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Simultaneously Modelling Dusty Star Forming Galaxies and Massive Quiescents: A Calibration Framework for Galaxy Formation Models

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read One calibrated galaxy-formation model now fits both dusty starbursts and massive quiescent galaxies.

desk verdict A serious calibration framework worth refereeing, but the dust-to-metal assumption is load-bearing and the abstract's physical claims should be softened. read the letter →

arxiv 2504.15283 v1 pith:RRP6X26O submitted 2025-04-21 astro-ph.GA

Pablo Araya-Araya , Rachel K. Cochrane , Christopher C. Hayward , Laerte Sodré Jr. , Robert M. Yates , Marcel P. van Daalen , Marcelo C. Vicentin This is my paper · ORCID
classification astro-ph.GA
keywords semi-analyticmodelsMCMCcalibrationdustystar-forminggalaxiessub-millimetremassivequiescentL-GalaxiesAGNfeedbackmerger-inducedstarbursts
open problems Dark Matter
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper asks whether the long-standing mismatch between dusty star-forming galaxies (DSFGs) and massive quiescent galaxies (MQs) in galaxy formation models can be cured by systematic calibration rather than by changing the physics by hand. The authors use the MCMC mode of the L-Galaxies semi-analytic model to fit 15 free parameters against nine different combinations of constraints, adding the number density of bright sub-millimetre galaxies as a direct calibration target for the first time. Among the nine best-fit models, one configuration — 'no HIMF', which drops the local neutral-hydrogen mass function from the constraint set — matches the observed 870-micron number counts and stays consistent with the lower limits on the high-redshift massive quiescent population. The price of that compromise is specific physics: high star-formation efficiency in merger-driven starbursts and supermassive-black-hole growth by cold-gas accretion that does not depend on halo mass. The result matters because it demonstrates that the SMG-MQ tension can be partly absorbed by parameter-space exploration, while also exposing how degenerate the fitted mechanisms are.

What carries the argument

The carrying object is the MCMC calibration mode of the L-Galaxies semi-analytic model, which explores 15 free parameters against chosen observables using a representative sample of dark-matter merger trees. Two new pieces are added: a merger-tree sampling procedure that keeps the rare SMG population representative, and a direct SMG number-density constraint built by converting simulated galaxies to 870-micron flux densities with the Cochrane et al. (2023a) scaling relations. The diagnostic that carries the physical interpretation is a set of fitted scaling laws — the merger starburst efficiency $M_{\star,\mathrm{burst}}/M_{\mathrm{cold}}$ vs mass ratio, the SN feedback efficiencies vs $V_{\mathrm{max}}$, and the SMBH cold-gas accretion fraction $\Delta M_{\mathrm{BH,Q}}/M_{\mathrm{cold}}$ vs $V_{\mathrm{200c}}$. The 'no HIMF' configuration's signature is a nearly flat SMBH accretion fraction combined with steep merger-starburst efficiency, which together let galaxies build stars and black holes rapidly and then quench.

What would settle it

Run the identical calibration with sub-mm fluxes computed from a dust-tracking version of L-Galaxies or from radiative-transfer post-processing; if the resulting SMG number-density target moves significantly, the 'no HIMF' parameters are an artifact of the scaling relations. A sharper observational test would be an MQ number-density measurement at z~3-4 whose central value sits clearly above the lower limits used here, since that would rule out the 'no HIMF' compromise.

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Extended reading notes

Core claim

The central claim is that the L-Galaxies SAM contains a parameter combination that simultaneously reproduces the observed number density of DSFGs and the observationally-derived lower limits for high-redshift massive quiescent galaxies. This is configuration 'no HIMF', calibrated against the Leja et al. (2020) stellar mass functions and Leja et al. (2022) quiescent fractions at $z=0.4$ and $z=2.8$, plus the AS2UDS SMG number density at $z\sim2.8$. Its best-fit parameters imply that merger-induced starbursts convert a large fraction of cold gas into stars even in minor mergers, and that the fraction of cold gas accreted by SMBHs during mergers is nearly flat with halo virial velocity, so massive black holes can grow quickly. The paper also claims this fit produces an SMBH mass function at $z=0$ in better agreement with observations than the other configurations, while the fitted models that instead match DSFGs best fail on the quiescent population, and vice versa. The authors frame the result as a step toward resolving the tension, not a unique solution: nearly equally good fits span about two dex in feedback efficiencies.

Load-bearing premise

The fit's load-bearing premise is that the 870-micron scaling relations used here (plus a fixed 40% dust-to-metal ratio for cold-gas metals) correctly translate simulated galaxies into sub-mm brightness; if that translation is biased, the best-fit parameters — including the claimed merger efficiencies and SMBH accretion behavior — would shift.

Editorial extensions

If this is right

  • Adding a single SMG number-density constraint at $z\sim2.8$ lifts predicted 870-micron number counts from more than two orders of magnitude below observation to within a factor of a few across an order of magnitude in flux density.
  • When the SMG constraint is included, the model that also matches the massive quiescent lower limits is 'no HIMF'; dropping the $z=0$ HI mass function does not break its HI predictions, so the HIMF appears to be a less critical constraint than previously assumed.
  • The best-fit physics of the 'no HIMF' model — high merger-driven starburst efficiencies and halo-mass-independent SMBH cold-gas accretion — provides a concrete, testable alternative to top-heavy IMF modifications for producing bright SMGs.
  • The $z=0$ SMBH mass function is not used in calibration, yet it discriminates between the two SMBH growth modes (peaked vs Schechter-like), so future calibrations that include it should break some of the degeneracies shown in Figure 15.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same calibration recipe could be transferred to other semi-analytic and cosmological simulation pipelines, offering a route to the SMG-MQ compromise without changing the IMF.
  • The fitted halo-mass-independence of SMBH cold-gas accretion is probably a stand-in for a more physical trigger: the MCMC finds that rapid black-hole growth must accompany rapid merger starbursts, so an explicit coupling between starburst-driven turbulence and black-hole feeding could reproduce the same observable.
  • Because the 40% dust-to-metal assumption is fixed rather than fitted, repeating the calibration with a dust-evolution model would directly test whether the claimed parameters are an artifact of the dust recipe rather than a required property of galaxy formation.
  • The representative merger-tree selection method should work for any rare tracer with a defined number density, so similar one-point constraints could bring other rare populations — for example high-redshift quasars or bright Lyman-alpha emitters — into MCMC calibration loops.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper uses the MCMC mode of the L-Galaxies semi-analytic model to calibrate 15 free parameters against nine combinations of observational constraints, including a newly implemented constraint on the number density of bright sub-mm galaxies (S870 >= 5.2 mJy at z ~ 2.8), computed from the Cochrane et al. (2023a) scaling relations with an assumed dust-to-metal ratio of 40%. After calibrating each configuration, the authors run the full Millennium volume and compare predictions for stellar mass functions, quiescent fractions, the HIMF, sub-mm number counts, redshift distributions, massive quiescent number densities, cosmic star formation rate density, and SMBH mass functions. They find that configurations including the nSMG constraint reproduce the cumulative S870 number counts within a factor of a few but underpredict the adopted nSMG at z = 2.8 by at least a factor of 2.5; none of the models matches the median high-redshift MQ number density, though some (notably 'no HIMF') reach the observational lower limits. The 'no HIMF' configuration is presented as the best compromise, and the paper argues that it requires high merger-induced star-formation efficiencies and a nearly halo-mass-independent SMBH cold-gas accretion rate. A degeneracy analysis (Section 4.3) shows that models within 1 dex of the best-fit likelihood span a wide range of physical scaling relations.

Significance. The paper's contribution is methodological and empirical: it systematically explores nine constraint configurations within the L-Galaxies MCMC framework and introduces, for the first time in this framework, a sub-mm number density as a direct calibration constraint. The resulting mock catalogues and number-count predictions are falsifiable, and the comparison to observations is transparent. If the central result holds, the 'no HIMF' configuration is a viable L-Galaxies variant that improves the long-standing SMG-MQ tension, and the paper demonstrates the value of robust calibration in exposing degeneracies. The explicit analysis of model degeneracy (Section 4.3) is a strength, as it quantifies how little the current observables constrain the underlying physical scalings. However, the claimed physical requirements are not yet robust because of the sensitivity to the dust-to-metal assumption and the degeneracy among equally good fits.

major comments (3)
  1. [3.3.3, 5.4] The nSMG constraint is computed from S870 estimated with the Cochrane et al. (2023a) scaling relations applied to L-Galaxies galaxies, with the dust mass set to 40% of cold-gas metals (Section 3.3.3 and Section 5.4). Because L-Galaxies does not track dust, the likelihood for the only DSFG-related constraint depends directly on this single scalar assumption. The paper asserts that this is unlikely to affect the results, citing Remy-Ruyer et al. (2014), but provides no sensitivity test or error propagation. A factor-of-two change in the dust-to-metal ratio - within the scatter of the cited relation - would shift the predicted S870 and the number of galaxies above 5.2 mJy, and the MCMC would compensate with different best-fit parameters (e.g., alpha_SF,burst and the SMBH growth parameters f_BH and V_BH in Appendix B). The claim that the model 'requires' high merger star-formation efficiencies and halo-mass-independent SMBH accretion is therefore contingent on an unquantified calibration choice. Please add a sensitivity run or explicitly propagate the uncertainty in the dust-to-metal ratio.
  2. [4.1.4, Figure 6] The abstract states that the model 'reasonably matches the number density of DSFGs,' but the calibration target itself - nSMG at z=2.8 with S870>=5.2 mJy - is not reproduced: all configurations except 'no low-z SMF, fQ' underpredict this number density by at least a factor of 2.5, and the highlighted 'no HIMF' configuration is among those. The support for the abstract claim comes from the cumulative number counts across flux density (Figure 7), which is a different, weaker statistic, and from the z=2.0 point. Please either soften the abstract and conclusions to state that the model approaches or is within a factor of a few of the observed nSMG, or provide a justification for why a factor-2.5 miss at the adopted calibration point is acceptable.
  3. [4.3, Figure 15; Abstract] The abstract's causal claim that the identified model 'requires high star formation efficiencies in mergers and a null dependency of SMBH cold gas accretion on halo mass' is not supported by the paper's own degeneracy analysis. Figure 15 shows that parameter sets within 1 dex of the best-fit likelihood produce scaling relations spanning roughly two orders of magnitude, and for three of the four scalings the best-fit model does not occupy the most densely populated region. Thus the quoted physical properties are properties of a single best-fit point, not robust features of the acceptable model ensemble. The paper should either demonstrate that these physical scalings are common to the high-likelihood region or explicitly restrict the abstract and conclusions to the best-fit model.
minor comments (5)
  1. [3.5] The convergence criterion ('no new accepted point with a higher likelihood within the final ~1000 steps') is not a standard convergence diagnostic; please report a quantitative measure such as the Gelman-Rubin statistic or split-chain acceptance diagnostics.
  2. [3.2, Appendix A] The average relative errors quoted in Section 3.2 (~10%, ~25%, ~5%, ~20% for SMFs, f_Q, nSMG, and HIMF) are not consistent with the values in Appendix A, where the f_Q error at z=2.8 is reported as 35% and the nSMG error as ~2%. Please harmonize the numbers.
  3. [Figure 6] The x-axis of Figure 6 is labelled only 'Configuration' with numbers 0-8; please add tick labels matching Table 1 (e.g., 'baseH20', 'baseL20', etc.) to make the figure self-contained.
  4. [2] The text says 'To date, five more recent modifications of L-Galaxies have been published' but then lists six references (Yates et al. 2021; Ayromlou et al. 2021; Izquierdo-Villalba et al. 2022; Murphy et al. 2022; Spinoso et al. 2023; Yates et al. 2024). Please correct the count or the reference list.
  5. [3.3.3] The phrase 'we further modified L-Galaxies to include this factor in the SfrInst parameter' is vague; please specify exactly how the merger-induced starburst SFR is added to the instantaneous SFR, as this directly affects the S870 estimates.

Circularity Check

1 steps flagged · score 2.0 of 10

The z=2.8, S870>5.2 mJy normalization of the predicted number counts is the fitted constraint itself, but the rest of the calibration and the physical claims rest on independent external inputs.

  1. fitted input called prediction [Section 3.3.3 (nSMG constraint) vs Section 4.1.4 / Fig. 7 (predicted S870 counts)]
    "we adopted S870 = 5.2 mJy (where completeness exceeds 90 per cent) as the flux density threshold for our calibration. ... the number density of galaxies with S870 ≥ 5.2 mJy at z ∼ 2.8 is nSMG = (2.48 ± 0.3) × 10−5 h3 Mpc−3. ... The first notable result is that including the number density of galaxies with S870 ≥ 5.2 mJy at z = 2.8 as an observational constraint (a single data point) improves the consistency of the predicted S870 number counts with observational results."

    The nSMG calibration value is, by definition, the cumulative integral of the same predicted dN/dS counts above S870 = 5.2 mJy at z = 2.8. Thus the improvement at that specific point in Figure 7 is a restatement of the fitted constraint, not an independent prediction. However, Figure 7 also compares counts across an order of magnitude in S870 and Figure 8 covers the full redshift distribution, neither of which is fixed by the single nSMG point, so the circularity is partial and confined to the normalization at the calibration threshold.

full rationale

The central calibration target is external: the observed SMG number density is taken from Dudzeviciute et al. (2020), and the model S870 values are computed with the Cochrane et al. (2023a) scaling relations, which were calibrated from radiative-transfer post-processing and are not fitted to the present MCMC target. The 'no HIMF' model's massive-quiescent agreement is an out-of-sample comparison at z ~ 3.5 and uses a different quiescent definition (Carnall et al. 2020) than the fQ constraint, so it is not forced by the fit. The abstract's statements about high merger star-formation efficiencies and halo-mass-independent SMBH accretion are direct readings of best-fit parameters rather than predictions; the paper's own Section 4.3 explicitly shows large degeneracies among equal-likelihood models, which is an interpretation risk but not a circular reduction. The only mild circular element is that the z = 2.8, S870 > 5.2 mJy normalization of the 'predicted' counts is the fitted constraint itself; the shape of the counts and their redshift dependence remain genuine out-of-sample predictions. Overall, the paper's main calibration claim is self-contained against external data, and the identified issue is a limited, partial self-consistency effect rather than load-bearing circularity.

Assumptions & free parameters 15 free parameters · 5 assumptions · 0 invented entities

The central claim rests on calibrating 15 L-Galaxies free parameters against observational data. The paper's own contribution is the calibration framework, not new physics laws, so the parameters are inputs fitted to data. Additional assumptions include the external S870 scaling relations, the dust-to-metal fraction, and the representativeness of the reduced merger tree sample.

free parameters (15)
  • alpha_SF = See Figure B1 (not tabulated in text)
    Fitted by MCMC; sets the efficiency of converting H2 surface density into stars in secular star formation.
  • alpha_SF,burst = See Figure B1 (not tabulated in text)
    Fitted by MCMC; normalization of the merger-induced starburst efficiency in Equation 1.
  • beta_SF,burst = See Figure B1 (not tabulated in text)
    Fitted by MCMC; power-law index of the mass-ratio dependence in merger starbursts.
  • k_AGN = See Figure B1 (not tabulated in text)
    Fitted by MCMC; efficiency of radio-mode AGN hot gas accretion and energy injection.
  • f_BH = See Figure B1 (not tabulated in text)
    Fitted by MCMC; normalization of cold gas accretion onto SMBHs in mergers (Equation 3).
  • V_BH = See Figure B1 (not tabulated in text)
    Fitted by MCMC; velocity scale controlling the halo-mass dependence of SMBH cold gas accretion.
  • eta_reheat = See Figure B1 (not tabulated in text)
    Fitted by MCMC; normalization of the SN reheating efficiency scaling (Equation 2).
  • V_reheat = See Figure B1 (not tabulated in text)
    Fitted by MCMC; velocity scale of the SN reheating efficiency.
  • beta_reheat = See Figure B1 (not tabulated in text)
    Fitted by MCMC; power-law slope of the SN reheating efficiency.
  • eta_eject = See Figure B1 (not tabulated in text)
    Fitted by MCMC; normalization of the SN ejection efficiency scaling.
  • V_eject = See Figure B1 (not tabulated in text)
    Fitted by MCMC; velocity scale of the SN ejection efficiency.
  • beta_eject = See Figure B1 (not tabulated in text)
    Fitted by MCMC; power-law slope of the SN ejection efficiency.
  • gamma_reinc = See Figure B1 (not tabulated in text)
    Fitted by MCMC; sets the reincorporation timescale of ejected gas.
  • alpha_friction = See Figure B1 (not tabulated in text)
    Fitted by MCMC; correction factor for dynamical friction timescales.
  • M_RP = See Figure B1 (not tabulated in text)
    Fitted by MCMC; halo mass threshold above which ram-pressure stripping is applied.
assumptions (5)
  • domain assumption The Planck 2014 cosmology and the Millennium dark-matter-only simulation merger trees provide a correct backbone for galaxy formation.
    Used throughout the paper as the fixed framework on which L-Galaxies runs; no validation of the N-body tree accuracy is given.
  • domain assumption The Cochrane et al. (2023a) S870 scaling relations convert galaxy SFR, stellar mass, dust mass, and redshift into 870 micron flux densities accurately for the entire simulated population.
    Invoked in Section 3.3.3 to compute nSMG, the paper's new calibration constraint; the relations are external and their systematic uncertainties are not propagated.
  • ad hoc to paper 40% of the metals in cold gas are in the form of dust.
    Stated in Section 5.4 as an assumption because L-Galaxies 2020 does not track dust; used to estimate dust mass for S870 fluxes.
  • domain assumption The sample of 430 merger trees selected by the new method represents full-volume predictions for the calibrated observables.
    The MCMC runs use this small sample; Appendix A shows representative errors of roughly 5-30% at z=0.4 and z=2.8, and representativeness at other redshifts is assumed.
  • domain assumption The observational constraints (Leja et al. 2020 SMFs, Leja et al. 2022 quiescent fractions, HIMF compilations, Dudzeviciute et al. 2020 nSMG) are unbiased estimates of the true galaxy population.
    Any systematic errors in SED fitting, sample selection, or photometric redshifts directly shift the calibration target.

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Pith. "Pith review of Simultaneously Modelling Dusty Star Forming Galaxies and Massive Quiescents: A Calibration Framework for Galaxy Formation Models." pith.science (2026). https://pith.science/paper/RRP6X26O

@misc{pith2026250415283,
  author       = {Pith},
  title        = {Pith review of: Simultaneously Modelling Dusty Star Forming Galaxies and Massive Quiescents: A Calibration Framework for Galaxy Formation Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RRP6X26O}},
  note         = {Machine review of arXiv:2504.15283}
}
read the original abstract

Galaxy formation models, particularly semi-analytic models (SAMs), rely on differential equations with free parameters to describe the physical mechanisms governing galaxy formation and evolution. Traditionally, most SAMs calibrate these parameters manually to match observational data. However, this approach fails to fully explore the multidimensional parameter space, resulting in limited robustness and inconsistency with some observations. In contrast, the L-Galaxies SAM features a unique Markov Chain Monte Carlo (MCMC) mode, enabling robust model calibration. Using this functionality, we address a long-standing tension in galaxy formation models: simultaneously reproducing the number densities of dusty star-forming galaxies (DSFGs) and high-redshift massive quiescent galaxies (MQs). We test nine combinations of observational constraints - including stellar mass functions, quiescent fractions, neutral hydrogen mass functions, and DSFG number densities - across different redshifts. We then analyze the resulting galaxy property predictions and discuss the underlying physical mechanisms. Our results identify a model that reasonably matches the number density of DSFGs while remaining consistent with observationally-derived lower limits on the number density of high-redshift MQs. This model requires high star formation efficiencies in mergers and a null dependency of supermassive black hole (SMBH) cold gas accretion on halo mass, facilitating rapid stellar mass and SMBH growth. Additionally, our findings highlight the importance of robust calibration procedures to address the significant degeneracies inherent to multidimensional galaxy formation models.

Figures

Figures reproduced from arXiv: 2504.15283 by the authors.

Figure 1
Figure 1. The stellar mass - halo mass relation for central galaxies in a 20×20 grid. We use this distribution to obtain a preliminary sample of dark matter halos and identify an optimal sample merger trees. choose 𝑧 = 2.8 in this work, as our motivation is to test whether we can match the SMG density and the quiescent galaxy fraction at this epoch. In particular, our observables, which we describe in Section 3.3, are the ste… view at source ↗
Figure 2
Figure 2. Comparison between the stellar mass functions of Leja et al. (2020) (continuity model used in this work; L20) and those used in Henriques et al. (2020) (compilation from literature; H20) at 𝑧 ∼ 0.4, 1.0, 2.0, and 2.8. Dashed grey horizontal and vertical lines indicate unity and the mass completeness of the Leja et al. (2020) dataset, respectively. The L20 stellar mass functions indicate higher galaxy number densitie… view at source ↗
Figure 3
Figure 3. The predicted stellar mass functions (SMFs) at 𝑧 = 0.4, 1.0, 2.0, and 2.8, for every configuration listed in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: The predicted quiescent fraction ( 𝑓Q; log(sSFR/yr−1 ) ≤ -11) as a function of stellar mass at 𝑧 = 0.4, 1.0, 2.0, and 2.8, for every configuration listed in [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: The predicted neutral hydrogen mass function (HIMF) at 𝑧 = 0 for every configuration listed in [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 8
Figure 8. Figure 8: The predicted redshift distribution of bright SMGs (𝑆870 ≥ 5.2 mJy) from a mock catalogue constructed for every configuration listed in [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: The predicted evolution of the number density of massive (log(𝑀★/M⊙ ) ≥ 10.6) quiescent (sSFR < 0.2/𝑡obs(𝑧), where 𝑡obs(𝑧) is the age of the universe at redshift 𝑧) galaxies obtained for all configuration listed in [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]
Figure 11
Figure 11. Figure 11: The supermassive black hole mass function at 𝑧 = 0, predicted by each of the configurations listed in [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]
Figure 12
Figure 12. Figure 12: Top: The best-fit parameter associated with the efficiency in con￾verting H2 into stars (secular star formation). The error bar indicates the 16th and 84th percentiles of the final 2, 000 MCMC runs of the 96 chains. Models calibrated with 𝑛SMG present similar and lowe…
Figure 13
Figure 13. Figure 13: Top: The best-fit scaling relation (Equation 2) that describes the efficiency of heating the cold gas and reheating the hot gas atmosphere, 𝜖disk, as a function of the maximum halo rotational velocity, 𝑉max - a proxy of halo mass. The model that best matches the obser…
Figure 15
Figure 15. Figure 15: Physical scaling relations that describe the SN feedback, 𝜖disk and 𝜖disk (top panels; Equation 2), SMBH growth (bottom left panel; Equation 3), and stellar mass formed in merger-induced starbursts (bottom right; Equation 1) for all sets of free parameters with total …

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Reference graph

Works this paper leans on

160 extracted references · 5 canonical work pages · cited by 1 Pith paper

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTION or pop #1...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTION or pop #1...

  3. [3]

    Alberts S., et al., 2024, @doi [ ] 10.3847/1538-4357/ad66cc , https://ui.adsabs.harvard.edu/abs/2024ApJ...975...85A 975, 85

    doi:10.3847/1538-4357/ad66cc 2024

  4. [4]

    E., Hilbert S., 2015, @doi [ ] 10.1093/mnras/stv050 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.448..364A 448, 364

    Angulo R. E., Hilbert S., 2015, @doi [ ] 10.1093/mnras/stv050 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.448..364A 448, 364

    doi:10.1093/mnras/stv050 2015

  5. [5]

    E., White S

    Angulo R. E., White S. D. M., 2010, @doi [ ] 10.1111/j.1365-2966.2010.16459.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.405..143A 405, 143

    arXiv 2010

  6. [6]

    C., Sodr \'e Laerte J., Overzier R

    Araya-Araya P., Vicentin M. C., Sodr \'e Laerte J., Overzier R. A., Cuevas H., 2021, @doi [ ] 10.1093/mnras/stab1133 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.504.5054A 504, 5054

    doi:10.1093/mnras/stab1133 2021

  7. [7]

    Araya-Araya P., et al., 2024, @doi [ ] 10.3847/1538-4357/ad90ae , https://ui.adsabs.harvard.edu/abs/2024ApJ...977..204A 977, 204

    doi:10.3847/1538-4357/ad90ae 2024

  8. [8]

    M., Nelson D., White S

    Ayromlou M., Kauffmann G., Yates R. M., Nelson D., White S. D. M., 2021, @doi [ ] 10.1093/mnras/stab1245 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.505..492A 505, 492

    doi:10.1093/mnras/stab1245 2021

Show all 160 references
  1. [9]

    K., Glazebrook K., Brinkmann J., Ivezi \'c Z ., Lupton R

    Baldry I. K., Glazebrook K., Brinkmann J., Ivezi \'c Z ., Lupton R. H., Nichol R. C., Szalay A. S., 2004, @doi [ ] 10.1086/380092 , https://ui.adsabs.harvard.edu/abs/2004ApJ...600..681B 600, 681

    2004 doi

  2. [10]

    K., Glazebrook K., Driver S

    Baldry I. K., Glazebrook K., Driver S. P., 2008, @doi [ ] 10.1111/j.1365-2966.2008.13348.x , https://ui.adsabs.harvard.edu/abs/2008MNRAS.388..945B 388, 945

    2008

  3. [12]

    J., Cowie L

    Barger A. J., Cowie L. L., Sanders D. B., Fulton E., Taniguchi Y., Sato Y., Kawara K., Okuda H., 1998, @doi [ ] 10.1038/28338 , https://ui.adsabs.harvard.edu/abs/1998Natur.394..248B 394, 248

    1998 doi

  4. [14]

    S., Wechsler R

    Behroozi P. S., Wechsler R. H., Conroy C., 2013, @doi [ ] 10.1088/0004-637X/770/1/57 , https://ui.adsabs.harvard.edu/abs/2013ApJ...770...57B 770, 57

    2013 doi

  5. [15]

    Binney J., Tremaine S., 1987, Galactic dynamics

    1987

  6. [16]

    W., Chapman S

    Blain A. W., Chapman S. C., Smail I., Ivison R., 2004, @doi [ ] 10.1086/422353 , https://ui.adsabs.harvard.edu/abs/2004ApJ...611..725B 611, 725

    2004 doi

  7. [18]

    Bruzual G., Charlot S., 2003, @doi [ ] 10.1046/j.1365-8711.2003.06897.x , https://ui.adsabs.harvard.edu/abs/2003MNRAS.344.1000B 344, 1000

    2003

  8. [19]

    Calvi R., Castignani G., Dannerbauer H., 2023, @doi [ ] 10.1051/0004-6361/202346200 , https://ui.adsabs.harvard.edu/abs/2023A&A...678A..15C 678, A15

    2023 doi

  9. [20]

    C., et al., 2020, @doi [ ] 10.1093/mnras/staa1535 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.496..695C 496, 695

    Carnall A. C., et al., 2020, @doi [ ] 10.1093/mnras/staa1535 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.496..695C 496, 695

    2020 doi

  10. [21]

    C., et al., 2023, @doi [ ] 10.1093/mnras/stad369 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.520.3974C 520, 3974

    Carnall A. C., et al., 2023, @doi [ ] 10.1093/mnras/stad369 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.520.3974C 520, 3974

    2023 doi

  11. [22]

    M., 2016, @doi [ ] 10.3847/0004-637X/824/1/36 , https://ui.adsabs.harvard.edu/abs/2016ApJ...824...36C 824, 36

    Casey C. M., 2016, @doi [ ] 10.3847/0004-637X/824/1/36 , https://ui.adsabs.harvard.edu/abs/2016ApJ...824...36C 824, 36

    2016 doi

  12. [23]

    M., Narayanan D., Cooray A., 2014, @doi [ ] 10.1016/j.physrep.2014.02.009 , https://ui.adsabs.harvard.edu/abs/2014PhR...541...45C 541, 45

    Casey C. M., Narayanan D., Cooray A., 2014, @doi [ ] 10.1016/j.physrep.2014.02.009 , https://ui.adsabs.harvard.edu/abs/2014PhR...541...45C 541, 45

    2014 doi

  13. [24]

    Cecchi R., Bolzonella M., Cimatti A., Girelli G., 2019, @doi [ ] 10.3847/2041-8213/ab2c80 , https://ui.adsabs.harvard.edu/abs/2019ApJ...880L..14C 880, L14

    2019 doi

  14. [25]

    Chabrier G., 2003, @doi [ ] 10.1086/376392 , https://ui.adsabs.harvard.edu/abs/2003PASP..115..763C 115, 763

    2003 doi

  15. [26]

    C., Lewis G

    Chapman S. C., Lewis G. F., Scott D., Richards E., Borys C., Steidel C. C., Adelberger K. L., Shapley A. E., 2001, @doi [ ] 10.1086/318919 , https://ui.adsabs.harvard.edu/abs/2001ApJ...548L..17C 548, L17

    2001 doi

  16. [27]

    C., Blain A

    Chapman S. C., Blain A. W., Smail I., Ivison R. J., 2005, @doi [ ] 10.1086/428082 , https://ui.adsabs.harvard.edu/abs/2005ApJ...622..772C 622, 772

    2005 doi

  17. [28]

    Charlot S., Bruzual G., 2007, in press

    2007

  18. [29]

    Chen C.-C., et al., 2016, @doi [ ] 10.3847/0004-637X/831/1/91 , https://ui.adsabs.harvard.edu/abs/2016ApJ...831...91C 831, 91

    2016 doi

  19. [30]

    K., et al., 2021, @doi [ ] 10.1093/mnras/stab467 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.503.2622C 503, 2622

    Cochrane R. K., et al., 2021, @doi [ ] 10.1093/mnras/stab467 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.503.2622C 503, 2622

    2021 doi

  20. [31]

    K., Hayward C

    Cochrane R. K., Hayward C. C., Angl \'e s-Alc \'a zar D., Somerville R. S., 2023a, @doi [ ] 10.1093/mnras/stac3451 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.518.5522C 518, 5522

    doi

  21. [32]

    K., et al., 2023b, @doi [ ] 10.1093/mnras/stad1602 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.523.6082C 523, 6082

    Cochrane R. K., et al., 2023b, @doi [ ] 10.1093/mnras/stad1602 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.523.6082C 523, 6082

    doi

  22. [33]

    K., Angl \'e s-Alc \'a zar D., Cullen F., Hayward C

    Cochrane R. K., Angl \'e s-Alc \'a zar D., Cullen F., Hayward C. C., 2024, @doi [ ] 10.3847/1538-4357/ad02f8 , https://ui.adsabs.harvard.edu/abs/2024ApJ...961...37C 961, 37

    2024 doi

  23. [34]

    G., Baugh C

    Cole S., Lacey C. G., Baugh C. M., Frenk C. S., 2000, @doi [ ] 10.1046/j.1365-8711.2000.03879.x , https://ui.adsabs.harvard.edu/abs/2000MNRAS.319..168C 319, 168

    2000

  24. [35]

    E., White M., 2009, @doi [ ] 10.1088/0004-637X/699/1/486 , https://ui.adsabs.harvard.edu/abs/2009ApJ...699..486C 699, 486

    Conroy C., Gunn J. E., White M., 2009, @doi [ ] 10.1088/0004-637X/699/1/486 , https://ui.adsabs.harvard.edu/abs/2009ApJ...699..486C 699, 486

    2009 doi

  25. [36]

    R., Casey C

    Cooper O. R., Casey C. M., Zavala J. A., Champagne J. B., da Cunha E., Long A. S., Spilker J. S., Staguhn J., 2022, @doi [ ] 10.3847/1538-4357/ac616d , https://ui.adsabs.harvard.edu/abs/2022ApJ...930...32C 930, 32

    2022 doi

  26. [37]

    L., Barger A

    Cowie L. L., Barger A. J., Wang W. H., Williams J. P., 2009, @doi [ ] 10.1088/0004-637X/697/2/L122 , https://ui.adsabs.harvard.edu/abs/2009ApJ...697L.122C 697, L122

    2009 doi

  27. [38]

    I., Lacey C

    Cowley W. I., Lacey C. G., Baugh C. M., Cole S., Frenk C. S., Lagos C. d. P., 2019, @doi [ ] 10.1093/mnras/stz1398 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.487.3082C 487, 3082

    2019 doi

  28. [40]

    Daddi E., et al., 2009, @doi [ ] 10.1088/0004-637X/694/2/1517 , https://ui.adsabs.harvard.edu/abs/2009ApJ...694.1517D 694, 1517

    2009 doi

  29. [41]

    Daddi E., et al., 2010, @doi [ ] 10.1088/0004-637X/713/1/686 , https://ui.adsabs.harvard.edu/abs/2010ApJ...713..686D 713, 686

    2010 doi

  30. [42]

    Dannerbauer H., et al., 2014, @doi [ ] 10.1051/0004-6361/201423771 , https://ui.adsabs.harvard.edu/abs/2014A&A...570A..55D 570, A55

    2014 doi

  31. [43]

    H., Appleby S., 2019, @doi [ ] 10.1093/mnras/stz937 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.486.2827D 486, 2827

    Dav \'e R., Angl \'e s-Alc \'a zar D., Narayanan D., Li Q., Rafieferantsoa M. H., Appleby S., 2019, @doi [ ] 10.1093/mnras/stz937 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.486.2827D 486, 2827

    2019 doi

  32. [44]

    Davidzon I., et al., 2017, @doi [ ] 10.1051/0004-6361/201730419 , https://ui.adsabs.harvard.edu/abs/2017A&A...605A..70D 605, A70

    2017 doi

  33. [45]

    De Lucia G., Fontanot F., Xie L., Hirschmann M., 2024, @doi [ ] 10.1051/0004-6361/202349045 , https://ui.adsabs.harvard.edu/abs/2024A&A...687A..68D 687, A68

    2024 doi

  34. [46]

    Downes D., et al., 1999, @doi [ ] 10.48550/arXiv.astro-ph/9907139 , https://ui.adsabs.harvard.edu/abs/1999A&A...347..809D 347, 809

    1999 doi arXiv

  35. [47]

    e U., et al., 2020, @doi [ ] 10.1093/mnras/staa769 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.3828D 494, 3828

    Dudzevi c i \= u t \. e U., et al., 2020, @doi [ ] 10.1093/mnras/staa769 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.3828D 494, 3828

    2020 doi

  36. [48]

    S., et al., 2004, @doi [ ] 10.1111/j.1365-2966.2004.07700.x , https://ui.adsabs.harvard.edu/abs/2004MNRAS.350..769D 350, 769

    Dunlop J. S., et al., 2004, @doi [ ] 10.1111/j.1365-2966.2004.07700.x , https://ui.adsabs.harvard.edu/abs/2004MNRAS.350..769D 350, 769

    2004

  37. [49]

    S., et al., 2017, @doi [ ] 10.1093/mnras/stw3088 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.466..861D 466, 861

    Dunlop J. S., et al., 2017, @doi [ ] 10.1093/mnras/stw3088 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.466..861D 466, 861

    2017 doi

  38. [50]

    R., Hammer F., Le F \`e vre O., Crampton D., 1999, @doi [ ] 10.1086/307069 , https://ui.adsabs.harvard.edu/abs/1999ApJ...515..518E 515, 518

    Eales S., Lilly S., Gear W., Dunne L., Bond J. R., Hammer F., Le F \`e vre O., Crampton D., 1999, @doi [ ] 10.1086/307069 , https://ui.adsabs.harvard.edu/abs/1999ApJ...515..518E 515, 518

    1999 doi

  39. [51]

    Fontanot F., Monaco P., Silva L., Grazian A., 2007, @doi [ ] 10.1111/j.1365-2966.2007.12449.x , https://ui.adsabs.harvard.edu/abs/2007MNRAS.382..903F 382, 903

    2007

  40. [52]

    Fu J., et al., 2013, @doi [ ] 10.1093/mnras/stt1117 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.434.1531F 434, 1531

    2013 doi

  41. [53]

    F., Weiss A., Wardlow J., Myers A

    Garc \' a-Vergara C., Hodge J., Hennawi J. F., Weiss A., Wardlow J., Myers A. D., Hickox R., 2020, @doi [ ] 10.3847/1538-4357/abbdfe , https://ui.adsabs.harvard.edu/abs/2020ApJ...904....2G 904, 2

    2020 doi

  42. [54]

    E., et al., 2017, @doi [ ] 10.1093/mnras/stw2721 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.465.1789G 465, 1789

    Geach J. E., et al., 2017, @doi [ ] 10.1093/mnras/stw2721 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.465.1789G 465, 1789

    2017 doi

  43. [55]

    Genel S., et al., 2014, @doi [ ] 10.1093/mnras/stu1654 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.445..175G 445, 175

    2014 doi

  44. [56]

    Girelli G., Bolzonella M., Cimatti A., 2019, @doi [ ] 10.1051/0004-6361/201834547 , https://ui.adsabs.harvard.edu/abs/2019A&A...632A..80G 632, A80

    2019 doi

  45. [57]

    Gouin C., Aghanim N., Dole H., Polletta M., Park C., 2022, @doi [ ] 10.1051/0004-6361/202243677 , https://ui.adsabs.harvard.edu/abs/2022A&A...664A.155G 664, A155

    2022 doi

  46. [58]

    Gould K. M. L., et al., 2023, @doi [ ] 10.3847/1538-3881/accadc , https://ui.adsabs.harvard.edu/abs/2023AJ....165..248G 165, 248

    2023 doi

  47. [59]

    L., Lacey C

    Granato G. L., Lacey C. G., Silva L., Bressan A., Baugh C. M., Cole S., Frenk C. S., 2000, @doi [ ] 10.1086/317032 , https://ui.adsabs.harvard.edu/abs/2000ApJ...542..710G 542, 710

    2000 doi

  48. [60]

    E., Almaini O., Pearce F

    Harrold J. E., Almaini O., Pearce F. R., Yates R. M., 2024, @doi [ ] 10.1093/mnrasl/slae043 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.532L..61H 532, L61

    2024 doi

  49. [61]

    P., et al., 2011, @doi [ ] 10.1088/0004-6256/142/5/170 , https://ui.adsabs.harvard.edu/abs/2011AJ....142..170H 142, 170

    Haynes M. P., et al., 2011, @doi [ ] 10.1088/0004-6256/142/5/170 , https://ui.adsabs.harvard.edu/abs/2011AJ....142..170H 142, 170

    2011 doi

  50. [62]

    C., Kere s D., Jonsson P., Narayanan D., Cox T

    Hayward C. C., Kere s D., Jonsson P., Narayanan D., Cox T. J., Hernquist L., 2011, @doi [ ] 10.1088/0004-637X/743/2/159 , https://ui.adsabs.harvard.edu/abs/2011ApJ...743..159H 743, 159

    2011 doi

  51. [63]

    C., Narayanan D., Kere s D., Jonsson P., Hopkins P

    Hayward C. C., Narayanan D., Kere s D., Jonsson P., Hopkins P. F., Cox T. J., Hernquist L., 2013, @doi [ ] 10.1093/mnras/sts222 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.428.2529H 428, 2529

    2013 doi

  52. [64]

    C., et al., 2021, @doi [ ] 10.1093/mnras/stab246 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.2922H 502, 2922

    Hayward C. C., et al., 2021, @doi [ ] 10.1093/mnras/stab246 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.2922H 502, 2922

    2021 doi

  53. [67]

    Henriques B. M. B., White S. D. M., Thomas P. A., Angulo R. E., Guo Q., Lemson G., Springel V., 2013, @doi [ ] 10.1093/mnras/stt415 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.431.3373H 431, 3373

    2013 doi

  54. [68]

    Henriques B. M. B., White S. D. M., Thomas P. A., Angulo R., Guo Q., Lemson G., Springel V., Overzier R., 2015, @doi [ ] 10.1093/mnras/stv705 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.451.2663H 451, 2663

    2015 doi

  55. [69]

    Henriques B. M. B., Yates R. M., Fu J., Guo Q., Kauffmann G., Srisawat C., Thomas P. A., White S. D. M., 2020, @doi [ ] 10.1093/mnras/stz3233 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.491.5795H 491, 5795

    2020 doi

  56. [70]

    Herard-Demanche T., et al., 2025, @doi [ ] 10.1093/mnras/staf030 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.537..788H 537, 788

    2025 doi

  57. [71]

    Hill R., Polletta M. d. C., Bethermin M., Dole H., Kneissl R., Scott D., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2412.00294 , https://ui.adsabs.harvard.edu/abs/2024arXiv241200294H p. arXiv:2412.00294

    2024 doi arXiv

  58. [72]

    Hirschmann M., Dolag K., Saro A., Bachmann L., Borgani S., Burkert A., 2014, @doi [ ] 10.1093/mnras/stu1023 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.442.2304H 442, 2304

    2014 doi

  59. [73]

    A., da Cunha E., 2020, @doi [Royal Society Open Science] 10.1098/rsos.200556 , https://ui.adsabs.harvard.edu/abs/2020RSOS....700556H 7, 200556

    Hodge J. A., da Cunha E., 2020, @doi [Royal Society Open Science] 10.1098/rsos.200556 , https://ui.adsabs.harvard.edu/abs/2020RSOS....700556H 7, 200556

    2020 doi

  60. [74]

    T., Rennehan D., Kobayashi C., Loubser S

    Hough R. T., Rennehan D., Kobayashi C., Loubser S. I., Dav \'e R., Babul A., Cui W., 2023, @doi [ ] 10.1093/mnras/stad2394 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.525.1061H 525, 1061

    2023 doi

  61. [75]

    T., Shao Z., Cui W., Loubser S

    Hough R. T., Shao Z., Cui W., Loubser S. I., Babul A., Dav \'e R., Rennehan D., Kobayashi C., 2024, @doi [ ] 10.1093/mnras/stae1435 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.532..476H 532, 476

    2024 doi

  62. [76]

    H., et al., 1998, @doi [ ] 10.1038/28328 , https://ui.adsabs.harvard.edu/abs/1998Natur.394..241H 394, 241

    Hughes D. H., et al., 1998, @doi [ ] 10.1038/28328 , https://ui.adsabs.harvard.edu/abs/1998Natur.394..241H 394, 241

    1998 doi

  63. [77]

    Ilbert O., et al., 2013, @doi [ ] 10.1051/0004-6361/201321100 , https://ui.adsabs.harvard.edu/abs/2013A&A...556A..55I 556, A55

    2013 doi

  64. [78]

    Izquierdo-Villalba D., Bonoli S., Dotti M., Sesana A., Rosas-Guevara Y., Spinoso D., 2020, @doi [ ] 10.1093/mnras/staa1399 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.495.4681I 495, 4681

    2020 doi

  65. [79]

    Izquierdo-Villalba D., Sesana A., Bonoli S., Colpi M., 2022, @doi [ ] 10.1093/mnras/stab3239 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.3488I 509, 3488

    2022 doi

  66. [80]

    G., Haynes M

    Jones M. G., Haynes M. P., Giovanelli R., Moorman C., 2018, @doi [ ] 10.1093/mnras/sty521 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477....2J 477, 2

    2018 doi

  67. [81]

    C., Remus R.-S., Seidel B., Valenzuela L

    Kimmig L. C., Remus R.-S., Seidel B., Valenzuela L. M., Dolag K., Burkert A., 2025, @doi [ ] 10.3847/1538-4357/ad9472 , https://ui.adsabs.harvard.edu/abs/2025ApJ...979...15K 979, 15

    2025 doi

  68. [82]

    Kugel R., et al., 2023, @doi [ ] 10.1093/mnras/stad2540 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.6103K 526, 6103

    2023 doi

  69. [83]

    arXiv:2501.19327

    Kumar A., et al., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2501.19327 , https://ui.adsabs.harvard.edu/abs/2025arXiv250119327K p. arXiv:2501.19327

    2025 doi arXiv

  70. [84]

    G., et al., 2016, @doi [ ] 10.1093/mnras/stw1888 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.462.3854L 462, 3854

    Lacey C. G., et al., 2016, @doi [ ] 10.1093/mnras/stw1888 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.462.3854L 462, 3854

    2016 doi

  71. [85]

    Lagos C. d. P., Tobar R. J., Robotham A. S. G., Obreschkow D., Mitchell P. D., Power C., Elahi P. J., 2018, @doi [ ] 10.1093/mnras/sty2440 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.481.3573L 481, 3573

    2018 doi

  72. [86]

    Lagos C. d. P., et al., 2019, @doi [ ] 10.1093/mnras/stz2427 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.489.4196L 489, 4196

    2019 doi

  73. [87]

    Lagos C. d. P., et al., 2024, @doi [ ] 10.1093/mnras/stae1024 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.531.3551L 531, 3551

    2024 doi

  74. [88]

    Lagos C. d. P., et al., 2025, @doi [ ] 10.1093/mnras/stae2626 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.536.2324L 536, 2324

    2025 doi

  75. [89]

    Laigle C., et al., 2016, @doi [ ] 10.3847/0067-0049/224/2/24 , https://ui.adsabs.harvard.edu/abs/2016ApJS..224...24L 224, 24

    2016 doi

  76. [90]

    G., Franx M., Whitaker K

    Leja J., van Dokkum P. G., Franx M., Whitaker K. E., 2015, @doi [ ] 10.1088/0004-637X/798/2/115 , https://ui.adsabs.harvard.edu/abs/2015ApJ...798..115L 798, 115

    2015 doi

  77. [91]

    D., Conroy C., van Dokkum P

    Leja J., Johnson B. D., Conroy C., van Dokkum P. G., Byler N., 2017, @doi [ ] 10.3847/1538-4357/aa5ffe , https://ui.adsabs.harvard.edu/abs/2017ApJ...837..170L 837, 170

    2017 doi

  78. [92]

    C., Johnson B

    Leja J., Carnall A. C., Johnson B. D., Conroy C., Speagle J. S., 2019a, @doi [ ] 10.3847/1538-4357/ab133c , https://ui.adsabs.harvard.edu/abs/2019ApJ...876....3L 876, 3

    doi

  79. [93]

    Leja J., et al., 2019b, @doi [ ] 10.3847/1538-4357/ab1d5a , https://ui.adsabs.harvard.edu/abs/2019ApJ...877..140L 877, 140

    doi

  80. [94]

    S., Johnson B

    Leja J., Speagle J. S., Johnson B. D., Conroy C., van Dokkum P., Franx M., 2020, @doi [ ] 10.3847/1538-4357/ab7e27 , https://ui.adsabs.harvard.edu/abs/2020ApJ...893..111L 893, 111

    2020 doi

  81. [95]

    Leja J., et al., 2022, @doi [ ] 10.3847/1538-4357/ac887d , https://ui.adsabs.harvard.edu/abs/2022ApJ...936..165L 936, 165

    2022 doi

  82. [96]

    K., et al., 2020, @doi [ ] 10.3847/1538-4357/aba044 , https://ui.adsabs.harvard.edu/abs/2020ApJ...899...58L 899, 58

    Leslie S. K., et al., 2020, @doi [ ] 10.3847/1538-4357/aba044 , https://ui.adsabs.harvard.edu/abs/2020ApJ...899...58L 899, 58

    2020 doi

  83. [98]

    Lim C.-F., et al., 2020, @doi [ ] 10.3847/1538-4357/ab8eaf , https://ui.adsabs.harvard.edu/abs/2020ApJ...895..104L 895, 104

    2020 doi

  84. [99]

    S., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2408.14546 , https://ui.adsabs.harvard.edu/abs/2024arXiv240814546L p

    Long A. S., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2408.14546 , https://ui.adsabs.harvard.edu/abs/2024arXiv240814546L p. arXiv:2408.14546

    2024 doi arXiv

  85. [100]

    C., Geach J

    Lovell C. C., Geach J. E., Dav \'e R., Narayanan D., Li Q., 2021, @doi [ ] 10.1093/mnras/staa4043 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.502..772L 502, 772

    2021 doi

  86. [101]

    J., Weinberg M

    Lu Y., Mo H. J., Weinberg M. D., Katz N., 2011, @doi [ ] 10.1111/j.1365-2966.2011.19170.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.416.1949L 416, 1949

    2011

  87. [102]

    Lu Y., et al., 2014, @doi [ ] 10.1088/0004-637X/795/2/123 , https://ui.adsabs.harvard.edu/abs/2014ApJ...795..123L 795, 123

    2014 doi

  88. [103]

    Madau P., Dickinson M., 2014, @doi [ ] 10.1146/annurev-astro-081811-125615 , https://ui.adsabs.harvard.edu/abs/2014ARA&A..52..415M 52, 415

    2014 doi

  89. [104]

    M., et al., 2022, @doi [ ] 10.3847/1538-4357/ac366a , https://ui.adsabs.harvard.edu/abs/2022ApJ...925...23M 925, 23

    Manning S. M., et al., 2022, @doi [ ] 10.3847/1538-4357/ac366a , https://ui.adsabs.harvard.edu/abs/2022ApJ...925...23M 925, 23

    2022 doi

  90. [106]

    Marinacci F., et al., 2018, @doi [MNRAS] 10.1093/mnras/sty2206 , 480, 5113

    2018 doi

  91. [107]

    P., et al., 2018, @doi [ ] 10.1038/nature24629 , https://ui.adsabs.harvard.edu/abs/2018Natur.553...51M 553, 51

    Marrone D. P., et al., 2018, @doi [ ] 10.1038/nature24629 , https://ui.adsabs.harvard.edu/abs/2018Natur.553...51M 553, 51

    2018 doi

  92. [108]

    McAlpine S., et al., 2016, @doi [Astronomy and Computing] 10.1016/j.ascom.2016.02.004 , https://ui.adsabs.harvard.edu/abs/2016A&C....15...72M 15, 72

    2016 doi

  93. [109]

    McAlpine S., et al., 2019, @doi [ ] 10.1093/mnras/stz1692 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.2440M 488, 2440

    2019 doi

  94. [110]

    Merlin E., et al., 2019, @doi [ ] 10.1093/mnras/stz2615 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.3309M 490, 3309

    2019 doi

  95. [111]

    J., et al., 2017, @doi [ ] 10.1093/mnras/stx861 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.469..492M 469, 492

    Micha owski M. J., et al., 2017, @doi [ ] 10.1093/mnras/stx861 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.469..492M 469, 492

    2017 doi

  96. [112]

    B., et al., 2018, @doi [ ] 10.1038/s41586-018-0025-2 , https://ui.adsabs.harvard.edu/abs/2018Natur.556..469M 556, 469

    Miller T. B., et al., 2018, @doi [ ] 10.1038/s41586-018-0025-2 , https://ui.adsabs.harvard.edu/abs/2018Natur.556..469M 556, 469

    2018 doi

  97. [113]

    G., Yates R

    Murphy G. G., Yates R. M., Mohamed S. S., 2022, @doi [ ] 10.1093/mnras/stab3568 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.510.1945M 510, 1945

    2022 doi

  98. [114]

    Muzzin A., et al., 2013, @doi [ ] 10.1088/0004-637X/777/1/18 , https://ui.adsabs.harvard.edu/abs/2013ApJ...777...18M 777, 18

    2013 doi

  99. [115]

    P., et al., 2018, @doi [MNRAS] 10.1093/mnras/sty618 , 477, 1206

    Naiman J. P., et al., 2018, @doi [MNRAS] 10.1093/mnras/sty618 , 477, 1206

    2018 doi

  100. [116]

    Nanayakkara T., et al., 2024, @doi [Scientific Reports] 10.1038/s41598-024-52585-4 , https://ui.adsabs.harvard.edu/abs/2024NatSR..14.3724N 14, 3724

    2024 doi

  101. [117]

    Narayanan D., et al., 2021, @doi [ ] 10.3847/1538-4365/abc487 , https://ui.adsabs.harvard.edu/abs/2021ApJS..252...12N 252, 12

    2021 doi

  102. [118]

    Nelson D., et al., 2018, @doi [MNRAS] 10.1093/mnras/stx3040 , 475, 624

    2018 doi

  103. [119]

    Neri R., Downes D., Cox P., Walter F., 2014, @doi [ ] 10.1051/0004-6361/201322528 , https://ui.adsabs.harvard.edu/abs/2014A&A...562A..35N 562, A35

    2014 doi

  104. [120]

    Oteo I., et al., 2018, @doi [ ] 10.3847/1538-4357/aaa1f1 , https://ui.adsabs.harvard.edu/abs/2018ApJ...856...72O 856, 72

    2018 doi

  105. [121]

    Pillepich A., et al., 2018, @doi [ ] 10.1093/mnras/stx2656 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.473.4077P 473, 4077

    2018 doi

  106. [122]

    Planck Collaboration et al., 2014, @doi [ ] 10.1051/0004-6361/201321591 , https://ui.adsabs.harvard.edu/abs/2014A&A...571A..16P 571, A16

    2014 doi

  107. [123]

    J., Krukau A., Petkova M., Steinborn D., 2017, @doi [Astronomy and Computing] 10.1016/j.ascom.2017.05.001 , https://ui.adsabs.harvard.edu/abs/2017A&C....20...52R 20, 52

    Ragagnin A., Dolag K., Biffi V., Cadolle Bel M., Hammer N. J., Krukau A., Petkova M., Steinborn D., 2017, @doi [Astronomy and Computing] 10.1016/j.ascom.2017.05.001 , https://ui.adsabs.harvard.edu/abs/2017A&C....20...52R 20, 52

    2017 doi

  108. [124]

    R \'e my-Ruyer A., et al., 2014, @doi [ ] 10.1051/0004-6361/201322803 , https://ui.adsabs.harvard.edu/abs/2014A&A...563A..31R 563, A31

    2014 doi

  109. [125]

    A., 1999, @doi [ ] 10.1086/311905 , https://ui.adsabs.harvard.edu/abs/1999ApJ...513L...9R 513, L9

    Richards E. A., 1999, @doi [ ] 10.1086/311905 , https://ui.adsabs.harvard.edu/abs/1999ApJ...513L...9R 513, L9

    1999 doi

  110. [126]

    R., Avila-Reese V., Faber S

    Rodr \' guez-Puebla A., Primack J. R., Avila-Reese V., Faber S. M., 2017, @doi [ ] 10.1093/mnras/stx1172 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.470..651R 470, 651

    2017 doi

  111. [127]

    C., 2017, @doi [ ] 10.1093/mnras/stx2172 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.472.2462S 472, 2462

    Safarzadeh M., Lu Y., Hayward C. C., 2017, @doi [ ] 10.1093/mnras/stx2172 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.472.2462S 472, 2462

    2017 doi

  112. [128]

    Schaye J., et al., 2023, @doi [ ] 10.1093/mnras/stad2419 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.4978S 526, 4978

    2023 doi

  113. [129]

    Schreiber C., et al., 2018, @doi [ ] 10.1051/0004-6361/201833070 , https://ui.adsabs.harvard.edu/abs/2018A&A...618A..85S 618, A85

    2018 doi

  114. [130]

    Shahidi A., et al., 2020, @doi [ ] 10.3847/1538-4357/ab96c5 , https://ui.adsabs.harvard.edu/abs/2020ApJ...897...44S 897, 44

    2020 doi

  115. [131]

    Shankar F., et al., 2020, @doi [Nature Astronomy] 10.1038/s41550-019-0949-y , https://ui.adsabs.harvard.edu/abs/2020NatAs...4..282S 4, 282

    2020 doi

  116. [132]

    F., Nelson D., Hernquist L., 2015, @doi [ ] 10.1093/mnras/stv1340 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.452..575S 452, 575

    Sijacki D., Vogelsberger M., Genel S., Springel V., Torrey P., Snyder G. F., Nelson D., Hernquist L., 2015, @doi [ ] 10.1093/mnras/stv1340 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.452..575S 452, 575

    2015 doi

  117. [133]

    M., et al., 2014, @doi [ ] 10.1088/0004-637X/788/2/125 , https://ui.adsabs.harvard.edu/abs/2014ApJ...788..125S 788, 125

    Simpson J. M., et al., 2014, @doi [ ] 10.1088/0004-637X/788/2/125 , https://ui.adsabs.harvard.edu/abs/2014ApJ...788..125S 788, 125

    2014 doi

  118. [134]

    M., et al., 2017, @doi [ ] 10.3847/1538-4357/aa65d0 , https://ui.adsabs.harvard.edu/abs/2017ApJ...839...58S 839, 58

    Simpson J. M., et al., 2017, @doi [ ] 10.3847/1538-4357/aa65d0 , https://ui.adsabs.harvard.edu/abs/2017ApJ...839...58S 839, 58

    2017 doi

  119. [135]

    E., et al., 2014, @doi [ ] 10.1088/0067-0049/214/2/24 , https://ui.adsabs.harvard.edu/abs/2014ApJS..214...24S 214, 24

    Skelton R. E., et al., 2014, @doi [ ] 10.1088/0067-0049/214/2/24 , https://ui.adsabs.harvard.edu/abs/2014ApJS..214...24S 214, 24

    2014 doi

  120. [136]

    J., Blain A

    Smail I., Ivison R. J., Blain A. W., 1997, @doi [ ] 10.1086/311017 , https://ui.adsabs.harvard.edu/abs/1997ApJ...490L...5S 490, L5

    1997 doi

  121. [137]

    Smith D. J. B., Hayward C. C., Jarvis M. J., Simpson C., 2017, @doi [ ] 10.1093/mnras/stx1689 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.471.2453S 471, 2453

    2017 doi

  122. [139]

    S., Gilmore R

    Somerville R. S., Gilmore R. C., Primack J. R., Dom \' nguez A., 2012, @doi [ ] 10.1111/j.1365-2966.2012.20490.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.423.1992S 423, 1992

    2012

  123. [140]

    Spinoso D., Bonoli S., Valiante R., Schneider R., Izquierdo-Villalba D., 2023, @doi [ ] 10.1093/mnras/stac3169 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.518.4672S 518, 4672

    2023 doi

  124. [141]

    Springel V., 2005, @doi [ ] 10.1111/j.1365-2966.2005.09655.x , https://ui.adsabs.harvard.edu/abs/2005MNRAS.364.1105S 364, 1105

    2005

  125. [142]

    Springel V., White S. D. M., Tormen G., Kauffmann G., 2001, @doi [ ] 10.1046/j.1365-8711.2001.04912.x , https://ui.adsabs.harvard.edu/abs/2001MNRAS.328..726S 328, 726

    2001

  126. [143]

    Springel V., et al., 2018, @doi [MNRAS] 10.1093/mnras/stx3304 , 475, 676

    2018 doi

  127. [144]

    M., et al., 2018, @doi [ ] 10.3847/1538-4357/aac5e5 , https://ui.adsabs.harvard.edu/abs/2018ApJ...860..161S 860, 161

    Stach S. M., et al., 2018, @doi [ ] 10.3847/1538-4357/aac5e5 , https://ui.adsabs.harvard.edu/abs/2018ApJ...860..161S 860, 161

    2018 doi

  128. [145]

    M., et al., 2019, @doi [ ] 10.1093/mnras/stz1536 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.487.4648S 487, 4648

    Stach S. M., et al., 2019, @doi [ ] 10.1093/mnras/stz1536 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.487.4648S 487, 4648

    2019 doi

  129. [146]

    M., et al., 2021, @doi [ ] 10.1093/mnras/stab714 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.504..172S 504, 172

    Stach S. M., et al., 2021, @doi [ ] 10.1093/mnras/stab714 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.504..172S 504, 172

    2021 doi

  130. [147]

    Straatman C. M. S., et al., 2014, @doi [ ] 10.1088/2041-8205/783/1/L14 , https://ui.adsabs.harvard.edu/abs/2014ApJ...783L..14S 783, L14

    2014 doi

  131. [148]

    T., 2025, @doi [ ] 10.1093/mnras/staf132 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.537.1849S 537, 1849

    Szpila J., Dav \'e R., Rennehan D., Cui W., Hough R. T., 2025, @doi [ ] 10.1093/mnras/staf132 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.537.1849S 537, 1849

    2025 doi

  132. [149]

    J., et al., 2010, @doi [ ] 10.1038/nature08773 , https://ui.adsabs.harvard.edu/abs/2010Natur.463..781T 463, 781

    Tacconi L. J., et al., 2010, @doi [ ] 10.1038/nature08773 , https://ui.adsabs.harvard.edu/abs/2010Natur.463..781T 463, 781

    2010 doi

  133. [150]

    Toft S., et al., 2014, @doi [ ] 10.1088/0004-637X/782/2/68 , https://ui.adsabs.harvard.edu/abs/2014ApJ...782...68T 782, 68

    2014 doi

  134. [151]

    R., et al., 2014, @doi [ ] 10.1088/0004-637X/783/2/85 , https://ui.adsabs.harvard.edu/abs/2014ApJ...783...85T 783, 85

    Tomczak A. R., et al., 2014, @doi [ ] 10.1088/0004-637X/783/2/85 , https://ui.adsabs.harvard.edu/abs/2014ApJ...783...85T 783, 85

    2014 doi

  135. [152]

    R., et al., 2016, @doi [ ] 10.3847/0004-637X/817/2/118 , https://ui.adsabs.harvard.edu/abs/2016ApJ...817..118T 817, 118

    Tomczak A. R., et al., 2016, @doi [ ] 10.3847/0004-637X/817/2/118 , https://ui.adsabs.harvard.edu/abs/2016ApJ...817..118T 817, 118

    2016 doi

  136. [153]

    W., et al., 2017, @doi [ ] 10.1093/mnras/stx1051 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.470..771T 470, 771

    Trayford J. W., et al., 2017, @doi [ ] 10.1093/mnras/stx1051 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.470..771T 470, 771

    2017 doi

  137. [154]

    Tucci M., Volonteri M., 2017, @doi [ ] 10.1051/0004-6361/201628419 , https://ui.adsabs.harvard.edu/abs/2017A&A...600A..64T 600, A64

    2017 doi

  138. [155]

    Valentino F., et al., 2020, @doi [ ] 10.3847/1538-4357/ab64dc , https://ui.adsabs.harvard.edu/abs/2020ApJ...889...93V 889, 93

    2020 doi

  139. [156]

    Valentino F., et al., 2023, @doi [ ] 10.3847/1538-4357/acbefa , https://ui.adsabs.harvard.edu/abs/2023ApJ...947...20V 947, 20

    2023 doi

  140. [157]

    Vani A., Ayromlou M., Kauffmann G., Springel V., 2025, @doi [ ] 10.1093/mnras/stae2625 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.536..777V 536, 777

    2025 doi

  141. [158]

    P., Graham A

    Vika M., Driver S. P., Graham A. W., Liske J., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15544.x , https://ui.adsabs.harvard.edu/abs/2009MNRAS.400.1451V 400, 1451

    2009

  142. [159]

    Vogelsberger M., et al., 2014, @doi [ ] 10.1093/mnras/stu1536 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.444.1518V 444, 1518

    2014 doi

  143. [160]

    H., Aretxaga I., Chapin E

    Wagg J., Hughes D. H., Aretxaga I., Chapin E. L., Dunlop J. S., Gazta \ n aga E., Devlin M., 2007, @doi [ ] 10.1111/j.1365-2966.2006.11352.x , https://ui.adsabs.harvard.edu/abs/2007MNRAS.375..745W 375, 745

    2007

  144. [161]

    Walter F., et al., 2012, @doi [ ] 10.1038/nature11073 , https://ui.adsabs.harvard.edu/abs/2012Natur.486..233W 486, 233

    2012 doi

  145. [162]

    Wang G. C. P., et al., 2021, @doi [ ] 10.1093/mnras/stab2800 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.508.3754W 508, 3754

    2021 doi

  146. [163]

    R., et al., 2023, @doi [ ] 10.1051/0004-6361/202245581 , https://ui.adsabs.harvard.edu/abs/2023A&A...677A.184W 677, A184

    Weaver J. R., et al., 2023, @doi [ ] 10.1051/0004-6361/202245581 , https://ui.adsabs.harvard.edu/abs/2023A&A...677A.184W 677, A184

    2023 doi

  147. [164]

    M., Henriques B., Thomas P

    Yates R. M., Henriques B., Thomas P. A., Kauffmann G., Johansson J., White S. D. M., 2013, @doi [ ] 10.1093/mnras/stt1542 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.435.3500Y 435, 3500

    2013 doi

  148. [165]

    M., Henriques B

    Yates R. M., Henriques B. M. B., Fu J., Kauffmann G., Thomas P. A., Guo Q., White S. D. M., Schady P., 2021, @doi [ ] 10.1093/mnras/stab741 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.503.4474Y 503, 4474

    2021 doi

  149. [166]

    M., Hendriks D., Vijayan A

    Yates R. M., Hendriks D., Vijayan A. P., Izzard R. G., Thomas P. A., Das P., 2024, @doi [ ] 10.1093/mnras/stad3419 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.6292Y 527, 6292

    2024 doi

  150. [167]

    A., et al., 2021, @doi [ ] 10.3847/1538-4357/abdb27 , https://ui.adsabs.harvard.edu/abs/2021ApJ...909..165Z 909, 165

    Zavala J. A., et al., 2021, @doi [ ] 10.3847/1538-4357/abdb27 , https://ui.adsabs.harvard.edu/abs/2021ApJ...909..165Z 909, 165

    2021 doi

  151. [168]

    A., Meyer M

    Zwaan M. A., Meyer M. J., Staveley-Smith L., Webster R. L., 2005, @doi [ ] 10.1111/j.1745-3933.2005.00029.x , https://ui.adsabs.harvard.edu/abs/2005MNRAS.359L..30Z 359, L30

    2005

  152. [169]

    da Cunha E., et al., 2015, @doi [ ] 10.1088/0004-637X/806/1/110 , https://ui.adsabs.harvard.edu/abs/2015ApJ...806..110D 806, 110

    2015 doi

Pith tools

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