Pith. sign in

REVIEW 3 major objections 5 minor 43 references

Deep Extragalactic VIsible Legacy Survey (DEVILS): Satellite Quenching at Intermediate Redshift

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Pith's one-line read Satellite galaxies in DEVILS and GAMA show star-formation suppressed by ~0.5 dex relative to isolated galaxies, with suppression growing to ~1 dex in the most massive halos and satellite passive fractions rising ~10-15% over the last ~5…

desk verdict Solid homogeneous measurement of satellite quenching; the time-evolution claim is fragile and should be softened. read the letter →

arxiv 2507.20822 v1 pith:Z7CQUKC7 submitted 2025-07-28 astro-ph.GA

classification astro-ph.GA
keywords satellitequenchinggalaxyevolutionDEVILSsurveyGAMApassivefractionstar-formingmainsequencedarkmatterhalomassintermediateredshift
topics Dark Matter
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 argues that the environmental shutdown of star formation in satellite galaxies has been operating continuously over the past five billion years, not just in the local universe. Matching the high-completeness DEVILS survey at intermediate redshift (0.3

What carries the argument

The argument rests on three matched tools. First, friends-of-friends group finding applied identically to DEVILS and GAMA, with halo masses estimated from the scaled proxy $M_{\rm halo}\sim A\,R_{50}\,\sigma^2$, where $R_{50}$ is the radius containing half the group members, $\sigma$ the galaxy velocity dispersion, and $A$ a multiplicity- and redshift-dependent scaling; only groups with N>2 are used to constrain halo masses. Second, galaxy stellar masses and star-formation rates from ProSpect SED fitting with an AGN component, run in the same way for both surveys across >20 UV-FIR bands. Third, passive galaxies defined as lying >2$\sigma$ below the star-forming main sequence, where $\sigma$ is derived from a two-component Gaussian mixture model at each epoch. These are the load-bearing pieces that make the cross-epoch comparison possible.

What would settle it

Measure dark matter halo masses for the same DEVILS groups with an independent mass tracer, such as X-ray emission, the Sunyaev-Zel'dovich effect, or weak lensing, and compare them to the M_halo ~ A R50 $sigma^{2}$ proxy as a function of redshift; if the proxy bias changes between z~0 and z~0.5, the apparent growth of satellite suppression could be a selection artifact rather than an evolutionary trend.

Watch

Extended reading notes

Core claim

The central claim is that satellite galaxies have systematically suppressed star formation relative to isolated centrals at the same stellar mass, that this suppression grows with dark matter halo mass, and that at fixed stellar and halo mass the suppression becomes stronger as the universe ages. Quantitatively: a ~0.5 dex offset in log10(SFR/Msun/yr) for all satellites, rising to ~1 dex in halos with log10(Mhalo/Msun) between 14 and 15, with the offset evolving by ~0.3 dex toward the present epoch. Correspondingly, the fraction of passive satellites at 10<log10(M*/Msun)<11 increases by ~10-15% over the last ~5 Gyr, whereas isolated centrals show flat or declining passive fractions. The paper presents these as the first results from comparing DEVILS and GAMA with identical galaxy property and environment pipelines, consistent with prior observations and numerical simulations.

Load-bearing premise

The analysis assumes the DEVILS group catalogue, still in preparation, recovers true dark matter halos and satellite/central assignments with the same fidelity as the GAMA G3C catalogue, so that any change in the satellite passive fraction with redshift reflects real galaxy evolution rather than a drift in group purity or halo mass calibration.

Editorial extensions

If this is right

  • Satellite quenching has been acting continuously for the last ~5 Gyr, so models must reproduce a suppression that persists rather than an event confined to the local universe.
  • The ~0.3 dex growth in the satellite-isolated SFR offset toward today means the rate of environmental quenching, not just its accumulated effect, is measurable.
  • Because suppression is strongest in 14<log10(Mhalo/Msun)<15 halos, the most massive group and cluster environments play the dominant role in building the passive satellite population.
  • The observed ~10-15% rise in satellite passive fraction, against flat or declining central fractions, gives a direct target for simulations; the paper finds TNG100 and Shark match best, while SIMBA over-quenches and EAGLE under-quenches.

Reading between the lines

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

  • Extending the same matched-pipeline comparison to the other DEVILS fields, once their group catalogues are ready, would test whether the D10 result is representative of the broader intermediate-redshift population.
  • If the redshift-dependent suppression is real, the halo mass dependence implies that satellites in more massive halos either quench faster after infall or were accreted earlier; distinguishing those needs infall-time information, e.g., from galaxy-galaxy lensing or merger trees.
  • The method of comparing satellite-to-central passive fractions, rather than absolute passive fractions, could be applied to higher-redshift surveys to build a single continuous quenching timeline from z~0 to z~1.
  • A direct prediction follows: at fixed stellar mass, satellites in 14<log10(Mhalo)<15 halos should show progressively older star-formation histories than those in 13-14 halos, testable with spectral indices or Dn4000 strengths.
Share X Bluesky LinkedIn Reddit HN

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 DEVILS D10 (0.3 < z < 0.5) and GAMA (0 < z < 0.2), with ProSpect-derived stellar masses and SFRs and friends-of-friends group catalogues, to measure the SFR offset between satellite and isolated central galaxies, its dependence on halo mass, and the time evolution of satellite passive fractions. The abstract claims approximately 0.5 dex suppression in log10(SFR) for satellites at fixed stellar mass, stronger suppression in more massive halos (up to ~1 dex), and an increase in suppression and passive fraction with time over the last ~5 Gyr. The analysis uses running medians, 5,000-realisation Monte Carlo error bands, and comparisons with literature passive fractions and several simulations.

Significance. If the central claims hold, this would be one of the first direct, methodologically homogeneous measurements of satellite quenching across 0 < z < 0.5, using the same SED-fitting code and the same FoF group-finding approach for both surveys. The ~0.5 dex suppression and the halo-mass dependence are visible in the figures and supported by the Monte Carlo bands, so these parts are a useful and credible contribution. The main weakness is the time-evolution claim, which rests on a single DEVILS redshift bin and is sensitive to the passive-galaxy selection definition; this part needs to be either made quantitatively robust or substantially toned down. The comparison with simulations is a useful consistency check, though the different passive selection used for simulations complicates the normalisation comparisons.

major comments (3)
  1. [Section 3.2, Table 1, Figure 4] The headline claim (iii) that satellite suppression and passive fraction increase with time over the last ~5 Gyr is not supported by the quoted numbers. In Table 1 the DEVILS 0.3<z<0.5 offsets are consistent within 1 sigma with the GAMA 0.1<z<0.2 offsets (for log Mhalo > 13: 0.58 ± 0.14 versus 0.69 ± 0.02; for 13 < log Mhalo < 14: 0.46 ± 0.18 versus 0.55 ± 0.02; for 14 < log Mhalo < 15: 0.85 ± 0.17 versus 0.98 ± 0.05). The SFR-offset evolution is therefore at most a two-bin GAMA trend extrapolated backward, not a demonstrated 5 Gyr trend. The passive-fraction version is similarly fragile: Section 3.2 reports that with a fixed 1 dex below the SFS selection, the weak increase between the 0.3<z<0.5 and 0.1<z<0.2 bins is removed, leaving only the GAMA 0.001<z<0.1 to 0.1<z<0.2 increase. I ask the authors to either quote the formal significance of the three-point slope in Figure 4, including the covariance between points, or reframe claim (iii) as a low-redshift GAMA trend with a DEVILS anchor consistent with no evolution, and adjust the abstract and summary accordingly.
  2. [Section 2.1.2] The entire comparison rests on the assumption that the DEVILS D10 FoF group catalogue (Bravo et al., in preparation) recovers halos and assigns central/satellite status with the same fidelity as GAMA's public G3C catalogue, and that the M_halo ~ A R50 sigma^2 proxy is calibrated identically at both epochs. The paper states that Bravo et al. optimise linking lengths and test recovery on Shark light cones, but no recovery statistics, purity/completeness as a function of redshift, or calibration of A are presented here. Given that the DEVILS group catalogue is not yet public, the redshift evolution in Section 3.2 could in principle be an artefact of redshift-dependent group completeness or halo mass bias. Please include quantitative validation from the mocks (e.g., satellite classification completeness and halo mass bias versus redshift), or explicitly model this systematic in the error budget.
  3. [Section 3.2.2, Figures 7 and 8] The simulations are compared to the observations using a different passive-galaxy definition: the simulations use log10(sSFR/yr^-1) < -11 + 0.5z following Wright et al. (2022), while the observations use a redshift-dependent 2 sigma offset from the fitted star-forming sequence (Appendix A). Since the claimed simulation ranking (e.g., SIMBA 2-3 sigma above, EAGLE 2-3 sigma below the observed trend) is stated in normalisation, the selection mismatch can masquerade as a difference in feedback physics. Either apply the identical passive selection to the simulations and observations, or restrict the simulation comparison to slopes and state this limitation explicitly when quoting normalisation offsets.
minor comments (5)
  1. [Sections 2.1.1 and 2.2.1] The phrase 'identical manner' should be qualified: the text itself notes that GAMA and DEVILS have different photometric depths and rest-frame coverage, which is a caveat on the claimed reduction of methodology bias.
  2. [Figure 3] Figure 3 does not show the Monte Carlo error polygons that appear in Figure 2, even though the text uses Figure 3 to argue that the offsets are similar across epochs; adding the error ranges or referring explicitly to Table 1 would make this claim easier to verify.
  3. [Section 3.1] The text says 'We do see tentative hints' of an increasing suppression and later says the offset increase is 'significant when considering the calculated errors'; these two statements are in tension and should be replaced with one significance statement tied to a specific number.
  4. [Table 2] The formatting of the asymmetric errors in Table 2 is difficult to read; a standard f_plus/f_minus layout or separate error columns would improve clarity.
  5. [Summary and Conclusions] The Summary repeats claim (iii) as established ('this suppression increases over the last ~5 Gyr') even though Section 3.2 advises caution about the selection dependence; harmonise the language with the actual significance.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the measurements are direct observables with external checks, and the flagged selection caveat is a robustness limitation rather than a circular reduction.

full rationale

The paper is an observational study, not a derivation. The three headline quantities—satellite-minus-isolated SFR offset, halo-mass dependence, and passive-fraction evolution—are computed directly from DEVILS/GAMA catalogues; none of these quantities is used as an input to define the samples or the passive selection. The model-dependent inputs are the ProSpect SED fits, the FoF group finder, the M_halo ~ A R50 sigma^2 proxy, and the SFS-based passive selection line; each is independently calibrated or standard and does not encode the satellite-central offset or the passive fraction. The paper explicitly tests an alternative 1-dex passive selection and reports that 'the weak increase in satellite passive fraction between the 0.3<z<0.5 bin and 0.1<z<0.2 bin is removed'—this is a robustness caveat, not evidence that the result is baked in by construction. Self-citations to Robotham et al. (2011), Thorne et al. (2021, 2022), Davies et al. (2019b), and Wright et al. (2022) are methodological or comparative references; none is invoked as a uniqueness theorem or as the sole justification for the central claim. Results are also checked against external SDSS literature (Oxland et al. 2024; Wetzel et al. 2013; McGee et al. 2011) and against multiple independent simulations, so the central trends are not solely self-referential. No equation-level reduction of a claim to its own input could be identified.

Assumptions & free parameters 4 free parameters · 7 assumptions · 0 invented entities

The central result rests on previously published or in-preparation survey products (ProSpect SED fits, FoF group catalogues, halo mass calibrations) plus data-derived selection thresholds. There are no new physical entities or forces. The main input choices made in this paper are the completeness limits and the 2-sigma passive threshold; the time-evolution linear fits are outputs, not fitted inputs.

free parameters (4)
  • Stellar mass completeness limit = 10 < log10(M*/Msun) < 11; evolving limit fit from 90% of the g-i colour distribution per lookback bin
    Sets which galaxies enter all analyses; a different limit would change the satellite/isolated contrast and passive fractions.
  • Halo mass completeness limit = log10(Mhalo/Msun) > 13; limit from number-count turnover per lookback bin, linearly fit
    Sets which halos are analyzed and therefore the halo-mass-dependent results.
  • Passive galaxy selection threshold = 2 sigma below the SFS, corresponding to 0.61, 0.60, 0.66 dex offsets in the three redshift bins
    Defines the passive fraction; authors test a simpler 1 dex alternative and find the relative satellite/central trend survives.
  • Halo mass scaling factor A = multiplicity- and redshift-dependent calibration from Robotham et al. (2011)
    M_halo ~ A R50 sigma^2 is adopted for both surveys; the halo mass bins and their suppression trends depend on this calibration.
assumptions (7)
  • domain assumption The DEVILS-optimized friends-of-friends linking lengths recover true galaxy groups from mock SHARK light cones, so N>2 groups trace common dark matter halos.
    Sec 2.1.2; all satellite and halo-mass measurements rely on group recovery. Mock-based validation is cited but not provided here.
  • domain assumption The most massive group member is the halo central; all other members are satellites.
    Sec 2.1.2 and 2.2.2; defines the central and satellite samples used throughout.
  • domain assumption Galaxies not assigned to any N>1 group are isolated centrals and form a clean unquenched control sample.
    Baseline for the SFR offsets; incomplete group membership at higher z would hide satellites in this control population and dilute the signal.
  • domain assumption ProSpect SED fitting with an AGN component yields reliable stellar masses and SFRs in both surveys.
    Adopted from Bellstedt et al. (2020b) and Thorne et al. (2021, 2022); the entire SFR suppression measurement is made in this plane.
  • domain assumption The SFR distribution at fixed stellar mass is a two-component Gaussian mixture, with the high-SFR component defining the main sequence and passive galaxies falling >2 sigma below it.
    Appendix A; defines passive fractions and the time-evolution result. The authors test one alternative threshold.
  • ad hoc to paper Halo mass completeness can be inferred from the turnover in N>1 group number counts per lookback bin.
    Sec 2.3; a pragmatic completeness estimator, specific to this paper, that sets the log10(Mhalo/Msun)>13 selection.
  • standard math Flat Lambda-CDM cosmology with H0=70 km/s/Mpc, Omega_Lambda=0.7, Omega_M=0.3.
    Stated in Section 1; standard cosmology for distances and volumes.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Deep Extragalactic VIsible Legacy Survey (DEVILS): Satellite Quenching at Intermediate Redshift." pith.science (2026). https://pith.science/paper/Z7CQUKC7

@misc{pith2026250720822,
  author       = {Pith},
  title        = {Pith review of: Deep Extragalactic VIsible Legacy Survey (DEVILS): Satellite Quenching at Intermediate Redshift},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Z7CQUKC7}},
  note         = {Machine review of arXiv:2507.20822}
}
abstract

Determining the processes by which galaxies transition from a star-forming to a quiescent state (quenching) is paramount to our understanding of galaxy evolution. One of the key mechanisms by which this takes place is via a galaxy's interactions with a local, over-dense environment (satellite or environmental quenching). In the very local Universe, we see these processes in action, and can also observe their effects via the distribution of satellite galaxy properties. However, extending similar analyses outside of the local Universe is problematic, largely due to the difficulties in robustly defining environments with small and/or incomplete spectroscopic samples. We use new environmental metrics from the high-completeness Deep Extragalactic VIsible Legacy Survey (DEVILS) to explore the properties of satellite galaxies at intermediate redshift (0.3$<$z$<$0.5) and compare directly to the Galaxy And Mass Assembly Survey (GAMA) at 0$<$z$<$0.2. Importantly, both the galaxy properties and environmental metrics in DEVILS and GAMA are derived in an identical manner, reducing any methodology biases. We find: i) that satellite galaxies in DEVILS and GAMA show suppressed star-formation in comparison to isolated systems at the same stellar mass, by $\sim$0.5dex in log$_{10}$(SFR/M$_{\odot}$yr$^{-1}$), ii) that this suppression is strongest in higher mass dark matter halos (up to $\sim$1dex in log$_{10}$(SFR/M$_{\odot}$yr$^{-1}$) in the most massive halos) and iii) that at fixed stellar and halo mass, this suppression increases with time - with satellite passive fractions increasing by $\sim$10-15\% over the last $\sim$5Gyr. This is consistent with previous observations and numerical simulations.

Figures

Figures reproduced from arXiv: 2507.20822 by the authors.

Figure 1
Figure 1. The redshift-mass distribution of galaxies (top row) and dark matter halos (bottom row) in GAMA (left column) and DEVILS (right column). All properties (Stellar mass, redshift, halo mass) are derived using the same method in both GAMA and DEVILS. Stellar mass, halo mass and redshift ranges used in this work are shown in coloured rectangles. Green and red lines show the stellar and halo mass completeness limits for G… view at source ↗
Figure 2
Figure 2. The SFR-M★ plane at the three different redshifts probed in this work. Top two rows are taken from GAMA, bottom row from DEVILS. The left panel shows all galaxies in the sample. Grey points show all galaxies, green points are isolated central galaxies, while red points show satellite galaxies. The lines display the running median of SFR for each sample. The right panel shows just the satellite galaxies coloured by h… view at source ↗
Figure 3
Figure 3. The log10(SFR/M⊙ yr−1 ) offset between the running median SFR for isolated central and satellite galaxies. The left panel shows the offset for all satellites at the three redshifts probed in this work, while the right panel shows the same but sub-divided into two halo mass ranges. The grey shaded region is where our samples become incomplete in stellar mass. MNRAS 000, 1–15 (2025) [PITH_FULL_IMAGE:figures/full_fig_… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: The evolution of the median log10(SFR/M⊙ yr−1 ) offset between isolated and satellite systems at 10<log10(M★/M⊙)<11, split in the the halo mass ranges used in this work. Linear regression fits are shown as solid lines to highlight the observational trends. mass). To fu…
Figure 5
Figure 5. Figure 5: The SFR-M★ plane at the three difference redshifts probed in this work showing the selection of passive galaxies. At each redshift we identify the star-forming population and fit the locus of the SFS (blue line), we then define passive galaxies as falling >2𝜎 below thi…
Figure 4
Figure 4. Figure 4: figure 4. This includes satellite in all log [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 6
Figure 6. Figure 6: Top: Evolution of the 10<log10(M★/M⊙)<11 passive fraction from 𝑧∼1.5 to today showing a comparison between our current results and existing literature passive fractions. Points are coloured in halo mass ranges, such that points/lines of the same colours can be compared…
Figure 7
Figure 7. Figure 7: Evolution of the 10<log10(M★/M⊙)<11 passive fraction from 𝑧∼1.5 to today showing a comparison between our current results and numerical simulations. For ease of comparison, we split our halo mass ranges into three separate panels. Observational data is shown faintly in…
Figure 8
Figure 8. Figure 8: Similar to [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

43 extracted references · 9 canonical work pages

  1. [1]

    N., Adelman-McCarthy J

    Abazajian K. N., Adelman-McCarthy J. K., Agüeros M. A., Allam S. S., AllendePrietoC.,AnD.,AndersonK.S.J.,etal.,2009,ApJS,182,543. doi:10.1088/0067-0049/182/2/543 Bahé Y. M., McCarthy I. G., 2015, MNRAS, 447,

  2. [5]

    MNRAS000, 1–15 (2025) 16L

    This paper has been typeset from a TEX/LATEX file prepared by the author. MNRAS000, 1–15 (2025) 16L. J. M. Davies Figure A1.Methodologyforselectingpassivegalaxiesateachepoch.Left:theSFR-M ★ plane.InΔlog 10(M★/M⊙)=0.5binsbetween9<log 10(M★/M⊙)<11we apply a Gaussian mixture model to log10(SFRs) to define the star-forming and passive populations. Means and s...

  3. [11]

    J., Kovač, K., et al

    doi:10.3847/1538- 4357/ab1f8d Kauffmann,G.,Heckman,T.M.,White,S.D.M.,etal.2003,MNRAS,341, 33 Kauffmann,G.,Heckman,T.M.,White,S.D.M.,etal.2003,MNRAS,341, 54 Kauffmann,G.,White,S.D.M.,Heckman,T.M.,etal.2004,MNRAS,353, 713 Knobel, C., Lilly, S. J., Kovač, K., et al. 2013, ApJ, 769, 24 Lagos C. del P., Tobar R. J., Robotham A. S. G., Obreschkow D., Mitchell P...

  4. [33]

    doi:10.1088/0004-637X/782/1/33 Liske J., et al., 2015, MNRAS, 452, 2087 Lotz M., Remus R.-S., Dolag K., Biviano A., Burkert A., 2019, MNRAS, 488,

  5. [34]

    2006, Proc

    doi:10.1086/516585 Sharp, R., Saunders, W., Smith, G., et al. 2006, Proc. SPIE, 6269, 62690G Siudek M., Małek K., Pollo A., Iovino A., Haines C. P., Bolzonella M., Cucciati O., et al., 2022, A&A, 666, A131. doi:10.1051/0004- 6361/202243613 Taylor, E. N., Hopkins, A. M., Baldry, I. K., et al. 2015, MNRAS, 446, 2144 Teimoorinia, H., Bluck, A. F. L., & Ellis...

  6. [44]

    MNRAS000, 1–15 (2025)

  7. [46]

    D., Nelson D., Genel S., Mari- nacci F., Rodriguez-Gomez V., et al., 2021a, MNRAS, 500,

    doi:10.18727/0722- 6691/5126 Donnari M., Pillepich A., Joshi G. D., Nelson D., Genel S., Mari- nacci F., Rodriguez-Gomez V., et al., 2021a, MNRAS, 500,

  8. [96]

    doi:10.1093/mnras/stz3264 Bluck A. F. L., Maiolino R., Piotrowska J. M., Trussler J., Ellison S. L., Sánchez S. F., Thorp M. D., et al., 2020b, MNRAS, 499,

Show all 43 references
  1. [128]

    L., 2021, MNRAS, 500,

    doi:10.1071/AS10046 Cleland C., McGee S. L., 2021, MNRAS, 500,

  2. [136]

    F., Davies L

    doi:10.3847/1538-4357/aad80d FossatiM.,WilmanD.J.,MendelJ.T.,SagliaR.P.,GalametzA.,BeifioriA., BenderR.,etal.,2017,ApJ,835,153.doi:10.3847/1538-4357/835/2/153 Fuentealba-Fuentes M. F., Davies L. J. M., Robotham A. S. G., Cook R. H. W., Bellstedt S., Lagos C. D. P., Bravo M., e...

  3. [153]

    V., Nagai D., 2018, MNRAS, 475,

    doi:10.1086/522027 Zinger E., Dekel A., Kravtsov A. V., Nagai D., 2018, MNRAS, 475,

  4. [230]

    N., Phillipps, S., Kelvin, L

    doi:10.1093/mnras/staa2806 Bremer, M. N., Phillipps, S., Kelvin, L. S., et al. 2018, MNRAS, 476, 12 Brinchmann,J.,Charlot,S.,White,S.D.M.,etal.2004,MNRAS,351,1151 Brown T., et al., 2017, MNRAS, 466, 1275 Cameron E., 2011, PASA, 28,

  5. [256]

    doi:10.1093/mnras/stab1601 Davies L. J. M., Thorne J. E., Bellstedt S., Bravo M., Robotham A. S. G., Driver S. P., Cook R. H. W., et al., 2022, MNRAS, 509,

  6. [336]

    2009, MNRAS, 393, 1302 Woo, J., Dekel, A., Faber, S

    doi:10.1093/mnras/stt469 Wolf, C., Aragón-Salamanca, A., Balogh, M., et al. 2009, MNRAS, 393, 1302 Woo, J., Dekel, A., Faber, S. M., & Koo, D. C. 2015, MNRAS, 448, 237 WrightR.J.,LagosC.delP.,DaviesL.J.M.,PowerC.,TrayfordJ.W.,Wong O. I., 2019, MNRAS, 487,

  7. [521]

    M., Elvis M., Gi- avalisco M., et al., 2007, ApJS, 172,

    doi:10.1093/mnras/stu2058 Scoville N., Aussel H., Brusa M., Capak P., Carollo C. M., Elvis M., Gi- avalisco M., et al., 2007, ApJS, 172,

  8. [540]

    E., Robotham A

    doi:10.1093/mnras/stab1294 Thorne J. E., Robotham A. S. G., Davies L. J. M., Bellstedt S., Brown M. J. I., Croom S. M., Delvecchio I., et al., 2022, MNRAS, 509,

  9. [590]

    G., Baugh, C

    doi:10.1093/mnras/staa3267 Cole, S., Lacey, C. G., Baugh, C. M., & Frenk, C. S. 2000, MNRAS, 319, 168 Cook R. H. W., Cortese L., Catinella B., Robotham A., 2020, MNRAS, 493,

  10. [591]

    J., Kovač, K., et al

    doi:10.1111/j.1365-2966.2010.17932.x Peng, Y.-j., Lilly, S. J., Kovač, K., et al. 2010, ApJ, 721, 193 Peng, Y.-j., Lilly, S. J., Renzini, A., & Carollo, M. 2012, ApJ, 757, 4 Peng, Y., Maiolino, R., & Cochrane, R. 2015, Nature, 521, 192 Pillepich A., Springel V., Nelson D., Gen...

  11. [905]

    2004, Proc

    doi:10.1093/mnras/staa1116 Saunders, W., Bridges, T., Gillingham, P., et al. 2004, Proc. SPIE, 5492, 389 Schaefer, A. L., Croom, S. M., Allen, J. T., et al. 2017, MNRAS, 464, 121 SchayeJ.,CrainR.A.,BowerR.G.,FurlongM.,SchallerM.,TheunsT.,Dalla Vecchia C., et al., 2015, MNRAS, 446,

  12. [969]

    K., Glazebrook, K., Brinkmann, J., et al

    doi:10.1093/mnras/stu2293 Baldry, I. K., Glazebrook, K., Brinkmann, J., et al. 2004, ApJ, 600, 681 Baldry, I. K., Robotham, A. S. G., Hill, D. T., et al. 2010, MNRAS, 404, 86 Baldry, I. K., Liske, J., Brown, M. J. I., et al. 2018, MNRAS, 474, 3875 Balogh, M., Eke, V., Miller, ...

  13. [1881]

    doi:10.1093/mnras/sty2957 Davies L. J. M., Robotham A. S. G., Lagos C. del P., Driver S. P., Stevens A. R. H., Bahé Y. M., Alpaslan M., et al., 2019, MNRAS, 483,

  14. [1934]

    doi:10.1088/0004-637X/698/2/1934 Muzzin A., van der Burg R. F. J., McGee S. L., Balogh M., Franx M., HoekstraH.,HudsonM.J.,etal.,2014,ApJ,796,65.doi:10.1088/0004- 637X/796/1/65 Nandra, K., Georgakakis, A., Willmer, C. N. A., et al. 2007, ApJ, 660, L11 Nelson D., Springel V., P...

  15. [1937]

    2008, MNRAS, 387, 1431 Davé R., Anglés-Alcázar D., Narayanan D., Li Q., Rafieferantsoa M

    doi:10.1093/mnras/stv725 Dalla Vecchia, C., & Schaye, J. 2008, MNRAS, 387, 1431 Davé R., Anglés-Alcázar D., Narayanan D., Li Q., Rafieferantsoa M. H., Appleby S., 2019, MNRAS, 486,

  16. [2827]

    doi:10.1093/mnras/stz937 Davies L. J. M., et al., 2015, MNRAS, 452, 616 Davies L. J. M., et al., 2016, MNRAS, 455, 4013 Davies L. J. M., et al., 2018, MNRAS, 480, 768 Davies L. J. M., Lagos C. del P., Katsianis A., Robotham A. S. G., Cortese L., Driver S. P., Bremer M. N., et ...

  17. [2891]

    doi:10.1093/mnras/stac2042 YangX.,MoH.J.,vandenBoschF.C.,PasqualiA.,LiC.,BardenM.,2007, ApJ, 671,

  18. [3010]

    C., Aquino, D., Yang, X., et al

    doi:10.1093/mnras/stab653 van den Bosch, F. C., Aquino, D., Yang, X., et al. 2008, MNRAS, 387, 79 van der Burg R. F. J., Rudnick G., Balogh M. L., Muzzin A., Lidman C., Old L. J., Shipley H., et al., 2020, A&A, 638, A112. doi:10.1051/0004- 6361/202037754 Wake, D. A., van Dokku...

  19. [3235]

    doi:10.1093/mnras/staa1466 Bellstedt S., Robotham A. S. G., Driver S. P., Thorne J. E., Davies L. J. M., Lagos C. del P., Stevens A. R. H., et al., 2020b, MNRAS, 498,

  20. [3551]

    S., et al

    doi:10.1093/mnras/stae1024 Lang, P., Wuyts, S., Somerville, R. S., et al. 2014, ApJ, 788, 11 Larson R. B., Tinsley B. M., Caldwell C. N., 1980, ApJ, 237, 692 Lewis, I. J., Cannon, R. D., Taylor, K., et al. 2002, MNRAS, 333, 279 LinL.,JianH.-Y.,FoucaudS.,NorbergP.,BowerR.G.,Col...

  21. [3573]

    del P., Bravo M., Tobar R., Obreschkow D., Power C., Robotham A

    doi:10.1093/mnras/sty2440 Lagos C. del P., Bravo M., Tobar R., Obreschkow D., Power C., Robotham A. S. G., Proctor K. L., et al., 2024, MNRAS, 531,

  22. [3651]

    R., Ellison S

    doi:10.1093/mnras/stae747 MNRAS000, 1–15 (2025) DEVILS: Satellite Quenching15 Patton D. R., Ellison S. L., Simard L., McConnachie A. W., Mendel J. T., 2011, MNRAS, 412,

  23. [3654]

    doi:10.1093/mnras/stx3329 APPENDIX A: SELECTION OF PASSIVE GALAXIES In this section we detail the methodology used to define the pas- sive galaxies, summarised in Figure

  24. [3740]

    doi:10.1093/mnras/stz1410 WrightR.J.,LagosC.delP.,PowerC.,StevensA.R.H.,CorteseL.,Poulton R. J. J., 2022, MNRAS, 516,

  25. [4004]

    doi:10.1093/mnras/staa3006 Donnari M., Pillepich A., Nelson D., Marinacci F., Vogelsberger M., Hern- quist L., 2021b, MNRAS, 506,

  26. [4077]

    M., et al., 2017, ApJ, 844, 48 Robotham A

    doi:10.1093/mnras/stx2656 Poggianti B. M., et al., 2017, ApJ, 844, 48 Robotham A. S. G., et al., 2014, MNRAS, 444, 3986 Robotham, A., Driver, S. P., Norberg, P., et al. 2010, PASA, 27, 76 Robotham, A. S. G., Norberg, P., Driver, S. P., et al. 2011, MNRAS, 416, 2640 Robotham,A....

  27. [4168]

    M., Koo, D

    doi:10.1093/mnras/stx1370 Barro, G., Faber, S. M., Koo, D. C., et al. 2017, ApJ, 840, 47 MNRAS000, 1–15 (2025) 14L. J. M. Davies Barsanti, S., Owers, M. S., Brough, S., et al. 2018, ApJ, 857, 71 Bell, E. F., Wolf, C., Meisenheimer, K., et al. 2004, ApJ, 608, 752 BellstedtS.,Dr...

  28. [4392]

    doi:10.1093/mnras/stab3145 Davies L. J. M., Thorne J. E., Bellstedt S., Bravo M., Robotham A. S. G., Driver S. P., Cook R. H. W., et al., 2025a MNRAS, XXX, XXx. doi:10.1093/mnras/stabXXX Davies L. J. M., Thorne J. E., Bellstedt S., Bravo M., Robotham A. S. G., Driver S. P., Co...

  29. [4760]

    M., Willmer, C

    doi:10.1093/mnras/stab1950 Faber, S. M., Willmer, C. N. A., Wolf, C., et al. 2007, ApJ, 665, 265 Fang, J. J., Faber, S. M., Koo, D. C., & Dekel, A. 2013, ApJ, 776, 63 Figueira M., Siudek M., Pollo A., Krywult J., Vergani D., Bolzonella M., Cucciati O., et al., 2024, A&A, 687, ...

  30. [4912]

    P., Pereira M

    doi:10.1093/mnras/stac1659 Haines C. P., Pereira M. J., Smith G. P., Egami E., Babul A., Finoguenov A., ZiparoF.,etal.,2015,ApJ,806,101.doi:10.1088/0004-637X/806/1/101 Henriques, B. M. B., White, S. D. M., Thomas, P. A., et al. 2015, MNRAS, 451, 2663 Katsianis A., Zheng X., Go...

  31. [4940]

    2018, MNRAS, 477, 2684 TurnerS.,SiudekM.,SalimS.,BaldryI.K.,PolloA.,LongmoreS.N.,Malek K., et al., 2021, MNRAS, 503,

    doi:10.1093/mnras/stab3208 Treyer, M., Kraljic, K., Arnouts, S., et al. 2018, MNRAS, 477, 2684 TurnerS.,SiudekM.,SalimS.,BaldryI.K.,PolloA.,LongmoreS.N.,Malek K., et al., 2021, MNRAS, 503,

  32. [5370]

    M., Suzuki T

    doi:10.1093/mnras/stz2070 Mao Z., Kodama T., Pérez-Martínez J. M., Suzuki T. L., Yamamoto N., Adachi K., 2022, A&A, 666, A141. doi:10.1051/0004-6361/202243733 Martin, D. C., Wyder, T. K., Schiminovich, D., et al. 2007, ApJS, 173, 342 McCracken H. J., et al., 2012, A&A, 544, A1...

  33. [5444]

    doi:10.1093/mnras/sty3393 Davies L. J. M., Thorne J. E., Robotham A. S. G., Bellstedt S., Driver S. P., Adams N. J., Bilicki M., et al., 2021, MNRAS, 506,

  34. [5581]

    R., Hogg, D

    doi:10.1093/mnras/staa2620 Blanton, M. R., Hogg, D. W., Bahcall, N. A., et al. 2003, ApJ, 594, 186 Bluck, A. F. L., Mendel, J. T., Ellison, S. L., et al. 2014, MNRAS, 441, 599 Bluck A. F. L., Maiolino R., Sánchez S. F., Ellison S. L., Thorp M. D., Piotrowska J. M., Teimoorinia...

  35. [5596]

    A., Vega-Martínez, C

    doi:10.1093/mnras/staa666 Cora, S. A., Vega-Martínez, C. A., Hough, T., et al. 2018, MNRAS, 479, 2 Cortese L., Catinella B., Smith R., 2021, PASA, 38, e035. doi:10.1017/pasa.2021.18 CrainR.A.,SchayeJ.,BowerR.G.,FurlongM.,SchallerM.,TheunsT.,Dalla Vecchia C., et al., 2015, MNRAS, 450,

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.