45. [45]

    Witzemann, A., Alonso, D., Fonseca, J., & Santos, M. G. 2019, MNRAS, 485, 5519 1

    work page 2019

  46. [46]

    Wolz, L., Blake, C., & Wyithe, J. S. B. 2017, Monthly Notices of the Royal Astronomical Society, 470, 3220 2, 13

    work page 2017

  47. [47]

    Wolz, L., Tonini, C., Blake, C., & Wyithe, J. S. B. 2016, MNRAS, 458, 3399 1

    work page 2016

  48. [48]

    2011, Scientia Sinica Physica, Mechanica & Astronomica, 41, 1441 2

    Zhan, H. 2011, Scientia Sinica Physica, Mechanica & Astronomica, 41, 1441 2

    work page 2011

  49. [49]

    2018, in 42nd COSPAR Scientific Assembly, V ol

    Zhan, H. 2018, in 42nd COSPAR Scientific Assembly, V ol. 42, E1.16 2

    work page 2018

  50. [50]

    2021, Chinese Science Bulletin, 66, 1290 2

    Zhan, H. 2021, Chinese Science Bulletin, 66, 1290 2

    work page 2021

  51. [51]

    Webster, R. L. 2005, Monthly Notices of the Royal Astronomical Society, 359, L30 3

    work page 2005