pith. machine review for the scientific record. sign in

arxiv: 2605.15096 · v1 · submitted 2026-05-14 · 🌌 astro-ph.GA

Recognition: 2 theorem links

· Lean Theorem

Impact of stellar population models on the estimated physical properties of galaxies

Bozhidara Stoyanova (1 , 2) , M\'ed\'eric Boquien (2) , Paola Santini (3) , Francesco Tombesi (1 , 3 , 4 , 5)
show 15 more authors
Emiliano Merlin (3) Pietro Bergamini (6) V\'eronique Buat (7) Denis Burgarella (7) ((1) Universit\`a degli Studi di Roma Tor Vergata (2) Universit\'e C\^ote d'Azur Observatoire de la C\^ote d'Azur CNRS Laboratoire Lagrange (3) INAF - Osservatorio Astronomico di Roma (4) Physics Department Tor Vergata University of Rome (5) INFN - Rome Tor Vergata (6) INAF - OAS Osservatorio di Astrofisica e Scienza dello Spazio di Bologna (7) Aix Marseille Univ CNES LAM)
Authors on Pith no claims yet

Pith reviewed 2026-05-15 03:04 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords stellar population synthesis modelsspectral energy distribution fittinggalaxy stellar massstar formation ratesystematic uncertaintiesCIGALEHST photometryJWST photometry
0
0 comments X

The pith

The choice of stellar population synthesis model shifts derived galaxy stellar masses by up to 0.6 dex and star formation rates by up to 0.4 dex.

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

This paper applies four different stellar population synthesis models to the spectral energy distributions of 17230 galaxies drawn from recent HST and JWST photometric catalogs. It uses the CIGALE code to fit the data and then employs synthetic catalogs to isolate how much the model choice alone alters the recovered stellar masses and star formation rates. The work shows that these choices produce systematic offsets large enough to affect comparisons between different galaxy evolution studies. A reader would care because stellar mass and SFR are the basic quantities used to test models of how galaxies form and grow over time.

Core claim

Four widely used stellar population synthesis models were applied via CIGALE to the observed photometry of 17230 galaxies with spectroscopic redshifts. Stellar mass estimates differ by as much as 0.6 dex and star formation rate estimates by as much as 0.4 dex between particular model pairs. Synthetic catalogs confirm that these offsets arise from the model choice itself rather than from noise or fitting artifacts alone.

What carries the argument

Direct comparison of four stellar population synthesis models inside the CIGALE SED-fitting code, applied both to real HST/JWST photometry and to synthetic catalogs generated from each model, to measure the resulting scatter in stellar mass and SFR.

If this is right

  • Galaxy evolution studies that adopt different stellar population models cannot be compared at face value without correcting for the systematic offsets.
  • Error budgets on stellar mass and SFR must include an additional term that reflects the spread among common SPS models.
  • Meta-analyses that pool results from multiple papers need to standardize on one model or apply explicit cross-calibration offsets.
  • Conclusions about the growth of stellar mass or the decline of star formation across cosmic time carry an extra uncertainty floor set by model choice.

Where Pith is reading between the lines

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

  • Survey teams releasing large catalogs could publish parallel versions of derived properties using two or three standard SPS models so users can propagate the choice as a systematic.
  • The offsets may be larger or smaller for particular galaxy types such as quiescent versus starburst systems, suggesting targeted follow-up on subsamples.
  • Future high-redshift studies with JWST could test whether the same model-to-model spread appears at earlier epochs where different stellar libraries dominate.

Load-bearing premise

The CIGALE fitting procedure and the input HST and JWST photometric catalogs contain no additional systematics that could either mask or exaggerate the differences caused by switching between stellar population models.

What would settle it

Re-running the identical photometry through an independent SED-fitting code that uses the same four models, or generating new synthetic catalogs with known input properties and recovering them with each model, would show whether the 0.6 dex and 0.4 dex spreads persist at the reported size.

Figures

Figures reproduced from arXiv: 2605.15096 by 2), (2) Universit\'e C\^ote d'Azur, 3, (3) INAF - Osservatorio Astronomico di Roma, 4, (4) Physics Department Tor Vergata University of Rome, 5), (5) INFN - Rome Tor Vergata, (6) INAF - OAS Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, (7) Aix Marseille Univ, Bozhidara Stoyanova (1, CNES, CNRS, Denis Burgarella (7) ((1) Universit\`a degli Studi di Roma Tor Vergata, Emiliano Merlin (3), Francesco Tombesi (1, Laboratoire Lagrange, LAM), M\'ed\'eric Boquien (2), Observatoire de la C\^ote d'Azur, Paola Santini (3), Pietro Bergamini (6), V\'eronique Buat (7).

Figure 1
Figure 1. Figure 1: Redshift distribution of the final sample based on the [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of the χ 2 r values of the best-fit models. The distribution peaks around 0.5 with a long tail towards higher val￾ues and a mean value generally between 1 and 3. In this case, the histograms are limited to 0.0 < χ2 r < 10.0 for clarity. A small number of objects have higher values. with systematic offsets that we discuss in the following sections. These plots are also colored by the difference… view at source ↗
Figure 3
Figure 3. Figure 3: Plot of the comparison of the SFR (left) and [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of the “true” (x axis) to the estimated (y axis) SFR (left) and M⋆ (right) for the galaxies in the synthetic catalog for every stellar population. The median relative uncertainty of the sample is shown in the top left of each plot. The solid black lines correspond to a one-to-one relation. These results were obtained using the parameters shown in Table C.1. These plots show the results for Z = 0… view at source ↗
Figure 5
Figure 5. Figure 5: Histograms of the offsets in dex between the physical properties (left: SFR, right: M⋆) determined by the model on the y axis label and the model on the x axis label inferred from the fits on the observed fluxes. The order of the plots follows [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Histograms showing the offsets between the estimated and the “true” values for SFR (left) and M⋆ (right). Every column shows the results on a different set of synthetic data (generated as described in [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of χ 2 r on the synthetic catalogs when using the same stellar population models for the generation of the cat￾alog and the fitting to estimate the physical properties. 0.00 0.25 0.50 0.75 1.00 1.25 (g - i) color 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 lo g(M / Li) BC03 CB19 BPASS single BPASS binary Z = 0.008 Z = 0.02 [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: M⋆/Li as a function of color for the 4 SPS models. Solid and dashed lines correspond to Z = 0.02 and Z = 0.008, respec￾tively. ence for the BC03 model can reach up to 62% between the BC03 and BPASS single-star models for Z = 0.02. The redshift dependence of ∆BIC follows a trend similar to that observed for the M⋆ and SFR offsets, decreasing toward higher redshift. At low redshift, there is a strong prefere… view at source ↗
Figure 9
Figure 9. Figure 9: Moving average of the offsets between models in the inferred physical properties of the sample. The moving window was 1000 galaxies wide. The shaded regions show the MAD of the distribution in each window. Left: M⋆. Right: SFR. Z Model BC03 CB19 BPASS single 0.008 CB19 2.6% 15.7% BPASS single 4.5% 50.6% 4.3% 42.4% BPASS binary 4.2% 26.0% 8.8% 18.5% 40.8% 1.3% 0.02 CB19 2.0% 13.0% BPASS single 2.5% 61.8% 2.… view at source ↗
Figure 10
Figure 10. Figure 10: A reference curve of the cosmic SFRD from [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Left: Distribution of ∆BIC on the fits of the observed galaxies. Right: The moving median of ∆BIC with redshift for all combinations of models. The moving median was calculated using a moving window with a width of 1000 sources. The shaded regions represent the MAD. 2 1 0 1 2 3 4 5 BC03 CB19 7 8 9 10 11 12 2 1 0 1 2 3 4 5 BPASS single 7 8 9 10 11 12 BPASS binary Stellar Mass [log(M /M )] S F R [M /y r] sS… view at source ↗
Figure 12
Figure 12. Figure 12: SFR - M⋆ plots for z ∼ 2 with the results from all model runs on the observed fluxes. The solid black line represents a constant specific SFR (sSFR) of 10 Gyr−1 and is not a fit to the data. The dashed lines show sSFR of 1 Gyr−1 and 100 Gyr−1 . France 2030 investment plan managed by the National Research Agency (ANR), as part of the Initiative of Excellence of Université Côte d’Azur under refer￾ence numbe… view at source ↗
read the original abstract

Accurate estimates of fundamental physical properties of galaxies, such as star formation rates (SFRs) or stellar masses, are essential for testing and constraining models of galaxy formation and evolution. Spectral energy distribution (SED) modeling has become the standard method for deriving these quantities. However, the influence of the underlying stellar population synthesis (SPS) models on the inferred parameters remains poorly quantified. This work investigates how the choice of SPS models affects the estimation of SFRs and stellar masses derived from SED modeling. Four widely used SPS models are applied to a sample of 17 230 galaxies with spectroscopic redshifts, selected from recently published Hubble Space Telescope and James Webb Space Telescope photometric catalogs. SEDs are modeled using the Code for Investigating GALaxy Emission. The analysis is performed in two steps: (i) estimating galaxy properties with each SPS model, and (ii) employing synthetic catalogs to assess the relative impact of model choice on the recovered parameters. Systematic differences are found among the models, with stellar mass estimates varying by up to ~ 0.6 dex and SFRs by up to ~ 0.4 dex between certain model pairs. The choice of stellar population model introduces significant systematic uncertainties in derived galaxy properties. This dependence should be accounted for when interpreting SED-based measurements and comparing results across different studies of galaxy evolution.

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 / 4 minor

Summary. The manuscript claims that the choice of stellar population synthesis (SPS) model introduces significant systematic uncertainties in galaxy stellar mass and star-formation rate (SFR) estimates derived via SED fitting with CIGALE. Applying four SPS models to 17 230 galaxies with spectroscopic redshifts from HST/JWST photometric catalogs, the authors report offsets reaching ~0.6 dex in stellar mass and ~0.4 dex in SFR between certain model pairs. A two-step analysis—direct fitting to real photometry followed by recovery tests on synthetic catalogs—is used to isolate the effect of model choice while holding redshift, photometry, and fitting code fixed.

Significance. If the measured offsets hold, the result is significant for the field: it provides a direct, empirical quantification of SPS-driven systematics that affect fundamental galaxy properties used in evolution studies. The synthetic-catalog step supplies a controlled test that strengthens the central claim by demonstrating that the observed differences arise from model choice rather than from data-specific artifacts. The work therefore supplies a concrete, falsifiable benchmark that future SED analyses can use when comparing results across studies.

major comments (2)
  1. [§4.2] §4.2 (synthetic catalog construction): the procedure for generating input photometry and noise properties is described only at a high level; it is not shown that the synthetic catalogs reproduce the exact magnitude limits, filter transmission curves, and photometric-error distributions of the real HST/JWST sample. Any mismatch would weaken the claim that the recovered offsets are purely due to SPS model differences.
  2. [Table 2, §5.1] Table 2 and §5.1: the maximum reported offsets (0.6 dex in mass, 0.4 dex in SFR) are quoted without the corresponding model-pair identifiers or the number of galaxies contributing to each bin; without this information the reader cannot assess whether the extremes are driven by a small subset of objects or by the full sample.
minor comments (4)
  1. [Abstract] Abstract: the phrase 'up to ~0.6 dex' should explicitly name the two SPS models that produce the largest offset.
  2. [§2.1] §2.1: the list of the four SPS models should be accompanied by a brief table summarizing their key differences (IMF, stellar libraries, TP-AGB treatment) so that the origin of the observed systematics is transparent.
  3. [Figure 3] Figure 3 caption: the color coding for the four models is not defined in the caption or legend; readers must cross-reference the text.
  4. [§6] §6 (discussion): the statement that 'this dependence should be accounted for' would be strengthened by a quantitative recommendation, e.g., a suggested systematic uncertainty floor to be added in quadrature to published mass/SFR errors.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments and positive assessment of the manuscript's significance. We address each major comment point by point below, agreeing where revisions are warranted to improve clarity and rigor.

read point-by-point responses

  1. Referee: [§4.2] §4.2 (synthetic catalog construction): the procedure for generating input photometry and noise properties is described only at a high level; it is not shown that the synthetic catalogs reproduce the exact magnitude limits, filter transmission curves, and photometric-error distributions of the real HST/JWST sample. Any mismatch would weaken the claim that the recovered offsets are purely due to SPS model differences.

    Authors: We agree that a more explicit demonstration of the synthetic catalog fidelity would strengthen the analysis. In the revised manuscript we will expand §4.2 with a detailed description of how the input photometry was generated to match the exact magnitude limits, filter transmission curves, and photometric-error distributions of the real HST/JWST sample. We will add a supplementary figure that directly compares the magnitude and error distributions between the real and synthetic catalogs, confirming that the simulation reproduces the observational properties to within the required precision. revision: yes

  2. Referee: [Table 2, §5.1] Table 2 and §5.1: the maximum reported offsets (0.6 dex in mass, 0.4 dex in SFR) are quoted without the corresponding model-pair identifiers or the number of galaxies contributing to each bin; without this information the reader cannot assess whether the extremes are driven by a small subset of objects or by the full sample.

    Authors: We thank the referee for this observation. In the revised version we will update Table 2 to explicitly list the model pairs that produce each maximum offset and report the number of galaxies contributing to each quoted value. We will also revise the text in §5.1 to state that all offsets are measured across the full sample of 17 230 galaxies and that no small subset drives the reported extremes. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper's central results consist of direct empirical comparisons: four published SPS models are applied via the external CIGALE code to fixed photometric catalogs (real and synthetic), with differences in recovered stellar masses and SFRs measured as outputs. No derivation step reduces by construction to a fitted parameter or self-defined quantity inside the paper; the synthetic-catalog recovery test isolates model choice while holding other inputs fixed. No load-bearing self-citations, uniqueness theorems, or ansatzes are invoked to justify the core claim. The reported offsets (up to 0.6 dex in mass) are therefore independent measurements rather than tautological restatements of the inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on the standard assumption that CIGALE's SED fitting engine correctly isolates the effect of the SPS library while holding all other ingredients fixed; no new free parameters or invented entities are introduced.

axioms (1)
  • domain assumption CIGALE's parameter space and dust/AGN modules remain unchanged across SPS libraries
    Required for the comparison to isolate SPS model differences

pith-pipeline@v0.9.0 · 5714 in / 1285 out tokens · 48138 ms · 2026-05-15T03:04:36.229838+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

78 extracted references · 78 canonical work pages

  1. [1]

    C., Helton, J

    Alberts, S., Williams, C. C., Helton, J. M., et al. 2024, ApJ, 975, 85

    work page 2024

  2. [2]

    & Fritze-v

    Anders, P. & Fritze-v. Alvensleben, U. 2003, A&A, 401, 1063

    work page 2003

  3. [3]

    B., Pirzkal, N., Finkelstein, S

    Bagley, M. B., Pirzkal, N., Finkelstein, S. L., et al. 2024, ApJ, 965, L6

    work page 2024

  4. [4]

    G., Kocevski, D

    Barro, G., Pérez-González, P. G., Kocevski, D. D., et al. 2024, ApJ, 963, 128

    work page 2024

  5. [5]

    & Robotham, A

    Bellstedt, S. & Robotham, A. S. G. 2025, MNRAS, 540, 2703

    work page 2025

  6. [6]

    2023, ApJ, 952, 84

    Bergamini, P., Acebron, A., Grillo, C., et al. 2023, ApJ, 952, 84

    work page 2023

  7. [7]

    E., et al

    Bezanson, R., Labbe, I., Whitaker, K. E., et al. 2024, ApJ, 974, 92

    work page 2024

  8. [8]

    2022, A&A, 663, A50

    Boquien, M., Buat, V ., Burgarella, D., et al. 2022, A&A, 663, A50

    work page 2022

  9. [9]

    2019, A&A, 622, A103

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

    work page 2019

  10. [10]

    & Charlot, S

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

    work page 2003

  11. [11]

    C., et al

    Calzetti, D., Armus, L., Bohlin, R. C., et al. 2000, ApJ, 533, 682

    work page 2000

  12. [12]

    C., Leja, J., Johnson, B

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

    work page 2019

  13. [13]

    M., Kartaltepe, J

    Casey, C. M., Kartaltepe, J. S., Drakos, N. E., et al. 2023, ApJ, 954, 31

    work page 2023

  14. [14]

    J., Moles, M., Cristóbal-Hornillos, D., et al

    Cenarro, A. J., Moles, M., Cristóbal-Hornillos, D., et al. 2019, A&A, 622, A176

    work page 2019

  15. [15]

    2003, PASP, 115, 763

    Chabrier, G. 2003, PASP, 115, 763

    work page 2003

  16. [16]

    & Fall, S

    Charlot, S. & Fall, S. M. 2000, ApJ, 539, 718 Cid Fernandes, R., González Delgado, R. M., García Benito, R., et al. 2014, A&A, 561, A130

    work page 2000

  17. [17]

    2017, A&A, 608, A41

    Ciesla, L., Elbaz, D., & Fensch, J. 2017, A&A, 608, A41

    work page 2017

  18. [18]

    E., Sanders, R

    Clarke, L., Shapley, A. E., Sanders, R. L., et al. 2024, ApJ, 977, 133

    work page 2024

  19. [19]

    W., Papovich, C., Finkelstein, S

    Cole, J. W., Papovich, C., Finkelstein, S. L., et al. 2025, ApJ, 979, 193

    work page 2025

  20. [20]

    2013, ARA&A, 51, 393

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

    work page 2013

  21. [21]

    & Gunn, J

    Conroy, C. & Gunn, J. E. 2010, ApJ, 712, 833

    work page 2010

  22. [22]

    E., & White, M

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

    work page 2009

  23. [23]

    Conselice, C. J. 2014, ARA&A, 52, 291

    work page 2014

  24. [24]

    2023, Nature Astronomy, 7, 622

    Curtis-Lake, E., Carniani, S., Cameron, A., et al. 2023, Nature Astronomy, 7, 622

    work page 2023

  25. [25]

    2007, ApJ, 670, 156 DESI Collaboration, Adame, A

    Daddi, E., Dickinson, M., Morrison, G., et al. 2007, ApJ, 670, 156 DESI Collaboration, Adame, A. G., Aguilar, J., et al. 2024, AJ, 168, 58 Article number, page 12 of 17 Bozhidara Stoyanova et al.: Impact of stellar population models on the estimated physical properties of galaxies

    work page 2007

  26. [26]

    J., Willott, C., Alberts, S., et al

    Eisenstein, D. J., Willott, C., Alberts, S., et al. 2023, arXiv e-prints, arXiv:2306.02465

    work page arXiv 2023

  27. [27]

    2007, A&A, 468, 33

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

    work page 2007

  28. [28]

    Eldridge, J. J. & Stanway, E. R. 2009, MNRAS, 400, 1019

    work page 2009

  29. [29]

    J., Stanway, E

    Eldridge, J. J., Stanway, E. R., Xiao, L., et al. 2017, PASA, 34, e058 Euclid Collaboration, Mellier, Y ., Abdurro’uf, et al. 2025, A&A, 697, A1

    work page 2017

  30. [30]

    L., Capak, P

    Faisst, A. L., Capak, P. L., Davidzon, I., et al. 2016, ApJ, 822, 29

    work page 2016

  31. [31]

    L., Bagley, M

    Finkelstein, S. L., Bagley, M. B., Arrabal Haro, P., et al. 2025, ApJ, 983, L4

    work page 2025

  32. [32]

    L., Bagley, M

    Finkelstein, S. L., Bagley, M. B., Arrabal Haro, P., et al. 2022, ApJ, 940, L55

    work page 2022

  33. [33]

    L., Bagley, M

    Finkelstein, S. L., Bagley, M. B., Ferguson, H. C., et al. 2023, ApJ, 946, L13

    work page 2023

  34. [34]

    P., Mather, J

    Gardner, J. P., Mather, J. C., Abbott, R., et al. 2023, PASP, 135, 068001

    work page 2023

  35. [35]

    2019, MNRAS, 485, 1675

    Ge, J., Mao, S., Lu, Y ., Cappellari, M., & Yan, R. 2019, MNRAS, 485, 1675

    work page 2019

  36. [36]

    2016, MNRAS, 462, 1757

    Gutkin, J., Charlot, S., & Bruzual, G. 2016, MNRAS, 462, 1757

    work page 2016

  37. [37]

    N., Helton, J

    Hainline, K. N., Helton, J. M., Johnson, B. D., et al. 2024, ApJ, 964, 66

    work page 2024

  38. [38]

    M., Rieke, G

    Helton, J. M., Rieke, G. H., Alberts, S., et al. 2025, Nature Astronomy, 9, 729

    work page 2025

  39. [39]

    W., Hsu, C.-C., Hathi, N., et al

    Holwerda, B. W., Hsu, C.-C., Hathi, N., et al. 2024, MNRAS, 529, 1067 Ivezi´c, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111

    work page 2024

  40. [40]

    & Gawiser, E

    Iyer, K. & Gawiser, E. 2017, ApJ, 838, 127

    work page 2017

  41. [41]

    J., Nicholls, D

    Kewley, L. J., Nicholls, D. C., & Sutherland, R. S. 2019, ARA&A, 57, 511

    work page 2019

  42. [42]

    D., Guo, J., Arrabal Haro, P., et al

    Kocevski, D. D., Guo, J., Arrabal Haro, P., et al. 2024, A Spectroscopic Census of Faint, Broad-Line AGN at z>5, JWST Proposal. Cycle 3, ID. #5718

    work page 2024

  43. [43]

    I., Greene, J

    Kokorev, V ., Caputi, K. I., Greene, J. E., et al. 2024, ApJ, 968, 38

    work page 2024

  44. [44]

    E., Bezanson, R., et al

    Labbe, I., Greene, J. E., Bezanson, R., et al. 2025, ApJ, 978, 92

    work page 2025

  45. [45]

    2019, MNRAS, 486, 5104 Le Fèvre, O., Tasca, L

    Laigle, C., Davidzon, I., Ilbert, O., et al. 2019, MNRAS, 486, 5104 Le Fèvre, O., Tasca, L. A. M., Cassata, P., et al. 2015, A&A, 576, A79

    work page 2019

  46. [46]

    2014, ApJS, 212, 14

    Leitherer, C., Ekström, S., Meynet, G., et al. 2014, ApJS, 212, 14

    work page 2014

  47. [47]

    C., Johnson, B

    Leja, J., Carnall, A. C., Johnson, B. D., Conroy, C., & Speagle, J. S. 2019, ApJ, 876, 3

    work page 2019

  48. [48]

    M., Leitherer, C., Ekstrom, S., Meynet, G., & Schaerer, D

    Levesque, E. M., Leitherer, C., Ekstrom, S., Meynet, G., & Schaerer, D. 2012, ApJ, 751, 67

    work page 2012

  49. [49]

    & Dickinson, M

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

    work page 2014

  50. [50]

    & Mannucci, F

    Maiolino, R. & Mannucci, F. 2019, A&A Rev., 27, 3

    work page 2019

  51. [51]

    2005, MNRAS, 362, 799

    Maraston, C. 2005, MNRAS, 362, 799

    work page 2005

  52. [52]

    J., Pentericci, L., Cimatti, A., et al

    McLure, R. J., Pentericci, L., Cimatti, A., et al. 2018, MNRAS, 479, 25

    work page 2018

  53. [53]

    2024, A&A, 691, A240

    Merlin, E., Santini, P., Paris, D., et al. 2024, A&A, 691, A240

    work page 2024

  54. [54]

    P., Oesch, P

    Naidu, R. P., Oesch, P. A., van Dokkum, P., et al. 2022, ApJ, 940, L14

    work page 2022

  55. [55]

    M., Bianchi, S., et al

    Nersesian, A., Xilouris, E. M., Bianchi, S., et al. 2019, A&A, 624, A80

    work page 2019

  56. [56]

    G., Weiner, B

    Noeske, K. G., Weiner, B. J., Faber, S. M., et al. 2007, ApJ, 660, L43

    work page 2007

  57. [57]

    2009, A&A, 507, 1793

    Noll, S., Burgarella, D., Giovannoli, E., et al. 2009, A&A, 507, 1793

    work page 2009

  58. [58]

    & Salim, S

    Osborne, C. & Salim, S. 2024, ApJ, 962, 59

    work page 2024

  59. [59]

    G., Mobasher, B., et al

    Pacifici, C., Iyer, K. G., Mobasher, B., et al. 2023, ApJ, 944, 141 Pérez-González, P. G., Barro, G., Rieke, G. H., et al. 2024, ApJ, 968, 4

    work page 2023

  60. [60]

    2012, MNRAS, 422, 3285 Planck Collaboration, Aghanim, N., Akrami, Y ., et al

    Pforr, J., Maraston, C., & Tonini, C. 2012, MNRAS, 422, 3285 Planck Collaboration, Aghanim, N., Akrami, Y ., et al. 2020, A&A, 641, A6

    work page 2012

  61. [61]

    2019, MNRAS, 490, 978

    Plat, A., Charlot, S., Bruzual, G., et al. 2019, MNRAS, 490, 978

    work page 2019

  62. [62]

    2023, MNRAS, 519, 1526

    Popesso, P., Concas, A., Cresci, G., et al. 2023, MNRAS, 519, 1526

    work page 2023

  63. [63]

    2023, PASP, 135, 048001

    Rigby, J., Perrin, M., McElwain, M., et al. 2023, PASP, 135, 048001

    work page 2023

  64. [64]

    I., et al

    Rinaldi, P., Navarro-Carrera, R., Caputi, K. I., et al. 2025, ApJ, 981, 161

    work page 2025

  65. [65]

    D., Tacchella, S., et al

    Robertson, B., Johnson, B. D., Tacchella, S., et al. 2024, ApJ, 970, 31

    work page 2024

  66. [66]

    E., Tacchella, S., Johnson, B

    Robertson, B. E., Tacchella, S., Johnson, B. D., et al. 2023, Nature Astronomy, 7, 611

    work page 2023

  67. [67]

    Salim, S., Boquien, M., & Lee, J. C. 2018, ApJ, 859, 11

    work page 2018

  68. [68]

    & Narayanan, D

    Salim, S. & Narayanan, D. 2020, ARA&A, 58, 529

    work page 2020

  69. [69]

    Salpeter, E. E. 1955, ApJ, 121, 161 Sánchez, S. F., Barrera-Ballesteros, J. K., Lacerda, E., et al. 2022, ApJS, 262, 36

    work page 1955

  70. [70]

    2017, ApJ, 847, 76 Seillé, L.-M., Buat, V ., Fernández, V ., et al

    Santini, P., Fontana, A., Castellano, M., et al. 2017, ApJ, 847, 76 Seillé, L.-M., Buat, V ., Fernández, V ., et al. 2024, A&A, 689, A102

    work page 2017

  71. [71]

    Smith, D. J. B., Dunne, L., da Cunha, E., et al. 2012, MNRAS, 427, 703

    work page 2012

  72. [72]

    Somerville, R. S. & Davé, R. 2015, ARA&A, 53, 51

    work page 2015

  73. [73]

    S., Steinhardt, C

    Speagle, J. S., Steinhardt, C. L., Capak, P. L., & Silverman, J. D. 2014, ApJS, 214, 15

    work page 2014

  74. [74]

    Stanway, E. R. & Eldridge, J. J. 2018, MNRAS, 479, 75

    work page 2018

  75. [75]

    N., Hopkins, A

    Taylor, E. N., Hopkins, A. M., Baldry, I. K., et al. 2011, MNRAS, 418, 1587

    work page 2011

  76. [76]

    2022, ApJ, 935, 110

    Treu, T., Roberts-Borsani, G., Bradac, M., et al. 2022, ApJ, 935, 110

    work page 2022

  77. [77]

    2011, Ap&SS, 331, 1

    Walcher, J., Groves, B., Budavári, T., & Dale, D. 2011, Ap&SS, 331, 1

    work page 2011

  78. [78]

    G., Adelman, J., Anderson, Jr., J

    York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120, 1579 Article number, page 13 of 17 A&A proofs:manuscript no. aanda_revised_clean Appendix A: 2D histograms of offset variations with redshift BC03 0 5 10CB19 3403 6807 0 5 10BPASS single 958 1917 977 1954 0.5 0.0 0.5 1.0 BC03 0 5 10BPASS binary 1102 2204 0.5 0.0 0.5 1.0 CB19 1246 2492 0...

    work page 2000