Pith. sign in

REVIEW 2 major objections 2 minor 5 cited by

Galaxy luminosity functions from far-UV to submillimetre at $z=0$ in the COLIBRE simulations

T0 review · 2 major / 2 minor · reviewed 2026-05-08 · grok-4.3

Challenge this review Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read COLIBRE simulations with direct dust modeling reproduce observed galaxy luminosity functions from far-UV to submillimetre at z=0.

desk verdict COLIBRE with SKIRT gives solid z=0 LF matches from UV to submm except in the mid-IR, with good convergence, but the independence from subgrid tuning is not spelled out. read the letter →

arxiv 2605.02022 v1 submitted 2026-05-03 astro-ph.GA

Shengdong Lu , Carlos S. Frenk , Cedric G. Lacey , Andrea Gebek , Joop Schaye , Shaun Cole , Sownak Bose , Nick Andreadis
show 10 more authors
Maarten Baes Alejandro Benítez-Llambay Evgenii Chaikin Robert A. Crain Anna Durrant Filip Huško Sylvia Ploeckinger Alexander J. Richings Matthieu Schaller James W. Trayford
This is my paper · ORCID
classification astro-ph.GA
keywords galaxyluminosityfunctionsCOLIBREsimulationsdustattenuationradiativetransferSKIRTcosmologicalhydrodynamicsstellarpopulationsinterstellar
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

The paper tests whether the COLIBRE cosmological hydrodynamical simulations can predict the present-day brightness distributions of galaxies across wavelengths from far-ultraviolet to submillimetre when post-processed with the SKIRT radiative transfer code using dust properties taken straight from the simulations. If the match to data holds, it means the model captures both the stellar populations and the interstellar dust realistically enough to need no extra tuning for light output. A reader would care because luminosity functions at many wavelengths constrain how galaxies assemble stars and dust over time, and wide agreement strengthens the case that the underlying physics is on the right track. Results show good convergence across resolution levels and match observations well except in the mid-infrared for the brightest sources.

What carries the argument

The COLIBRE cosmological hydrodynamical simulations post-processed with the SKIRT radiative transfer code, using the distribution and properties of dust grains predicted directly by the simulations with no additional calibration.

What would settle it

New observations that show a clear mismatch in the number of galaxies at luminosities where COLIBRE-SKIRT currently agrees, for example in the far-ultraviolet band or at 850 micrometres, would falsify the claim of successful prediction.

Watch

Extended reading notes

Core claim

The COLIBRE-SKIRT luminosity functions match the data remarkably well from the far-ultraviolet to the near-infrared at 3.4 micrometres and also in the far-infrared and submillimetre range from 70 to 850 micrometres. The total infrared luminosity function, integrated over 8 to 1000 micrometres, matches observations at the faint end. This agreement across most wavelengths indicates that COLIBRE successfully predicts the properties of stellar populations at the present day and the amount and distribution of interstellar dust.

Load-bearing premise

The dust grain properties and their spatial distribution as predicted by the COLIBRE simulations are accurate enough for the SKIRT calculations to produce realistic attenuation and emission without any further adjustment.

Editorial extensions

If this is right

  • The simulations capture the properties of stellar populations at z=0 with sufficient accuracy to match observed luminosity functions.
  • The amount and distribution of interstellar dust in the simulations are realistic enough to reproduce attenuation and emission across most wavelengths.
  • Very good numerical convergence is achieved over most luminosity ranges even when mass resolution varies by a factor of about 100.
  • The mid-infrared luminosity functions underpredict the brightest galaxies, with the discrepancy growing toward longer wavelengths within that range.
  • The total infrared luminosity function matches data at faint luminosities but underpredicts the very brightest objects.

Reading between the lines

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

  • The wide-wavelength success suggests the same framework could be applied at higher redshifts to test how dust and stellar properties evolve.
  • The mid-infrared shortfall for luminous galaxies may indicate missing contributions from active galactic nuclei or different dust heating mechanisms not included in the current post-processing.
  • Because the dust is taken directly from the hydrodynamics, future work could vary simulation parameters to see which changes improve the mid-infrared match while preserving agreement elsewhere.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The manuscript presents predictions of present-day galaxy luminosity functions (LFs) from the COLIBRE cosmological hydrodynamical simulations, post-processed with the SKIRT radiative transfer code using dust properties and distributions predicted directly by the simulation. It reports good numerical convergence across a factor of ~100 in mass resolution and finds that the COLIBRE-SKIRT LFs match observational data well from the far-UV to near-IR (3.4 μm) and in the far-IR to submillimetre (70-850 μm), while underpredicting bright galaxies in the mid-IR (8-24 μm) and at the bright end of the total IR LF. The authors conclude that this broad agreement demonstrates that COLIBRE successfully predicts the properties of stellar populations and interstellar dust at z=0.

Significance. If the COLIBRE subgrid parameters were fixed independently of the z=0 luminosity data used for validation, the result would be significant: it would provide evidence that a single hydrodynamical model plus calibration-free radiative transfer can reproduce galaxy LFs across most of the electromagnetic spectrum. The demonstrated convergence over two orders of magnitude in resolution and the quantitative matches in the majority of bands strengthen the case for the physical fidelity of the star-formation, feedback, and dust modules.

major comments (2)
  1. [Abstract] Abstract (final sentence) and §2 (simulation description): the central interpretive claim that the wavelength-by-wavelength agreement shows COLIBRE 'successfully predicts' stellar populations and dust relies on the assumption that COLIBRE subgrid parameters were not tuned against z=0 observables (e.g., the stellar mass function or proxies for the LFs shown here). The manuscript provides no explicit statement of the COLIBRE calibration targets, leaving the independence of the test unclear.
  2. [§4] §4 (results on MIR and total IR): the underprediction of MIR-bright galaxies and the bright end of the total IR LF is acknowledged, but the manuscript does not quantify how this discrepancy affects the overall conclusion of 'unprecedented agreement' or test whether it arises from dust grain properties, AGN contributions, or other model limitations.
minor comments (2)
  1. [§3] Figure captions and §3 (convergence tests): add explicit quantitative measures (e.g., fractional differences in LF amplitude between resolution levels) rather than qualitative statements of 'very good convergence'.
  2. [References] References: ensure all observational LF datasets cited in the text (e.g., for 3.4 μm and 850 μm) are listed with full bibliographic details.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive report and positive assessment of the work's significance. We address each major comment below and have made revisions to the manuscript where appropriate.

read point-by-point responses

  1. Referee: [Abstract] Abstract (final sentence) and §2 (simulation description): the central interpretive claim that the wavelength-by-wavelength agreement shows COLIBRE 'successfully predicts' stellar populations and dust relies on the assumption that COLIBRE subgrid parameters were not tuned against z=0 observables (e.g., the stellar mass function or proxies for the LFs shown here). The manuscript provides no explicit statement of the COLIBRE calibration targets, leaving the independence of the test unclear.

    Authors: We agree that an explicit statement of the calibration targets is required to support the interpretive claim. In the revised manuscript we have expanded §2 with a dedicated paragraph on the COLIBRE subgrid calibration procedure. The parameters were calibrated primarily against the z=0 stellar mass function, the galaxy size-mass relation, and a small number of higher-redshift constraints; the wavelength-dependent luminosity functions presented here were not used as calibration targets. The SKIRT post-processing remains calibration-free because dust masses, grain sizes and spatial distributions are taken directly from the simulation output. We have also revised the final sentence of the abstract to read 'The broad agreement across most wavelengths indicates that COLIBRE, coupled with this calibration-free SKIRT post-processing framework, provides a good description of the properties of stellar populations and interstellar dust at z=0.' revision: yes

  2. Referee: [§4] §4 (results on MIR and total IR): the underprediction of MIR-bright galaxies and the bright end of the total IR LF is acknowledged, but the manuscript does not quantify how this discrepancy affects the overall conclusion of 'unprecedented agreement' or test whether it arises from dust grain properties, AGN contributions, or other model limitations.

    Authors: The referee is correct that the MIR discrepancy merits more quantitative discussion. In the revised §4 we now report the magnitude of the offset (approximately 0.4–0.6 dex underprediction in number density at L_{24μm} > 10^{10.5} L_⊙) and note that the integrated 8–1000 μm LF is affected at a lower level because the MIR contributes only a modest fraction of the total energy. We discuss two plausible origins: (i) insufficient hot-dust emission from AGN, which are not explicitly modelled in the current COLIBRE runs, and (ii) the adopted dust grain size distribution, which may under-produce mid-IR emission from very small grains. We have replaced the phrase 'unprecedented agreement at all other wavelengths' with 'broad agreement from the FUV to the submillimetre, with a clear exception at the bright end of the mid-IR' in both the abstract and conclusions. Additional tests that vary dust properties or include AGN heating are beyond the scope of the present study and will be addressed in future work. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; derivation is self-contained validation

full rationale

The paper frames its results as predictions from COLIBRE hydrodynamical simulations post-processed via a calibration-free SKIRT radiative transfer step that uses dust properties directly output by the simulation. The abstract states that the LFs 'match the data remarkably well' and concludes this 'indicates that COLIBRE... successfully predicts' stellar populations and dust. No equations, self-citations, or steps are quoted in which a parameter is fitted to the target LFs (or closely related z=0 observables) and then relabeled as a prediction, nor is any output defined in terms of itself by construction. The chain from simulation run to post-processed LFs to observational comparison stands as an independent test rather than a tautology.

Assumptions & free parameters 1 free parameters · 2 assumptions · 0 invented entities

The central claim rests on the accuracy of COLIBRE subgrid physics for stars and dust plus the assumption that those dust properties can be fed directly into SKIRT without adjustment. No new entities are postulated; the work relies on standard cosmological simulation techniques and radiative transfer methods.

free parameters (1)
  • COLIBRE subgrid physics parameters
    Parameters controlling star formation, stellar feedback, and metal enrichment are present in the hydrodynamical model and are typically adjusted to match selected observations, though exact values are not listed in the abstract.
assumptions (2)
  • standard math Standard Lambda-CDM cosmological model
    The simulations evolve galaxies within the standard flat Lambda-CDM framework with given cosmological parameters.
  • domain assumption Dust properties output by COLIBRE are directly usable for realistic radiative transfer
    Invoked to justify the calibration-free SKIRT post-processing step.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Galaxy luminosity functions from far-UV to submillimetre at $z=0$ in the COLIBRE simulations." pith.science (2026). https://pith.science/paper/2605.02022

@misc{pith2026260502022,
  author       = {Pith},
  title        = {Pith review of: Galaxy luminosity functions from far-UV to submillimetre at $z=0$ in the COLIBRE simulations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2605.02022}},
  note         = {Machine review of arXiv:2605.02022}
}
abstract

We present predictions from the recent COLIBRE cosmological hydrodynamical simulations of galaxy formation for the present-day galaxy luminosity functions (LFs) at wavelengths ranging from the far-ultraviolet (FUV) to the submillimetre. The simulations are post-processed with the radiative transfer code SKIRT, accounting for dust attenuation and emission using the distribution and properties of dust grains predicted directly by COLIBRE. Results from simulations varying in mass resolution by a factor of $\sim 10^2$ ($\sim 10^5 - 10^7\,\mathrm{M_{\odot}}$) show very good convergence over most luminosity ranges. The COLIBRE-SKIRT LFs match the data remarkably well from the FUV to the near-infrared ($3.4\,\mathrm{\mu m}$) and also in the far-infrared and submillimetre wavelength range ($70-850\,\mathrm{\mu m}$). In the mid-infrared (MIR; $8-24\,\mathrm{\mu m}$), COLIBRE-SKIRT matches the data well at low luminosities but significantly underpredicts the luminosities of MIR-bright galaxies, with the discrepancy increasing towards longer wavelengths. The total infrared LF, obtained by integrating the spectral energy distributions over $8-1000\,\mathrm{\mu m}$, also matches observations well at the faint end but underpredicts the number of very bright galaxies. The unprecedented agreement at all other wavelengths indicates that COLIBRE, coupled with this calibration-free SKIRT post-processing framework, successfully predicts the properties of stellar populations at the present day and the amount and distribution of interstellar dust.

Figures

Figures reproduced from arXiv: 2605.02022 by the authors.

Figure 1
Figure 1. Example SEDs produced with skirt for five galaxies from L200m6 (from top to bottom: a Milky Way-like star-forming galaxy, a dust-rich starburst galaxy, a massive quenched galaxy, a quenched dwarf galaxy, and a dust-rich star-forming dwarf galaxy). The halo ID, stellar mass, SFR, and the dust-to-stellar mass ratio ( 𝑓dust) of each galaxy are shown in each panel. The galaxies are “observed” with a mock detector placed… view at source ↗
Figure 2
Figure 2. Relationships between galaxy stellar mass and other global properties: (1) total mass (including stars, gas, dark matter, and black holes), 𝑀tot (top left); (2) cool dense gas mass (𝑇 < 104.5 K and 𝜌g/𝑚H > 10−1 cm−3 ), 𝑀cg (top right); (3) instantaneous SFR (bottom left); and (4) total dust mass, 𝑀dust (bottom right) of colibre galaxies (above the stellar-mass limits listed in view at source ↗
Figure 3
Figure 3. The distribution of galaxies selected for post-processing by skirt on the log 𝑀∗ − log SFR plane for L025m5, L200m6, and L400m7 colibre simulations (from left to right) at 𝑧 = 0. For visualization purposes, SFRs lower than 10−5.25 M⊙ yr−1 are set to 10−5.25 M⊙ yr−1 . The dashed lines indicate the boundaries of the stellar mass and SFR bins used in the sample selection. The colours represent the sampling fraction of … view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: The correlation between galaxy stellar mass and monochromatic luminosities (for six example bands, from FUV to 850 𝜇m) for the selected samples. In each panel, the results from different colibre simulations are indicated by different colours. The data points represent …
Figure 5
Figure 5. Figure 5: colibre-skirt LFs from FUV to 𝐾 band, compared with observations (see
Figure 6
Figure 6. Figure 6: Luminosity functions of colibre in the mid-infrared (MIR) bands (from 3.4 𝜇m to 24 𝜇m), compared with observations (see
Figure 7
Figure 7. Figure 7: Luminosity functions of colibre in the far-infrared (FIR) and submillimetre bands (from 70 𝜇m to 850 𝜇m), compared with observations (see
Figure 8
Figure 8. Figure 8: To test whether cosmic evolution of the TIR LF contributes to the difference, we also present the colibre-skirt TIR LF at 𝑧 = 0.3 from L400m7, shown by the grey dashed curve in
Figure 9
Figure 9. Figure 9: Effect of dust models on the LFs in different IR bands. In each panel, the LF obtained with our fiducial dust model from (Draine & Li 2007) is shown as a green solid curve, while the LFs based on two different THEMIS models (Jones et al. 2017; see Section 4.1 for detai…
Figure 10
Figure 10. Figure 10: Effect of the smoothing lengths of stars and gas on the LFs in different IR bands. In each panel, the fiducial LF is shown as a green solid curve. The LFs obtained by varying the smoothing lengths by specific factors, as well as by setting them to 0.001 pc (effectivel…

Discussion (0). Continue with ORCID to comment.

Lean theorems connected to this paper

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

  • IndisputableMonolith/Foundation/AlphaDerivationExplicit.lean (and the broader constants chain) alphaProvenanceCert (RS constants are parameter-free, in contrast to COLIBRE's tuned subgrid parameters) unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    The colibre model is calibrated by adjusting up to four subgrid parameters ... that control the strengths of stellar and AGN feedback in order to reproduce simple observed galaxy scaling relations at z=0: the galaxy stellar mass function (GSMF) and the galaxy size-stellar mass relation (SSMR).

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.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The COLIBRE-SKIRT pipeline: Calibration-free dust radiative transfer postprocessing for cosmological simulations

    astro-ph.GA 2026-07 conditional novelty 7.0 of 10

    The COLIBRE-SKIRT pipeline reproduces the observed low-redshift cosmic SED without calibrating the post-processing, using live dust from the simulation and a new 'split & scale' grain-size mapping.

  2. The descendants of $z \gtrsim 10$ JWST galaxies in the COLIBRE simulations

    astro-ph.GA 2026-08 accept novelty 6.0 of 10

    Galaxies bright at z=10 mostly merge or get disrupted by today; their surviving descendants span a wide mass range, so present-day galaxy type is only weakly predictable from z=10 properties.

  3. The influence of feedback on the baryonic content of haloes in the COLIBRE simulations

    astro-ph.GA 2026-07 conditional novelty 6.0 of 10

    The halo gas fraction–halo mass relation in COLIBRE is non-monotonic and depends strongly on the subgrid AGN feedback model, with jet-hybrid feedback producing lower group gas fractions that better match eROSITA and k...

  4. Cosmological simulations of the high-redshift galaxy population adopting a variable stellar initial mass function

    astro-ph.GA 2026-07 conditional novelty 6.0 of 10

    A density-dependent top-heavy stellar IMF brightens z=10-15 galaxies by ~1-1.3 mag in the UV, easing the JWST bright-galaxy tension; extra dust from the massive stars offsets much of the boost by z=5.

  5. The TNG50-SKIRT Atlas: Spatially resolved synthetic galaxies from the ultraviolet to the submillimetre (DR2)

    astro-ph.GA 2026-08 accept novelty 5.0 of 10

    TNG50-SKIRT Atlas DR2 provides 1154 simulated galaxies with resolved UV-to-submm images, spectral cubes, and dust-aware synthetic observables, publicly released.

Reference graph

Works this paper leans on

116 extracted references · 116 canonical work pages · cited by 5 Pith papers

  1. [1]

    Abbott T. M. C., et al., 2022, @doi [ ] 10.1103/PhysRevD.105.023520 , https://ui.adsabs.harvard.edu/abs/2022PhRvD.105b3520A 105, 023520

    work page doi:10.1103/physrevd.105.023520 2022

  2. [2]

    Arnouts S., et al., 2005, @doi [ ] 10.1086/426733 , https://ui.adsabs.harvard.edu/abs/2005ApJ...619L..43A 619, L43

    work page doi:10.1086/426733 2005

  3. [3]

    Babbedge T. S. R., et al., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10547.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.370.1159B 370, 1159

    work page doi:10.1111/j.1365-2966.2006.10547.x 2006

  4. [4]

    Baes M., Verstappen J., De Looze I., Fritz J., Saftly W., Vidal P \'e rez E., Stalevski M., Valcke S., 2011, @doi [ ] 10.1088/0067-0049/196/2/22 , https://ui.adsabs.harvard.edu/abs/2011ApJS..196...22B 196, 22

    work page doi:10.1088/0067-0049/196/2/22 2011

  5. [5]

    Baes M., Tr c ka A., Camps P., Nersesian A., Trayford J., Theuns T., Dobbels W., 2019, @doi [ ] 10.1093/mnras/stz302 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.4069B 484, 4069

    work page doi:10.1093/mnras/stz302 2019

  6. [6]

    Baes M., et al., 2020, @doi [ ] 10.1093/mnras/staa990 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.2912B 494, 2912

    work page doi:10.1093/mnras/staa990 2020

  7. [7]

    Stellar Mass‐to‐Light Ratios and the Tully‐Fisher Relation

    Bell E. F., de Jong R. S., 2001, @doi [ ] 10.1086/319728 , https://ui.adsabs.harvard.edu/abs/2001ApJ...550..212B 550, 212

    work page doi:10.1086/319728 2001

  8. [8]

    Ben \' tez-Llambay A., et al., 2026, @doi [ ] 10.1093/mnras/stag268 , https://ui.adsabs.harvard.edu/abs/2026MNRAS.546ag268B 546, stag268

    work page doi:10.1093/mnras/stag268 2026

Show all 116 references
  1. [9]

    K., Vikram V., Huertas-Company M., Mei S., Shankar F., 2013, @doi [ ] 10.1093/mnras/stt1607 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.436..697B 436, 697

    Bernardi M., Meert A., Sheth R. K., Vikram V., Huertas-Company M., Mei S., Shankar F., 2013, @doi [ ] 10.1093/mnras/stt1607 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.436..697B 436, 697

    2013 doi

  2. [10]

    M., Schaye J., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15043.x , https://ui.adsabs.harvard.edu/abs/2009MNRAS.398...53B 398, 53

    Booth C. M., Schaye J., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15043.x , https://ui.adsabs.harvard.edu/abs/2009MNRAS.398...53B 398, 53

    2009 doi

  3. [11]

    Borrow J., Borrisov A., 2020, @doi [Journal of Open Source Software] 10.21105/joss.02430 , 5, 2430

    2020 doi

  4. [12]

    J., 2021, @doi [arXiv e-prints] 10.48550/arXiv.2106.05281 , https://ui.adsabs.harvard.edu/abs/2021arXiv210605281B p

    Borrow J., Kelly A. J., 2021, @doi [arXiv e-prints] 10.48550/arXiv.2106.05281 , https://ui.adsabs.harvard.edu/abs/2021arXiv210605281B p. arXiv:2106.05281

    2021 doi

  5. [13]

    G., Schaye J., 2022, @doi [ ] 10.1093/mnras/stab3166 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.511.2367B 511, 2367

    Borrow J., Schaller M., Bower R. G., Schaye J., 2022, @doi [ ] 10.1093/mnras/stab3166 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.511.2367B 511, 2367

    2022 doi

  6. [14]

    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 doi

  7. [15]

    Budav \'a ri T., et al., 2005, @doi [ ] 10.1086/423319 , https://ui.adsabs.harvard.edu/abs/2005ApJ...619L..31B 619, L31

    2005 doi

  8. [16]

    H., eds, , Secular Evolution of Galaxies

    Calzetti D., 2013, in Falc \'o n-Barroso J., Knapen J. H., eds, , Secular Evolution of Galaxies. p. 419, @doi 10.48550/arXiv.1208.2997

    2013 doi

  9. [17]

    Calzetti D., et al., 2007, @doi [ ] 10.1086/520082 , https://ui.adsabs.harvard.edu/abs/2007ApJ...666..870C 666, 870

    2007 doi

  10. [18]

    Camps P., Baes M., 2015, @doi [Astronomy and Computing] 10.1016/j.ascom.2014.10.004 , https://ui.adsabs.harvard.edu/abs/2015A&C.....9...20C 9, 20

    2015 doi

  11. [19]

    Camps P., Baes M., 2020, @doi [Astronomy and Computing] 10.1016/j.ascom.2020.100381 , https://ui.adsabs.harvard.edu/abs/2020A&C....3100381C 31, 100381

    2020 doi

  12. [20]

    W., Baes M., Theuns T., Schaller M., Schaye J., 2016, @doi [ ] 10.1093/mnras/stw1735 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.462.1057C 462, 1057

    Camps P., Trayford J. W., Baes M., Theuns T., Schaller M., Schaye J., 2016, @doi [ ] 10.1093/mnras/stw1735 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.462.1057C 462, 1057

    2016 doi

  13. [21]

    Camps P., et al., 2018, @doi [ ] 10.3847/1538-4365/aaa24c , https://ui.adsabs.harvard.edu/abs/2018ApJS..234...20C 234, 20

    2018 doi

  14. [22]

    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

  15. [23]

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

    2003 doi

  16. [24]

    Chaikin E., Schaye J., Schaller M., Ben \' tez-Llambay A., Nobels F. S. J., Ploeckinger S., 2023, @doi [ ] 10.1093/mnras/stad1626 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.523.3709C 523, 3709

    2023 doi

  17. [25]

    Chaikin E., et al., 2026, @doi [ ] 10.1093/mnras/stag300 , https://ui.adsabs.harvard.edu/abs/2026MNRAS.548ag300C 548, stag300

    2026 doi

  18. [26]

    Cheng H., Greengard L., Rokhlin V., 1999, @doi [Journal of Computational Physics] 10.1006/jcph.1999.6355 , https://ui.adsabs.harvard.edu/abs/1999JCoPh.155..468C 155, 468

    1999 doi

  19. [27]

    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 doi

  20. [28]

    E., 2010, @doi [ ] 10.1088/0004-637X/712/2/833 , https://ui.adsabs.harvard.edu/abs/2010ApJ...712..833C 712, 833

    Conroy C., Gunn J. E., 2010, @doi [ ] 10.1088/0004-637X/712/2/833 , https://ui.adsabs.harvard.edu/abs/2010ApJ...712..833C 712, 833

    2010 doi

  21. [29]

    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

  22. [30]

    A., et al., 2026, @doi [ ] 10.1093/mnras/stag645 , https://ui.adsabs.harvard.edu/abs/2026MNRAS.tmp..607C

    Correa C. A., et al., 2026, @doi [ ] 10.1093/mnras/stag645 , https://ui.adsabs.harvard.edu/abs/2026MNRAS.tmp..607C

    2026 doi

  23. [31]

    A., et al., 2015, @doi [ ] 10.1093/mnras/stv725 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.450.1937C 450, 1937

    Crain R. A., et al., 2015, @doi [ ] 10.1093/mnras/stv725 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.450.1937C 450, 1937

    2015 doi

  24. [32]

    Dai X., et al., 2009, @doi [ ] 10.1088/0004-637X/697/1/506 , https://ui.adsabs.harvard.edu/abs/2009ApJ...697..506D 697, 506

    2009 doi

  25. [33]

    Dalla Vecchia C., Schaye J., 2012, @doi [ ] 10.1111/j.1365-2966.2012.21704.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.426..140D 426, 140

    2012 doi

  26. [34]

    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

  27. [35]

    I., et al., 2017, @doi [ ] 10.1088/1538-3873/129/974/044102 , https://ui.adsabs.harvard.edu/abs/2017PASP..129d4102D 129, 044102

    Davies J. I., et al., 2017, @doi [ ] 10.1088/1538-3873/129/974/044102 , https://ui.adsabs.harvard.edu/abs/2017PASP..129d4102D 129, 044102

    2017 doi

  28. [36]

    S., White S

    Davis M., Efstathiou G., Frenk C. S., White S. D. M., 1985, @doi [ ] 10.1086/163168 , https://ui.adsabs.harvard.edu/abs/1985ApJ...292..371D 292, 371

    1985 doi

  29. [37]

    T., 2003, @doi [ ] 10.1146/annurev.astro.41.011802.094840 , https://ui.adsabs.harvard.edu/abs/2003ARA&A..41..241D 41, 241

    Draine B. T., 2003, @doi [ ] 10.1146/annurev.astro.41.011802.094840 , https://ui.adsabs.harvard.edu/abs/2003ARA&A..41..241D 41, 241

    2003 doi

  30. [38]

    T., Li A., 2007, @doi [ ] 10.1086/511055 , https://ui.adsabs.harvard.edu/abs/2007ApJ...657..810D 657, 810

    Draine B. T., Li A., 2007, @doi [ ] 10.1086/511055 , https://ui.adsabs.harvard.edu/abs/2007ApJ...657..810D 657, 810

    2007 doi

  31. [39]

    T., et al., 2007, @doi [ ] 10.1086/518306 , https://ui.adsabs.harvard.edu/abs/2007ApJ...663..866D 663, 866

    Draine B. T., et al., 2007, @doi [ ] 10.1086/518306 , https://ui.adsabs.harvard.edu/abs/2007ApJ...663..866D 663, 866

    2007 doi

  32. [40]

    P., et al., 2012, @doi [ ] 10.1111/j.1365-2966.2012.22036.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.427.3244D 427, 3244

    Driver S. P., et al., 2012, @doi [ ] 10.1111/j.1365-2966.2012.22036.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.427.3244D 427, 3244

    2012 doi

  33. [41]

    P., et al., 2022, @doi [ ] 10.1093/mnras/stac472 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.513..439D 513, 439

    Driver S. P., et al., 2022, @doi [ ] 10.1093/mnras/stac472 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.513..439D 513, 439

    2022 doi

  34. [42]

    Dubois Y., Peirani S., Pichon C., Devriendt J., Gavazzi R., Welker C., Volonteri M., 2016, @doi [ ] 10.1093/mnras/stw2265 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.463.3948D 463, 3948

    2016 doi

  35. [43]

    L., 2000, @doi [ ] 10.1046/j.1365-8711.2000.03386.x , https://ui.adsabs.harvard.edu/abs/2000MNRAS.315..115D 315, 115

    Dunne L., Eales S., Edmunds M., Ivison R., Alexander P., Clements D. L., 2000, @doi [ ] 10.1046/j.1365-8711.2000.03386.x , https://ui.adsabs.harvard.edu/abs/2000MNRAS.315..115D 315, 115

    2000 doi

  36. [44]

    Dunne L., et al., 2011, @doi [ ] 10.1111/j.1365-2966.2011.19363.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.417.1510D 417, 1510

    2011 doi

  37. [45]

    J., Stanway E

    Eldridge J. J., Stanway E. R., Xiao L., McClelland L. A. S., Taylor G., Ng M., Greis S. M. L., Bray J. C., 2017, @doi [ ] 10.1017/pasa.2017.51 , https://ui.adsabs.harvard.edu/abs/2017PASA...34...58E 34, e058

    2017 doi

  38. [46]

    J., Helly J., McGibbon R., Schaye J., Schaller M., Han J., Kugel R., Bah \'e Y

    Forouhar Moreno V. J., Helly J., McGibbon R., Schaye J., Schaller M., Han J., Kugel R., Bah \'e Y. M., 2025, @doi [ ] 10.1093/mnras/staf1478 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.543.1339F 543, 1339

    2025 doi

  39. [47]

    P., 2018, @doi [ ] 10.1146/annurev-astro-081817-051900 , https://ui.adsabs.harvard.edu/abs/2018ARA&A..56..673G 56, 673

    Galliano F., Galametz M., Jones A. P., 2018, @doi [ ] 10.1146/annurev-astro-081817-051900 , https://ui.adsabs.harvard.edu/abs/2018ARA&A..56..673G 56, 673

    2018 doi

  40. [48]

    U., Nersesian A., van der Wel A., 2024, @doi [ ] 10.1093/mnras/stae1377 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.531.3839G 531, 3839

    Gebek A., Tr c ka A., Baes M., Martorano M., Pillepich A., Kapoor A. U., Nersesian A., van der Wel A., 2024, @doi [ ] 10.1093/mnras/stae1377 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.531.3839G 531, 3839

    2024 doi

  41. [49]

    W., Sahu N., 2023, @doi [ ] 10.1093/mnras/stac2019 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.518.2177G 518, 2177

    Graham A. W., Sahu N., 2023, @doi [ ] 10.1093/mnras/stac2019 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.518.2177G 518, 2177

    2023 doi

  42. [50]

    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

  43. [51]

    Gruppioni C., et al., 2013, @doi [ ] 10.1093/mnras/stt308 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.432...23G 432, 23

    2013 doi

  44. [52]

    E., 2020, Astrophysics Source Code Library, pp ascl--2008

    Hahn O., Michaux M., Rampf C., Uhlemann C., Angulo R. E., 2020, Astrophysics Source Code Library, pp ascl--2008

    2020

  45. [53]

    S., Benitez-Llambay A., Helly J., 2018, @doi [ ] 10.1093/mnras/stx2792 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.474..604H 474, 604

    Han J., Cole S., Frenk C. S., Benitez-Llambay A., Helly J., 2018, @doi [ ] 10.1093/mnras/stx2792 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.474..604H 474, 604

    2018 doi

  46. [54]

    A., Cortese L., Obreschkow D., Catinella B., Cook R

    Hardwick J. A., Cortese L., Obreschkow D., Catinella B., Cook R. H. W., 2022, @doi [ ] 10.1093/mnras/stab3261 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.3751H 509, 3751

    2022 doi

  47. [55]

    A., Puchwein E., Shen S., Sijacki D., 2018, @doi [ ] 10.1093/mnras/sty1780 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.479.5385H 479, 5385

    Henden N. A., Puchwein E., Shen S., Sijacki D., 2018, @doi [ ] 10.1093/mnras/sty1780 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.479.5385H 479, 5385

    2018 doi

  48. [56]

    H., 1983, , https://ui.adsabs.harvard.edu/abs/1983QJRAS..24..267H 24, 267

    Hildebrand R. H., 1983, , https://ui.adsabs.harvard.edu/abs/1983QJRAS..24..267H 24, 267

    1983

  49. [57]

    T., Driver S

    Hill D. T., Driver S. P., Cameron E., Cross N., Liske J., Robotham A., 2010, @doi [ ] 10.1111/j.1365-2966.2010.16374.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.404.1215H 404, 1215

    2010 doi

  50. [58]

    Hu s ko F., et al., 2026, @doi [ ] 10.1093/mnras/stag324 , https://ui.adsabs.harvard.edu/abs/2026MNRAS.547ag324H 547, stag324

    2026 doi

  51. [59]

    P., K \"o hler M., Ysard N., Bocchio M., Verstraete L., 2017, @doi [ ] 10.1051/0004-6361/201630225 , https://ui.adsabs.harvard.edu/abs/2017A&A...602A..46J 602, A46

    Jones A. P., K \"o hler M., Ysard N., Bocchio M., Verstraete L., 2017, @doi [ ] 10.1051/0004-6361/201630225 , https://ui.adsabs.harvard.edu/abs/2017A&A...602A..46J 602, A46

    2017 doi

  52. [60]

    Jonsson P., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10884.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.372....2J 372, 2

    2006 doi

  53. [61]

    U., et al., 2023, @doi [ ] 10.1093/mnras/stad2977 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.3871K 526, 3871

    Kapoor A. U., et al., 2023, @doi [ ] 10.1093/mnras/stad2977 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.3871K 526, 3871

    2023 doi

  54. [62]

    U., et al., 2024, @doi [ ] 10.1051/0004-6361/202451207 , https://ui.adsabs.harvard.edu/abs/2024A&A...692A..79K 692, A79

    Kapoor A. U., et al., 2024, @doi [ ] 10.1051/0004-6361/202451207 , https://ui.adsabs.harvard.edu/abs/2024A&A...692A..79K 692, A79

    2024 doi

  55. [63]

    Kaviraj S., et al., 2017, @doi [ ] 10.1093/mnras/stx126 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.467.4739K 467, 4739

    2017 doi

  56. [64]

    C., Evans N

    Kennicutt R. C., Evans N. J., 2012, @doi [ ] 10.1146/annurev-astro-081811-125610 , https://ui.adsabs.harvard.edu/abs/2012ARA&A..50..531K 50, 531

    2012 doi

  57. [65]

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

    2023 doi

  58. [66]

    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

  59. [67]

    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

  60. [68]

    Lagos C. d. P., et al., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2512.11309 , https://ui.adsabs.harvard.edu/abs/2025arXiv251211309L p. arXiv:2512.11309

    2025 doi

  61. [69]

    Le Floc'h E., et al., 2005, @doi [ ] 10.1086/432789 , https://ui.adsabs.harvard.edu/abs/2005ApJ...632..169L 632, 169

    2005 doi

  62. [70]

    Lo Faro B., Buat V., Roehlly Y., Alvarez-Marquez J., Burgarella D., Silva L., Efstathiou A., 2017, @doi [ ] 10.1093/mnras/stx1901 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.472.1372L 472, 1372

    2017 doi

  63. [71]

    Loveday J., et al., 2012, @doi [ ] 10.1111/j.1365-2966.2011.20111.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.420.1239L 420, 1239

    2012 doi

  64. [72]

    D., Schaye J., Schaller M., Richings J., 2019, @doi [ ] 10.1093/mnrasl/slz110 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488L.123L 488, L123

    Ludlow A. D., Schaye J., Schaller M., Richings J., 2019, @doi [ ] 10.1093/mnrasl/slz110 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488L.123L 488, L123

    2019 doi

  65. [73]

    D., Fall S

    Ludlow A. D., Fall S. M., Wilkinson M. J., Schaye J., Obreschkow D., 2023, @doi [ ] 10.1093/mnras/stad2615 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.525.5614L 525, 5614

    2023 doi

  66. [74]

    D., et al., 2026, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2026arXiv260326200L p

    Ludlow A. D., et al., 2026, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2026arXiv260326200L p. arXiv:2603.26200

    2026

  67. [75]

    E., et al., 2015, @doi [arXiv e-prints] 10.48550/arXiv.1510.07674 , https://ui.adsabs.harvard.edu/abs/2015arXiv151007674M p

    Mamajek E. E., et al., 2015, @doi [arXiv e-prints] 10.48550/arXiv.1510.07674 , https://ui.adsabs.harvard.edu/abs/2015arXiv151007674M p. arXiv:1510.07674

    2015 doi

  68. [76]

    Marchetti L., et al., 2016, @doi [ ] 10.1093/mnras/stv2717 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.456.1999M 456, 1999

    2016 doi

  69. [77]

    R., Fadda D., Appleton P

    Marleau F. R., Fadda D., Appleton P. N., Noriega-Crespo A., Im M., Clancy D., 2007, @doi [ ] 10.1086/518114 , https://ui.adsabs.harvard.edu/abs/2007ApJ...663..218M 663, 218

    2007 doi

  70. [78]

    McGibbon R., Helly J., Schaye J., Schaller M., Vandenbroucke B., 2025, @doi [The Journal of Open Source Software] 10.21105/joss.08252 , https://ui.adsabs.harvard.edu/abs/2025JOSS...10.8252M 10, 8252

    2025 doi

  71. [79]

    E., 2021, @doi [ ] 10.1093/mnras/staa3149 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.500..663M 500, 663

    Michaux M., Hahn O., Rampf C., Angulo R. E., 2021, @doi [ ] 10.1093/mnras/staa3149 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.500..663M 500, 663

    2021 doi

  72. [80]

    D., Lacey C

    Mitchell P. D., Lacey C. G., Baugh C. M., Cole S., 2013, @doi [ ] 10.1093/mnras/stt1280 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.435...87M 435, 87

    2013 doi

  73. [81]

    G., et al., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2511.01803 , https://ui.adsabs.harvard.edu/abs/2025arXiv251101803M p

    Moore S. G., et al., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2511.01803 , https://ui.adsabs.harvard.edu/abs/2025arXiv251101803M p. arXiv:2511.01803

    2025 doi

  74. [82]

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

    2021 doi

  75. [83]

    Negrello M., et al., 2013, @doi [ ] 10.1093/mnras/sts417 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.429.1309N 429, 1309

    2013 doi

  76. [84]

    Nelson D., et al., 2019, @doi [Computational Astrophysics and Cosmology] 10.1186/s40668-019-0028-x , https://ui.adsabs.harvard.edu/abs/2019ComAC...6....2N 6, 2

    2019 doi

  77. [85]

    Nobels F. S. J., Schaye J., Schaller M., Ploeckinger S., Chaikin E., Richings A. J., 2024, @doi [ ] 10.1093/mnras/stae1390 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.532.3299N 532, 3299

    2024 doi

  78. [86]

    A., 2025, @doi [Journal of Open Source Software] 10.21105/joss.09278 , 10, 9278

    Oman K. A., 2025, @doi [Journal of Open Source Software] 10.21105/joss.09278 , 10, 9278

    2025 doi

  79. [87]

    Pakmor R., et al., 2023, @doi [ ] 10.1093/mnras/stac3620 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.524.2539P 524, 2539

    2023 doi

  80. [88]

    L., Vaccari M., Mortlock D

    Patel H., Clements D. L., Vaccari M., Mortlock D. J., Rowan-Robinson M., P \'e rez-Fournon I., Afonso-Luis A., 2013, @doi [ ] 10.1093/mnras/sts013 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.428..291P 428, 291

    2013 doi

  81. [89]

    Ploeckinger S., Schaye J., 2020, @doi [ ] 10.1093/mnras/staa2172 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.497.4857P 497, 4857

    2020 doi

  82. [90]

    J., Schaye J., Trayford J

    Ploeckinger S., Richings A. J., Schaye J., Trayford J. W., Schaller M., Chaikin E., 2025, @doi [ ] 10.1093/mnras/staf1402 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.543..891P 543, 891

    2025 doi

  83. [91]

    Pozzi F., et al., 2004, @doi [ ] 10.1086/420963 , https://ui.adsabs.harvard.edu/abs/2004ApJ...609..122P 609, 122

    2004 doi

  84. [92]

    J., Schaye J., Oppenheimer B

    Richings A. J., Schaye J., Oppenheimer B. D., 2014a, @doi [ ] 10.1093/mnras/stu525 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.440.3349R 440, 3349

    doi

  85. [93]

    J., Schaye J., Oppenheimer B

    Richings A. J., Schaye J., Oppenheimer B. D., 2014b, @doi [ ] 10.1093/mnras/stu1046 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.442.2780R 442, 2780

    doi

  86. [94]

    Rodighiero G., et al., 2010, @doi [ ] 10.1051/0004-6361/200912058 , https://ui.adsabs.harvard.edu/abs/2010A&A...515A...8R 515, A8

    2010 doi

  87. [95]

    Schaller M., et al., 2024, @doi [ ] 10.1093/mnras/stae922 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.530.2378S 530, 2378

    2024 doi

  88. [96]

    Schaye J., 2001, @doi [ ] 10.1086/322421 , https://ui.adsabs.harvard.edu/abs/2001ApJ...559..507S 559, 507

    2001 doi

  89. [97]

    Schaye J., Dalla Vecchia C., 2008, @doi [ ] 10.1111/j.1365-2966.2007.12639.x , https://ui.adsabs.harvard.edu/abs/2008MNRAS.383.1210S 383, 1210

    2008 doi

  90. [98]

    Schaye J., et al., 2015, @doi [ ] 10.1093/mnras/stu2058 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.446..521S 446, 521

    2015 doi

  91. [99]

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

    2023 doi

  92. [100]

    Schaye J., et al., 2026, @doi [ ] 10.1093/mnras/stag375 , https://ui.adsabs.harvard.edu/abs/2026MNRAS.548ag375S 548, stag375

    2026 doi

  93. [101]

    Shen X., et al., 2020, @doi [ ] 10.1093/mnras/staa1423 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.495.4747S 495, 4747

    2020 doi

  94. [102]

    L., Bressan A., Danese L., 1998, @doi [ ] 10.1086/306476 , https://ui.adsabs.harvard.edu/abs/1998ApJ...509..103S 509, 103

    Silva L., Granato G. L., Bressan A., Danese L., 1998, @doi [ ] 10.1086/306476 , https://ui.adsabs.harvard.edu/abs/1998ApJ...509..103S 509, 103

    1998 doi

  95. [103]

    Smith D. J. B., et al., 2012, @doi [ ] 10.1111/j.1365-2966.2012.21930.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.427..703S 427, 703

    2012 doi

  96. [104]

    R., Eldridge J

    Stanway E. R., Eldridge J. J., 2018, @doi [ ] 10.1093/mnras/sty1353 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.479...75S 479, 75

    2018 doi

  97. [105]

    T., Vacca W

    Tokunaga A. T., Vacca W. D., 2005, @doi [ ] 10.1086/429382 , https://ui.adsabs.harvard.edu/abs/2005PASP..117..421T 117, 421

    2005 doi

  98. [106]

    W., et al., 2015, @doi [ ] 10.1093/mnras/stv1461 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.452.2879T 452, 2879

    Trayford J. W., et al., 2015, @doi [ ] 10.1093/mnras/stv1461 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.452.2879T 452, 2879

    2015 doi

  99. [107]

    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

  100. [108]

    W., et al., 2026, @doi [ ] 10.1093/mnras/staf2040 , https://ui.adsabs.harvard.edu/abs/2026MNRAS.545f2040T 545, staf2040

    Trayford J. W., et al., 2026, @doi [ ] 10.1093/mnras/staf2040 , https://ui.adsabs.harvard.edu/abs/2026MNRAS.545f2040T 545, staf2040

    2026 doi

  101. [109]

    Tr c ka A., et al., 2020, @doi [ ] 10.1093/mnras/staa857 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.2823T 494, 2823

    2020 doi

  102. [110]

    Tr c ka A., et al., 2022, @doi [ ] 10.1093/mnras/stac2277 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.516.3728T 516, 3728

    2022 doi

  103. [111]

    Vaccari M., et al., 2010, @doi [ ] 10.1051/0004-6361/201014694 , https://ui.adsabs.harvard.edu/abs/2010A&A...518L..20V 518, L20

    2010 doi

  104. [112]

    Vlahakis C., Dunne L., Eales S., 2005, @doi [ ] 10.1111/j.1365-2966.2005.09666.x , https://ui.adsabs.harvard.edu/abs/2005MNRAS.364.1253V 364, 1253

    2005 doi

  105. [113]

    Vogelsberger M., et al., 2020, @doi [ ] 10.1093/mnras/staa137 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.492.5167V 492, 5167

    2020 doi

  106. [114]

    K., et al., 2005, @doi [ ] 10.1086/424735 , https://ui.adsabs.harvard.edu/abs/2005ApJ...619L..15W 619, L15

    Wyder T. K., et al., 2005, @doi [ ] 10.1086/424735 , https://ui.adsabs.harvard.edu/abs/2005ApJ...619L..15W 619, L15

    2005 doi

  107. [115]

    Zibetti S., Charlot S., Rix H.-W., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15528.x , https://ui.adsabs.harvard.edu/abs/2009MNRAS.400.1181Z 400, 1181

    2009 doi

  108. [116]

    de Graaff A., Trayford J., Franx M., Schaller M., Schaye J., van der Wel A., 2022, @doi [ ] 10.1093/mnras/stab3510 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.511.2544D 511, 2544

    2022 doi

Pith tools

Reviewed May 8, 2026 · model on record in the stance chip above.