Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

Evaluating quenching in cosmological simulations of galaxy formation with spectral covariance in the optical window

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Spectral covariance tests reveal that EAGLE and TNG100 simulations capture the variance of real galaxy spectra, with subtle AGN feedback mismatches tied to black hole seeding.

desk verdict Careful application of spectral covariance to simulations; the AGN mismatch is suggestive, but unvalidated classification cuts and small samples leave the BH-seeding claim unproven. read the letter →

arxiv 2411.08945 v2 pith:GTY5HJPU submitted 2024-11-13 astro-ph.CO astro-ph.GA

Z. Sharbaf , I. Ferreras , A. Negri , J. Angthopo , C. Dalla Vecchia , O. Lahav , R. S. Somerville This is my paper · ORCID
classification astro-ph.COastro-ph.GA
keywords spectralcovarianceprincipalcomponentanalysisgalaxyformationsimulationsEAGLEIllustrisTNGAGNfeedbackquenchingSDSSspectra
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 asks whether cosmological hydrodynamical simulations reproduce not just the average properties of galaxies but the full pattern of spectral variation seen in real galaxies. It applies principal component analysis to continuum-subtracted SDSS spectra and projects synthetic spectra from the EAGLE and Illustris TNG100 simulations onto the same eigenbasis. The central claim is that the simulated spectra are largely consistent with the observed spectral covariance, but subtle differences—especially in the AGN and quiescent subsets—can be traced to differences in how the simulations seed and grow central black holes. If this is right, spectral covariance offers a model-independent benchmark for galaxy formation simulations, and AGN feedback seeding is a key lever for improving them.

What carries the argument

The central machinery is principal component analysis on the covariance matrix of continuum-subtracted, emission-line-masked optical spectra (3800–4200 Å and 5000–5400 Å windows). Spectra from SDSS define the eigenbasis; synthetic spectra from EAGLE and TNG100 are projected onto the same eigenvectors to locate each simulated galaxy in the latent space of PC1–PC3. The argument then uses two supporting tools: SSP spectral fitting of stacked spectra split by PC projection percentiles to read off stellar age and metallicity, and star formation histories constructed directly from simulated stellar particles to interpret the latent-space positions physically.

What would settle it

If a non-AGN physical process (e.g., stellar feedback, environment) were found to be the dominant cause of the spectral variance differences between EAGLE and TNG100, the central claim would fail. A concrete test: run a simulation with identical subgrid prescriptions to TNG100 but with EAGLE-style BH seeding (earlier and lower mass seeds), and check whether the latent-space distribution of AGN and quiescent galaxies shifts to match the SDSS data and EAGLE's PC1 gradient.

Watch

Extended reading notes

Core claim

The paper's central discovery is that the covariance structure of real galaxy spectra, as encoded by the first three principal components of SDSS spectra in two optical windows, is largely reproduced by both EAGLE and TNG100 synthetic spectra, but with identifiable deviations. The first principal component is predominantly driven by stellar age, and the age sequence SF→AGN→Q is preserved in both simulations. However, the AGN and quiescent subsets show differences in the distribution of spectral variance that the authors trace to the subgrid implementation of AGN feedback, specifically the black hole seeding time: TNG100 seeds black holes later (sharp peak at z≈2) and with a higher seed mass and halo mass threshold, whereas EAGLE seeds earlier and more evenly in redshift. The resulting differences in the Eddington-ratio distribution lead to noticeably different quenching patterns and star formation histories for quiescent galaxies. The paper argues that spectral covariance is a powerful, model-independent test of simulations, complementing scaling relations.

Load-bearing premise

The classification of simulated galaxies into star-forming, AGN, and quiescent using direct cuts in sSFR and λEdd is assumed to be equivalent to the BPT emission-line classification of SDSS galaxies, even though the thresholds are tuned to match observed fractions.

Editorial extensions

If this is right

  • Spectral covariance provides a purely data-driven benchmark for galaxy formation simulations, independent of physical parameter choices, so simulation comparisons can move beyond bulk scaling relations.
  • The AGN feedback subgrid prescriptions in simulations need to reproduce not just the quenched fraction but the detailed shape of the spectral variance, meaning black hole seeding and accretion prescriptions are directly testable against observed spectra.
  • The explicit SF→AGN→Q evolutionary sequence in latent space gives a physical ordering of galaxy populations that simulations should reproduce; the overlap between SF and AGN in simulations indicates a specific deficiency in how AGN activity is coupled to star formation.
  • Differences in the star formation histories of quiescent galaxies between EAGLE and TNG100 directly reflect the different quenching mechanisms, so latent-space projections can be used to diagnose which physical process is responsible for quenching in a given simulation.
  • Using both blue and red spectral windows cross-checks the results and shows that the age sensitivity of PC1 is robust across wavelength ranges, strengthening the interpretation that PC1 tracks stellar age.

Reading between the lines

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

  • The paper's methodology could be extended to other simulations (e.g., TNG50, SIMBA, ASTRID) to see whether the AGN-mismatch signature is generic or specific to the EAGLE/TNG flavor of subgrid physics.
  • The latent-space comparison could be applied to emission-line windows as well, as a direct test of the mapping from sSFR/λEdd cuts to BPT classes.
  • The authors implicitly suggest that the later, more massive black hole seeding in TNG100 leads to a stronger but more abrupt quenching, which might also produce observable imprints in the halo occupation statistics or the scatter in the star-forming main sequence at higher redshift.
  • One could test the age-metallicity degeneracy interpretation directly by constructing mock spectra with known age-metallicity combinations and projecting them onto the SDSS eigenbasis, thus calibrating the PC axes in terms of physical parameters.
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, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper applies the PCA-based spectral covariance method of Sharbaf et al. (2023) to synthetic spectra constructed from EAGLE and TNG100 galaxies, matching the SDSS instrumental setup, noise properties, and stellar-mass distributions. It projects the continuum-subtracted synthetic spectra onto SDSS-derived eigenvectors for star-forming, AGN, and quiescent subsamples, compares the resulting latent-space locations, fits SSP ages and metallicities to percentile stacks, and examines the simulated star formation histories. The central claim is that the optical absorption-line covariance of real galaxies is broadly reproduced by both simulations, with star-forming populations in good agreement, while AGN and, downstream, quiescent populations show discrepancies that the authors attribute to differences in AGN subgrid prescriptions, specifically central black hole seeding.

Significance. If the conclusions hold, the paper offers a genuinely model-independent benchmark for galaxy-formation simulations that goes beyond scaling relations, and it identifies a concrete, falsifiable point of divergence among EAGLE, TNG100, and SDSS in the AGN population. The construction of the synthetic spectra is careful: realistic noise is drawn from SDSS inverse variances, velocity dispersions are matched, stellar-mass distributions are homogenised before comparison, and the analysis code and synthetic data are made publicly available. These strengths are substantial. However, the headline comparison currently lacks any quantitative statistical test, and the simulated-galaxy classification underlying the AGN conclusion is calibrated to global fractions rather than validated against a physical mapping. These issues must be addressed before the causal attribution to black-hole seeding can be accepted.

major comments (4)
  1. [Section 3.2, Table A1, Appendix A] The equivalence between the SDSS BPT classification and the simulation cuts in (sSFR, lambda_Edd) is assumed rather than demonstrated. The thresholds are chosen to reproduce the observed SF/AGN/Q fractions, as stated explicitly in Section 3.2 and Table A1, and they differ between the two simulations: EAGLE defines AGN as lambda_Edd > -2 while TNG100 uses lambda_Edd > -0.6 (Fig. 1). Because the 'AGN' samples therefore occupy different regions of accretion parameter space, the latent-space and SSP-fit differences attributed to AGN feedback (Figs. 6-8 and Section 7) may partly be a selection artifact rather than a physical difference in stellar populations. A concrete test would be to generate synthetic BPT classifications for the same simulated galaxies, for example via photoionization modelling, and check whether the adopted cuts recover those classes; at minimum, the authors should show that the conclusions are robust to varying the thresholds within reasonable ranges.
  2. [Section 6, Figures 5 and B1] The statement that 'real and simulated spectra are consistent regarding spectral covariance' is not backed by any statistical test. The text describes overlapping contours and qualitative separation, but no two-sample test (e.g., KS, AD, or a bootstrap overlap fraction) is computed between the PC distributions, and no error bars account for the finite sizes of the simulated subsamples (Table 1 lists 104 EAGLE AGN, 244 TNG100 AGN, 740-1045 quiescent, and 2094-2460 star-forming galaxies after homogenisation). Without such quantification, 'consistency' and 'discrepancy' cannot be distinguished from noise, particularly for the small AGN samples. Please add a distributional test on the PC projections and report effect sizes or confidence intervals.
  3. [Section 6.1, Figures 6-8, Table 1] The SSP-fit comparison uses stacks of the lowest and highest 33rd/67th percentile projections. For the EAGLE AGN sample this means each stack contains only about 35 galaxies (104 total after homogenisation; Table 1), and for TNG100 AGN about 81 galaxies. The MCMC contours show the fitting uncertainty of the stacked spectrum but not the galaxy-to-galaxy sampling variance within each stack or the uncertainty due to the stack construction. The claim that 'AGN galaxies in EAGLE show different metallicities in the opposite direction to the age-metallicity degeneracy' (Section 6.1) may therefore be driven by a small number of objects. Please include bootstrap resampling of the stacks, or an equivalent procedure, and report the number of galaxies per stack so that the statistical weight of the AGN divergence can be assessed.
  4. [Section 7, Figure 13] The attribution of the latent-space mismatch to black-hole seeding is only circumstantial. The paper demonstrates that EAGLE and TNG100 differ in the redshift distribution of BH seeding and in the distribution of lambda_Edd, but those quantities also depend on the accretion and feedback prescriptions, and no control test (e.g., varying only the seeding prescription within one simulation) is presented. The abstract wording 'could lead to the mismatch' is appropriately cautious, but the Discussion later states 'We ascribe this difference to the quenching mechanisms adopted by the simulations', which overstates the evidence. Please either soften this causal language or add a test that directly varies the seeding prescription.
minor comments (5)
  1. [Section 6.1, Figures 9-11] The text and figure axes contain several typos: '67rd' should be '67th', 'Lock back time' should be 'Lookback time', and Section 7 contains 'intringuingspike' rather than 'intriguing spike'.
  2. [Figure B1 caption, Appendix B] The caption contains 'desccribed' and the appendix text 'thhe'; please correct these typographical errors.
  3. [Section 6, Figure 5 caption] The caption states that the SDSS sample corresponds to the one homogenised with EAGLE, while Figure B1 shows SDSS sets for both simulations; please clarify in each panel which homogenised SDSS sample is being displayed.
  4. [Section 5, Equation (2)] The projection formula is written explicitly only for PC1, although the analysis uses the first three principal components; please state that the equation is illustrative and give the general form, or define the projections for PC2 and PC3 explicitly.
  5. [Appendix D, Figure D1] The noiseless comparison is shown only for EAGLE; repeating the exercise for TNG100 would make the claim that noise modelling is not the dominant systematic more convincing.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the spectral eigenvectors are derived from SDSS data alone and the synthetic spectra are projected independently, so the covariance comparison is not forced by construction.

full rationale

The paper's central comparison projects EAGLE/TNG synthetic spectra onto PCA eigenvectors derived solely from SDSS spectra (Eq. 2), so the latent-space distributions are not constructed from the simulations' own covariance. The only fitted inputs are the SF/AGN/Q cuts in sSFR and lambda_Edd (Table A1), and these are explicitly calibrated to reproduce observed BPT fractions rather than renamed as predictions; the subsequent spectral-covariance comparison, SSP age/metallicity stacks, and SFH analysis are not forced by that calibration. The dependence on the earlier PCA-SDSS paper (Sharbaf et al. 2023) is a normal use of an externally published, falsifiable result and does not smuggle the conclusions in. The use of E-MILES SSPs both to generate synthetic spectra and to fit stacks is a limitation for interpreting the simulated fits, but it does not make the observed-vs-simulated covariance comparison circular. No load-bearing step reduces to its own input by construction.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the fidelity of the SSP models, the continuum-subtraction assumption, and the equivalence of simulated and observed galaxy classification. No new physical entities are postulated.

free parameters (2)
  • Simulated galaxy classification thresholds (sSFR and lambda_Edd) for EAGLE and TNG100 = EAGLE SF: log(sSFR)>-10.5, lambda_Edd<-2; AGN: lambda_Edd>-2, log(sSFR)>-11.5; Q: log(sSFR)<-11, lambda_Edd<-4.2.
    Chosen so that subgroup fractions match the SDSS BPT classification (Section 3.2, Table A1).
  • Stacking percentiles for spectral fitting and SFH analysis = 33rd and 67th percentile
    Chosen for sample size reasons; differs from the 10th/90th used in PCA-SDSS (Section 6.1).
assumptions (4)
  • domain assumption E-MILES SSP models accurately represent the stellar populations of simulated stellar particles
    Used to generate synthetic spectra and to fit stacked spectra (Section 4).
  • domain assumption Continuum subtraction preserves absorption-line information and removes only dust/calibration effects
    Central to the PCA approach; if continuum subtraction removes physical variance, the comparison is biased (Section 3.1).
  • domain assumption The simulated classification via sSFR and lambda_Edd is equivalent to the SDSS BPT classification
    Used to define SF/AGN/Q subgroups in simulations (Section 3.2).
  • domain assumption The z=0.1 snapshot represents the z=0.05-0.1 SDSS sample
    Used to extract simulated galaxies (Section 2.2).

how reviews work

0 comments
Cite this review

Pith. "Pith review of Evaluating quenching in cosmological simulations of galaxy formation with spectral covariance in the optical window." pith.science (2026). https://pith.science/paper/GTY5HJPU

@misc{pith2026241108945,
  author       = {Pith},
  title        = {Pith review of: Evaluating quenching in cosmological simulations of galaxy formation with spectral covariance in the optical window},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GTY5HJPU}},
  note         = {Machine review of arXiv:2411.08945}
}
abstract

Cosmological hydrodynamical simulations provide valuable insights on galaxy evolution when coupled with observational data. Comparisons with real galaxies are typically performed via scaling relations of the observables. Here we follow an alternative approach based on the spectral covariance in a model-independent way. We build upon previous work by Sharbaf et al. that studied the covariance of high quality SDSS continuum-subtracted spectra in a relatively narrow range of velocity dispersion ($\sigma\in [100,150]$\,km\,s$^{-1}$). Here the same analysis is applied to synthetic data from the EAGLE and Illustris TNG100 simulations, to assess the ability of these runs to mimic real galaxies. The real and simulated spectra are consistent regarding spectral covariance, although with subtle differences that can inform the implementation of subgrid physics. Spectral fitting done a posteriori on stacks segregated with respect to latent space reveals that the first principal component (PC1) is predominantly influenced by the stellar age distribution, with an underlying age-metallicity degeneracy. Good agreement is found regarding star formation prescriptions but there is disagreement with AGN feedback, that also affects the subset of quiescent galaxies. We show a substantial difference in the implementation of the AGN subgrid prescriptions, regarding central black hole seeding, that could lead to the mismatch. Differences are manifest between these two simulations in the star formation histories stacked with respect to latent space. We emphasise that this methodology only relies on the spectral covariance to assess whether simulations provide a true representation of galaxy formation.

Figures

Figures reproduced from arXiv: 2411.08945 by the authors.

Figure 1
Figure 1. Galaxy classification based on 𝜆Edd and sSFR in simulations in EAGLE (top) and TNG100 (bottom). The light blue, green, and red regions, show our choice for SF, AGN, Q galaxies, respectively. Galaxies with zero star formation rate shown with log10 sSFR(yr−1 ) = −14. errors caused by dust reddening or residual flux calibrations. The PCA study focuses on the two spectral intervals of 3800 to 4200 Å and 5000 to 5400 Å .… view at source ↗
Figure 2
Figure 2. Distribution of stellar mass before and after homogenisation be￾tween observed (SDSS) and simulation data (EAGLE). The blue, green, and red histograms correspond to SF, AGN, and Q galaxies, respectively. The inset panels show the distribution of SDSS and EAGLE galaxies in each sub￾sample before homogenisation. A KS test confirms that the final distributions originate from the same parent sample. Each panel shows the… view at source ↗
Figure 3
Figure 3. Equivalent of [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Comparison between stacked spectra of observed and simulated galaxies after all the processes are applied, including separation into different groups, homogenisation, and spectral synthesis. Gray areas indicate (from left) the blue and red spectral intervals explored, …
Figure 5
Figure 5. Figure 5: Distribution of the projections of the EAGLE (top), TNG100 (middle) and SDSS (bottom) spectra onto the first three principal components of PCA derived by the SDSS sample. The galaxies are separated into star-forming (blue), AGN (green), and quiescent (red). The left (r…
Figure 6
Figure 6. Figure 6: Confidence levels of the SSP-equivalent age (in Gyr) and metallicity ([Z/H]) obtained from fitting the continuum subtracted spectra with the E-MILES population models (Vazdekis et al. 2016). We fit the stacked spectra produced by combining data from the lowest (33 perc…
Figure 7
Figure 7. Figure 7: Equivalent of [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Equivalent of [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]
Figure 9
Figure 9. Figure 9: Median star formation history of galaxies with the lowest (33 percentile) and highest (67 percentile) value of PC1 in the blue interval, for Q, AGN, and SF sub-classes (from top to bottom). The EAGLE (TNG100) sample is shown in the left (right) column. 0.02 0.05 0.08 0…
Figure 10
Figure 10. Figure 10: Equivalent of [PITH_FULL_IMAGE:figures/full_fig_p015_10.png]
Figure 11
Figure 11. Figure 11: Equivalent of [PITH_FULL_IMAGE:figures/full_fig_p016_11.png]
Figure 12
Figure 12. Figure 12: SDSS (𝑔 − 𝑟) colour vs stellar mass relation of the homogenised sub-samples of the EAGLE (top) and TNG100 (bottom) simulations. They are colour-coded with respect to their activity, into star-forming (blue), AGN (green) and quiescent (red). The SDSS 𝑔 and 𝑟 (dust-free…
Figure 13
Figure 13. Figure 13: Top: Comparison of the SMBH seeding times – expressed as red￾shift – between the EAGLE and TNG100 simulations. Bottom: Distribution of 𝜆Edd in EAGLE and TNG100, measured in the 𝑧 = 0.1 snapshot. order of magnitude higher in TNG100 with respect to EAGLE. This would mea…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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.

Reference graph

Works this paper leans on

88 extracted references · 6 canonical work pages · cited by 1 Pith paper

  1. [1]

    Abolfathi B., et al., 2018, @doi [ ] 10.3847/1538-4365/aa9e8a , https://ui.adsabs.harvard.edu/abs/2018ApJS..235...42A 235, 42

    doi:10.3847/1538-4365/aa9e8a 2018

  2. [2]

    Ahumada et al., 2020, @doi [ ] 10.3847/1538-4365/ab929e , https://ui.adsabs.harvard.edu/abs/2020ApJS..249....3A 249, 3

    doi:10.3847/1538-4365/ab929e 2020

  3. [3]

    Angthopo J., Ferreras I., Silk J., 2019, @doi [ ] 10.1093/mnrasl/slz106 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488L..99A 488, L99

    doi:10.1093/mnrasl/slz106 2019

  4. [4]

    Angthopo J., Ferreras I., Silk J., 2020, @doi [ ] 10.1093/mnras/staa1276 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.495.2720A 495, 2720

    work page doi:10.1093/mnras/staa1276 2020

  5. [5]

    G., Dalla Vecchia C., Pillepich A., 2021, @doi [ ] 10.1093/mnras/staa3294 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.3685A 502, 3685

    Angthopo J., Negri A., Ferreras I., de la Rosa I. G., Dalla Vecchia C., Pillepich A., 2021, @doi [ ] 10.1093/mnras/staa3294 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.3685A 502, 3685

    work page doi:10.1093/mnras/staa3294 2021

  6. [6]

    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

    doi:10.1086/380092 2004

  7. [7]

    A., Phillips M

    Baldwin J. A., Phillips M. M., Terlevich R., 1981, @doi [ ] 10.1086/130766 , https://ui.adsabs.harvard.edu/abs/1981PASP...93....5B 93, 5

    doi:10.1086/130766 1981

  8. [8]

    Bernardi et al., 2003, @doi [ ] 10.1086/374256 , https://ui.adsabs.harvard.edu/abs/2003AJ....125.1849B 125, 1849

    doi:10.1086/374256 2003

Show all 88 references
  1. [10]

    G., Schaye J., Frenk C

    Bower R. G., Schaye J., Frenk C. S., Theuns T., Schaller M., Crain R. A., McAlpine S., 2017, @doi [ ] 10.1093/mnras/stw2735 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.465...32B 465, 32

    2017 doi

  2. [11]

    Brinchmann J., Charlot S., White S. D. M., Tremonti C., Kauffmann G., Heckman T., Brinkmann J., 2004, @doi [ ] 10.1111/j.1365-2966.2004.07881.x , https://ui.adsabs.harvard.edu/abs/2004MNRAS.351.1151B 351, 1151

    2004

  3. [12]

    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

  4. [13]

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

    2003 doi

  5. [14]

    Cid Fernandes R., Stasi \'n ska G., Mateus A., Vale Asari N., 2011, @doi [ ] 10.1111/j.1365-2966.2011.18244.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.413.1687C 413, 1687

    2011

  6. [15]

    P., 2017, @doi [ ] 10.3847/1538-4357/835/1/15 , https://ui.adsabs.harvard.edu/abs/2017ApJ...835...15C 835, 15

    Ciotti L., Pellegrini S., Negri A., Ostriker J. P., 2017, @doi [ ] 10.3847/1538-4357/835/1/15 , https://ui.adsabs.harvard.edu/abs/2017ApJ...835...15C 835, 15

    2017 doi

  7. [16]

    A., van de Voort F., 2023, @doi [ ] 10.1146/annurev-astro-041923-043618 , https://ui.adsabs.harvard.edu/abs/2023ARA&A..61..473C 61, 473

    Crain R. A., van de Voort F., 2023, @doi [ ] 10.1146/annurev-astro-041923-043618 , https://ui.adsabs.harvard.edu/abs/2023ARA&A..61..473C 61, 473

    2023 doi

  8. [17]

    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

  9. [19]

    J., Crain R

    Davies J. J., Crain R. A., Oppenheimer B. D., Schaye J., 2020, @doi [ ] 10.1093/mnras/stz3201 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.491.4462D 491, 4462

    2020 doi

  10. [20]

    J., Romano J

    Disney M. J., Romano J. D., Garcia-Appadoo D. A., West A. A., Dalcanton J. J., Cortese L., 2008, @doi [ ] 10.1038/nature07366 , https://ui.adsabs.harvard.edu/abs/2008Natur.455.1082D 455, 1082

    2008 doi

  11. [21]

    Donnari M., et al., 2019, @doi [ ] 10.1093/mnras/stz712 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.485.4817D 485, 4817

    2019 doi

  12. [22]

    Donnari M., et al., 2021, @doi [ ] 10.1093/mnras/staa3006 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.4004D 500, 4004

    2021 doi

  13. [24]

    Ferreras I., et al., 2019, @doi [ ] 10.1093/mnras/stz2095 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.489..608F 489, 608

    2019 doi

  14. [25]

    S., Silk J., 2023, @doi [RAS Techniques and Instruments] 10.1093/rasti/rzad004 , https://ui.adsabs.harvard.edu/abs/2023RASTI...2...78F 2, 78

    Ferreras I., Lahav O., Somerville R. S., Silk J., 2023, @doi [RAS Techniques and Instruments] 10.1093/rasti/rzad004 , https://ui.adsabs.harvard.edu/abs/2023RASTI...2...78F 2, 78

    2023 doi

  15. [26]

    Folkes S., Lahav O., Maddox S., 1996, , 283, 651

    1996

  16. [27]

    W., Lang D., Goodman J., 2013, @doi [PASP] 10.1086/670067 , https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306

    Foreman-Mackey D., Hogg D. W., Lang D., Goodman J., 2013, @doi [PASP] 10.1086/670067 , https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306

    2013 doi

  17. [28]

    Furlong M., et al., 2015, @doi [ ] 10.1093/mnras/stv852 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.450.4486F 450, 4486

    2015 doi

  18. [30]

    F., Zibetti S., Brinchmann J., Kelson D

    Gallazzi A., Bell E. F., Zibetti S., Brinchmann J., Kelson D. D., 2014, @doi [ ] 10.1088/0004-637X/788/1/72 , https://ui.adsabs.harvard.edu/abs/2014ApJ...788...72G 788, 72

    2014 doi

  19. [31]

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

    2014 doi

  20. [32]

    P., 2017, @doi [ ] 10.1093/mnras/stx1602 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.471.1976G 471, 1976

    Georgakakis A., Aird J., Schulze A., Dwelly T., Salvato M., Nandra K., Merloni A., Schneider D. P., 2017, @doi [ ] 10.1093/mnras/stx1602 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.471.1976G 471, 1976

    2017 doi

  21. [33]

    Girardi L., Bressan A., Bertelli G., Chiosi C., 2000, @doi [ ] 10.1051/aas:2000126 , https://ui.adsabs.harvard.edu/abs/2000A&AS..141..371G 141, 371

    2000 doi

  22. [34]

    R., Schaye J., Booth C

    Haas M. R., Schaye J., Booth C. M., Dalla Vecchia C., Springel V., Theuns T., Wiersma R. P. C., 2013a, @doi [ ] 10.1093/mnras/stt1487 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.435.2931H 435, 2931

    doi

  23. [35]

    R., Schaye J., Booth C

    Haas M. R., Schaye J., Booth C. M., Dalla Vecchia C., Springel V., Theuns T., Wiersma R. P. C., 2013b, @doi [ ] 10.1093/mnras/stt1488 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.435.2955H 435, 2955

    doi

  24. [36]

    Habouzit M., et al., 2021, @doi [ ] 10.1093/mnras/stab496 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.503.1940H 503, 1940

    2021 doi

  25. [37]

    M., Kauffmann G., Brinchmann J., Charlot S., Tremonti C., White S

    Heckman T. M., Kauffmann G., Brinchmann J., Charlot S., Tremonti C., White S. D. M., 2004, @doi [ ] 10.1086/422872 , https://ui.adsabs.harvard.edu/abs/2004ApJ...613..109H 613, 109

    2004 doi

  26. [38]

    Hirschmann M., et al., 2023, @doi [ ] 10.1093/mnras/stad2955 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.3610H 526, 3610

    2023 doi

  27. [39]

    C., 2008, @doi [ ] 10.1146/annurev.astro.45.051806.110546 , https://ui.adsabs.harvard.edu/abs/2008ARA&A..46..475H 46, 475

    Ho L. C., 2008, @doi [ ] 10.1146/annurev.astro.45.051806.110546 , https://ui.adsabs.harvard.edu/abs/2008ARA&A..46..475H 46, 475

    2008 arXiv

  28. [40]

    C., 2009, @doi [ ] 10.1088/0004-637X/699/1/626 , https://ui.adsabs.harvard.edu/abs/2009ApJ...699..626H 699, 626

    Ho L. C., 2009, @doi [ ] 10.1088/0004-637X/699/1/626 , https://ui.adsabs.harvard.edu/abs/2009ApJ...699..626H 699, 626

    2009 doi

  29. [41]

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

    2017 doi

  30. [43]

    J., Nicholls D

    Kewley L. J., Nicholls D. C., Sutherland R. S., 2019, @doi [ ] 10.1146/annurev-astro-081817-051832 , https://ui.adsabs.harvard.edu/abs/2019ARA&A..57..511K 57, 511

    2019 doi

  31. [44]

    Kroupa P., 2001, @doi [ ] 10.1046/j.1365-8711.2001.04022.x , https://ui.adsabs.harvard.edu/abs/2001MNRAS.322..231K 322, 231

    2001

  32. [45]

    G., Ibarra-Medel H., Pacifici C., S \'a nchez S

    Lammers C., Iyer K. G., Ibarra-Medel H., Pacifici C., S \'a nchez S. F., Tacchella S., Woo J., 2023, @doi [ ] 10.3847/1538-4357/acdd57 , https://ui.adsabs.harvard.edu/abs/2023ApJ...953...26L 953, 26

    2023 doi

  33. [46]

    Le Brun A. M. C., McCarthy I. G., Schaye J., Ponman T. J., 2014, @doi [ ] 10.1093/mnras/stu608 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.441.1270L 441, 1270

    2014 doi

  34. [47]

    Li S.-L., Xie F.-G., 2017, @doi [ ] 10.1093/mnras/stx1778 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.471.2848L 471, 2848

    2017 doi

  35. [48]

    S., et al., 2003, , 344, 847

    Madgwick D. S., et al., 2003, , 344, 847

    2003

  36. [49]

    Marinacci F., et al., 2018, @doi [ ] 10.1093/mnras/sty2206 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.480.5113M 480, 5113

    2018 doi

  37. [50]

    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

  38. [51]

    G., Harrison C

    McAlpine S., Bower R. G., Harrison C. M., Crain R. A., Schaller M., Schaye J., Theuns T., 2017, @doi [ ] 10.1093/mnras/stx658 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.468.3395M 468, 3395

    2017 doi

  39. [52]

    R., et al., 2022, @doi [ ] 10.1051/0004-6361/202142215 , https://ui.adsabs.harvard.edu/abs/2022A&A...665A.144M 665, A144

    Mulcahey C. R., et al., 2022, @doi [ ] 10.1051/0004-6361/202142215 , https://ui.adsabs.harvard.edu/abs/2022A&A...665A.144M 665, A144

    2022 doi

  40. [53]

    P., 2017, @doi [ ] 10.1146/annurev-astro-081913-040019 , https://ui.adsabs.harvard.edu/abs/2017ARA&A..55...59N 55, 59

    Naab T., Ostriker J. P., 2017, @doi [ ] 10.1146/annurev-astro-081913-040019 , https://ui.adsabs.harvard.edu/abs/2017ARA&A..55...59N 55, 59

    2017 doi

  41. [54]

    P., et al., 2018, @doi [ ] 10.1093/mnras/sty618 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.1206N 477, 1206

    Naiman J. P., et al., 2018, @doi [ ] 10.1093/mnras/sty618 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.1206N 477, 1206

    2018 doi

  42. [55]

    Negri A., Volonteri M., 2017, @doi [ ] 10.1093/mnras/stx362 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.467.3475N 467, 3475

    2017 doi

  43. [56]

    Negri A., Dalla Vecchia C., Aguerri J. A. L., Bah \'e Y., 2022, @doi [ ] 10.1093/mnras/stac1481 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.515.2121N 515, 2121

    2022 doi

  44. [57]

    Nelson D., et al., 2015, @doi [Astronomy and Computing] 10.1016/j.ascom.2015.09.003 , https://ui.adsabs.harvard.edu/abs/2015A&C....13...12N 13, 12

    2015 doi

  45. [58]

    Nelson D., et al., 2018, @doi [ ] 10.1093/mnras/stx3040 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475..624N 475, 624

    2018 doi

  46. [59]

    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

  47. [60]

    Nersesian A., et al., 2021, @doi [ ] 10.1093/mnras/stab1984 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.506.3986N 506, 3986

    2021 doi

  48. [62]

    Mag.] 10.1080/14786440109462720 , 2, 559

    Pearson K., 1901, @doi [Phil. Mag.] 10.1080/14786440109462720 , 2, 559

    1901 doi

  49. [63]

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

    doi

  50. [64]

    Pillepich A., et al., 2018b, @doi [ ] 10.1093/mnras/stx3112 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475..648P 475, 648

    doi

  51. [65]

    Planck Collaboration et al., 2014, @doi [ ] 10.1051/0004-6361/201321529 , https://ui.adsabs.harvard.edu/abs/2014A&A...571A...1P 571, A1

    2014 doi

  52. [66]

    Planck Collaboration et al., 2016, @doi [ ] 10.1051/0004-6361/201525830 , https://ui.adsabs.harvard.edu/abs/2016A&A...594A..13P 594, A13

    2016 doi

  53. [69]

    Rogers B., Ferreras I., Pasquali A., Bernardi M., Lahav O., Kaviraj S., 2010b, @doi [ ] 10.1111/j.1365-2966.2010.16436.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.405..329R 405, 329

    2010

  54. [70]

    M., et al., 2015, @doi [ ] 10.1093/mnras/stv2056 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.454.1038R 454, 1038

    Rosas-Guevara Y. M., et al., 2015, @doi [ ] 10.1093/mnras/stv2056 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.454.1038R 454, 1038

    2015 doi

  55. [71]

    G., Schaye J., McAlpine S., Dalla Vecchia C., Frenk C

    Rosas-Guevara Y., Bower R. G., Schaye J., McAlpine S., Dalla Vecchia C., Frenk C. S., Schaller M., Theuns T., 2016, @doi [ ] 10.1093/mnras/stw1679 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.462..190R 462, 190

    2016 doi

  56. [72]

    Salim S., 2014, @doi [Serbian Astronomical Journal] 10.2298/SAJ1489001S , https://ui.adsabs.harvard.edu/abs/2014SerAJ.189....1S 189, 1

    2014 doi

  57. [73]

    S \'a nchez-Bl \'a zquez P., et al., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10699.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.371..703S 371, 703

    2006

  58. [74]

    Scannapieco C., et al., 2012, @doi [ ] 10.1111/j.1365-2966.2012.20993.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.423.1726S 423, 1726

    2012

  59. [75]

    G., Theuns T., Crain R

    Schaller M., Dalla Vecchia C., Schaye J., Bower R. G., Theuns T., Crain R. A., Furlong M., McCarthy I. G., 2015, @doi [ ] 10.1093/mnras/stv2169 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.454.2277S 454, 2277

    2015 doi

  60. [77]

    Schaye J., 2004, @doi [ ] 10.1086/421232 , https://ui.adsabs.harvard.edu/abs/2004ApJ...609..667S 609, 667

    2004 doi

  61. [78]

    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

  62. [80]

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

    2015 doi

  63. [81]

    Scholz-D \' az L., Mart \' n-Navarro I., Falc \'o n-Barroso J., 2023, @doi [ ] 10.1093/mnras/stac3422 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.518.6325S 518, 6325

    2023 doi

  64. [82]

    Sharbaf Z., Ferreras I., Lahav O., 2023, @doi [ ] 10.1093/mnras/stad2668 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.526..585S 526, 585

    2023 doi

  65. [83]

    A., et al., 2013, @doi [ ] 10.1088/0004-6256/146/2/32 , https://ui.adsabs.harvard.edu/abs/2013AJ....146...32S 146, 32

    Smee S. A., et al., 2013, @doi [ ] 10.1088/0004-6256/146/2/32 , https://ui.adsabs.harvard.edu/abs/2013AJ....146...32S 146, 32

    2013 doi

  66. [84]

    S., Dav \'e R., 2015, @doi [ ] 10.1146/annurev-astro-082812-140951 , https://ui.adsabs.harvard.edu/abs/2015ARA&A..53...51S 53, 51

    Somerville R. S., Dav \'e R., 2015, @doi [ ] 10.1146/annurev-astro-082812-140951 , https://ui.adsabs.harvard.edu/abs/2015ARA&A..53...51S 53, 51

    2015 doi

  67. [85]

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

    2005

  68. [87]

    Springel V., et al., 2018, @doi [ ] 10.1093/mnras/stx3304 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475..676S 475, 676

    2018 doi

  69. [88]

    Strateva et al., 2001, @doi [ ] 10.1086/323301 , https://ui.adsabs.harvard.edu/abs/2001AJ....122.1861S 122, 1861

    2001 doi

  70. [89]

    A., et al., 2020, @doi [ ] 10.1093/mnras/staa374 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.1888T 493, 1888

    Terrazas B. A., et al., 2020, @doi [ ] 10.1093/mnras/staa374 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.1888T 493, 1888

    2020 doi

  71. [90]

    Torrey P., Vogelsberger M., Genel S., Sijacki D., Springel V., Hernquist L., 2014, @doi [ ] 10.1093/mnras/stt2295 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.438.1985T 438, 1985

    2014 doi

  72. [91]

    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

  73. [92]

    W., Theuns T., Bower R

    Trayford J. W., Theuns T., Bower R. G., Crain R. A., Lagos C. d. P., Schaller M., Schaye J., 2016, @doi [ ] 10.1093/mnras/stw1230 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.460.3925T 460, 3925

    2016 doi

  74. [93]

    Vazdekis A., Koleva M., Ricciardelli E., R \"o ck B., Falc \'o n-Barroso J., 2016, @doi [ ] 10.1093/mnras/stw2231 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.463.3409V 463, 3409

    2016 doi

  75. [94]

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

    2014 doi

  76. [95]

    Volonteri M., Dubois Y., Pichon C., Devriendt J., 2016, @doi [ ] 10.1093/mnras/stw1123 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.460.2979V 460, 2979

    2016 doi

  77. [96]

    Weinberger R., et al., 2017, @doi [ ] 10.1093/mnras/stw2944 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.465.3291W 465, 3291

    2017 doi

  78. [97]

    Wiersma R. P. C., Schaye J., Theuns T., Dalla Vecchia C., Tornatore L., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15331.x , https://ui.adsabs.harvard.edu/abs/2009MNRAS.399..574W 399, 574

    2009

  79. [98]

    J., Lagos C

    Wright R. J., Lagos C. d. P., Davies L. J. M., Power C., Trayford J. W., Wong O. I., 2019, @doi [ ] 10.1093/mnras/stz1410 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.487.3740W 487, 3740

    2019 doi

  80. [99]

    G., et al., 2000, @doi [ ] 10.1086/301513 , https://ui.adsabs.harvard.edu/abs/2000AJ....120.1579Y 120, 1579

    York D. G., et al., 2000, @doi [ ] 10.1086/301513 , https://ui.adsabs.harvard.edu/abs/2000AJ....120.1579Y 120, 1579

    2000 doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.