Pith. sign in

REVIEW 4 major objections 6 minor 89 references

In FIRE-2 galaxies, star-formation ergodicity is only apparent

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

T0 review · deepseek-v4-flash

2026-08-01 16:22 UTC pith:3FCTTASV

load-bearing objection Useful and honest paper: apparent TM-metric ergodicity in FIRE-2 SFMS deviations is really decreasing variance, but the block-scrambling control has a validation gap and one internal contradiction. the 4 major comments →

arxiv 2607.18005 v1 pith:3FCTTASV submitted 2026-07-20 astro-ph.GA

Ergodicity of FIRE: star formation variations within and between simulated galaxies

Fraser M. Smith , Robert J. Thacker This is my paper
classification astro-ph.GA
keywords ergodicitystar-forming main sequencestar formation historiesThirumalai-Mountain metricblock scramblingFIRE-2 simulationsgalaxy formationstar formation variability
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper asks whether the star-formation history of one simulated galaxy can stand in for the average of many: whether ensemble averages over galaxies match time averages of individual galaxies. Tracking deviations from the star-forming main sequence across 20 FIRE-2 galaxies, the authors find that the Thirumalai-Mountain metric drifts toward zero over cosmic time — the signature usually read as ergodic convergence. But when the time series is block-scrambled to destroy temporal correlation, that convergence disappears. The paper concludes that the apparent ergodicity is produced by decreasing variance as galaxies transition from bursty to smooth star formation, not by true ergodic exploration.

Core claim

The central discovery is that SFMS deviations in FIRE-2 galaxies display apparent, not true, ergodicity. By the TM metric, both short-timescale (10^7 yr) and some long-timescale (10^9 yr) deviations converge toward zero over roughly 10 Gyr, across three SFMS definitions and for disk- and spheroid-dominated morphologies. However, block-scrambling the time series removes the convergence, yielding TM metric exponents below unity. The authors attribute the apparent convergence to decreasing variance with time — the bursty-to-smooth transition and absence of late mergers — so the ensemble average is not constant in the strong sense required for ergodicity.

What carries the argument

The Thirumalai-Mountain (TM) metric, which measures the spread of individual time averages around the ensemble-averaged time average, is the main diagnostic. The paper uses its power-law convergence exponent to classify systems, and block-scrambling of the time series into 500 Myr or 1 Gyr blocks as a control; in a genuinely ergodic system, shuffling blocks should preserve convergence. SFMS deviations are the quantity tracked, defined relative to three main-sequence fits.

Load-bearing premise

The conclusion depends on block-scrambling being a valid way to expose non-ergodicity in short, nonstationary, heteroscedastic time series: if scrambling destroys TM-metric convergence for a process that is genuinely ergodic but has decreasing variance, the paper's distinction between apparent and true ergodicity would not be established.

What would settle it

Take a long, stationary, genuinely ergodic time series of the same length as the FIRE-2 SFMS deviations but engineered to have decreasing variance, block-scramble it, and measure the TM exponent: if scrambling pushes the exponent below unity, the block-scrambling test is a false positive for non-ergodicity. Alternatively, construct a synthetic galaxy population with constant variance in SFMS deviations and check whether TM convergence survives scrambling; survival would contradict the paper's variance-decrease explanation.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • TM-metric convergence to zero alone is not sufficient to establish ergodicity of star-formation deviations; stationarity tests and temporal-scramble checks are needed.
  • Observational estimates that treat a galaxy population's scatter about the main sequence as a proxy for individual galaxy variability may conflate ensemble spread with time variation.
  • The bursty-to-smooth transition in massive galaxies is the likely cause of the apparent convergence, so samples dominated by massive, late-time disks will look artificially more ergodic.
  • Short-timescale SFR indicators converge faster than long-timescale ones, so conclusions about ergodicity depend on the averaging window of the SFR tracer.
  • For currently star-forming galaxies in FIRE-2, one cannot infer an individual galaxy's full star-formation history from the ensemble SFMS at a single epoch.

Where Pith is reading between the lines

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

  • Because the sample is a z=0 disk-selected set with no AGN feedback, the apparent-ergodicity result may not extend to quenched systems or high-redshift populations; adding AGN feedback could turn apparent convergence into genuine non-ergodicity in massive galaxies.
  • The paper leaves open the possibility that alternative ergodicity metrics — designed for nonstationary, finite-length series — could distinguish true from apparent ergodicity more cleanly than block-scrambling alone; a natural test is to apply such metrics to the same FIRE-2 time series.
  • A direct observational analogue would be to reconstruct SFHs for a sample of local galaxies and apply the same TM-metric-plus-scrambling procedure; if observed data also lose convergence under scrambling, the FIRE-2 result is a generic property of galaxy SFHs rather than a simulation artifact.

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

4 major / 6 minor

Summary. The paper uses 20 FIRE-2 zoom-in simulations to test whether deviations from the star-forming main sequence (SFMS) are ergodic, i.e. whether ensemble averages of galaxy SFMS deviations reproduce individual time-averaged histories. Three SFMS definitions are considered (Speagle et al. 2014, Popesso et al. 2023, and a fit to the simulated sample itself), together with short-timescale (~20 Myr) and long-timescale (800 Myr) SFR estimators. The central claim is that the SFMS-deviation time series display apparent ergodic convergence of the Thirumalai-Mountain (TM) metric at late cosmic times, but that this convergence is not true ergodicity: it is driven by decreasing variance accompanying the bursty-to-smooth transition, and block-scrambling the time series removes the convergence. The paper carefully restricts its claims to star-forming galaxies, repeats the analysis with external and sample-based SFMS definitions, and explores mass and morphology splits.

Significance. If the central claim is correct, the paper has a useful cautionary message for galaxy-evolution studies: apparent TM-metric convergence in SFMS deviations can be a nonstationarity artifact, not evidence that ensemble averages can be substituted for individual SFHs. The paper's strengths include the use of high-resolution FIRE-2 simulations, multiple SFMS definitions and SFR estimators, explicit treatment of morphology, cross-checks with archaeological SFHs, and an explicit toy-model demonstration (Section 3.3.1) that the TM metric alone cannot distinguish stationary from decreasing-variance processes. The final conclusion is appropriately hedged: the authors state that TM-metric convergence is necessary but not sufficient. However, the quantitative case for the stronger 'not truly ergodic' claim rests on a block-scrambling diagnostic whose validity for these short, nonstationary, unevenly sampled series is not demonstrated, and the current Table 2 contains an internal inconsistency with the text.

major comments (4)
  1. [Section 3.4, Table 2] The block-scrambling control is the main evidence for the claim that the observed convergence is only apparent. The paper cites Kelty-Stephen & Mangalam (2022) for the premise that block-scrambling preserves TM convergence for ergodic systems, but that premise is not established for the present regime: ~14 Gyr, N=20, heteroscedastic, nonstationary series with missing snapshots and visible edge effects (Figure 11). A concrete validation is needed, e.g. applying the same 500 Myr/1 Gyr block-scrambling to stationary ergodic surrogates and to the g(t)Y(t) amplitude-modulated process discussed in the text, with the same N and sampling pattern. Without this, the inference from 'scrambling suppresses convergence' to 'the original series is not ergodic' does not follow; a nonstationary but finite-sample ergodic process could in principle behave similarly. The internal consistency of the reported
  2. [Section 3.3.4, Eq. (7)] The fitted power-law exponents in Table 2 are presented as supporting the ergodic classification, but many have error bars so large that the classification is not meaningful. Examples include alpha(sSFR9)=5.25±17.14 for the Sample SFMS complete sample, alpha(sSFR7)=8.89±5.61 for the P23 high-mass group, and alpha(sSFR9)=1.31±5.30 for the P23 complete sample. The statement in Section 3.4 that block-scrambled TM metrics 'asymptotically approach a nonzero value' is also not supported by Eq. (7), in which alpha<1 corresponds to slower power-law decay toward zero, not a nonzero asymptote. Either fit a model with an explicit offset and report it, or revise the interpretation. The quantitative convergence claims should be revisited with bootstrap/confidence intervals and a clear statement of which alpha values are statistically distinguishable from the ergodic threshold of unity.
  3. [Section 2.5, Section 3.2, Table 2] The Sample SFMS is fitted to the same FIRE-2 snapshots whose deviations are subsequently analyzed, so the zero-mean property of the residuals is partly inherited from the fit rather than from galaxy physics. The authors mitigate this by repeating every analysis with the Speagle et al. (2014) and Popesso et al. (2023) SFMSs, and the central visual trends are indeed present for those external definitions. However, Table 2 shows that the Sample-SFMS fits produce extreme exponents (e.g. 8.36±2.53 and 13.80±8.57) that behave very differently from the external relations. The abstract's claim that apparent convergence is seen 'regardless of the SFMS definition adopted' should therefore be qualified with the caveat that the sample-constructed SFMS is not an independent test. This is not a fatal flaw, but the current wording overstates the uniformity across definitions.
  4. [Section 3.3.2, Figure 9] The number of galaxies contributing to the TM metric changes with time (rightmost column of Figure 9), especially at early epochs. Changes in Ngal and the associated changes in the ensemble mean can themselves produce artificial TM-metric evolution. The text acknowledges this qualitatively, but the main conclusion would be strengthened by a fixed-sample or completeness-corrected analysis, or by an explicit test of how much of the early-time TM behavior is driven by sample membership changes rather than by intrinsic SFMS-deviation dynamics.
minor comments (6)
  1. [Eq. (5), Eq. (6)] The notation for time averages and ensemble averages is introduced only verbally. Please define <...> and the overbar explicitly in Eq. (6), including the time dependence of Ngal.
  2. [Section 3.4, Table 2] Table 2 is titled 'Power law exponents derived from TM metric convergence fit', but Section 3.4 refers to 'convergence values'. Use the same terminology throughout to avoid confusing alpha with a terminal offset.
  3. [Section 3.1, Figure 1] The caption of Figure 1 says 'Error bars show the accepted scatter of 0.3 dex', but it is unclear whether the plotted error bars represent the observed scatter of FIRE-2 galaxies or the input scatter used in constructing the SFMS. Please clarify.
  4. [Section 2.3] The statement that archaeological SFHs are 'inherently smooth due to the high sampling rate' is confusing. A 1 Myr binning is fine, but smoothness is not guaranteed by sampling rate alone; clarify the smoothing or interpolation procedure.
  5. [References] The SciPy reference is incorrectly formatted: 'Nature Medicine, 17, 261' should be 'Nature Methods, 17, 261'.
  6. [Data Availability] The TM-metric and block-scrambling codes are said to be 'available from the author upon reasonable request'. Given the increasing reproducibility standards in astrostatistics, please deposit the analysis code in a public repository.

Circularity Check

1 steps flagged

Partial circularity from fitting the Sample SFMS to the same data whose SFMS deviations it defines; central claim is still independently supported by fixed observed SFMS fits.

specific steps
  1. fitted input called prediction [Section 2.5 (Eq. 2), Section 3.2 (Eqs. 3-4), Section 3.3 (Eq. 6)]
    "By fitting our own SFMS, we create a fit that is more consistent with the simulation sample. ... these biases disappear when we consider our sample-constructed SFMS, as one would expect, since the SFMS considered is the zero-point reference for calculating ΔsSFR."

    The Sample SFMS is a least-squares fit (Eq. 2) to the same FIRE-2 snapshots whose SFR/M* deviations (Eqs. 3-4) are then used to compute the TM metric (Eq. 6). Because the fit is the zero-point of those deviations, any systematic mean trend absorbed by the fitted parameters is removed from the residuals by construction, so part of the apparent TM convergence for the Sample-SFMS curves is inherited from the fitting step. This is not the whole story: the same qualitative convergence appears with the externally fixed Popesso et al. (2023) and Speagle et al. (2014) SFMSs, so the central claim does not reduce to the fit.

full rationale

The derivation chain is mostly self-contained. The TM metric (Eq. 5) and the α power-law classification are taken from external literature, not from the authors' prior work. The block-scrambling control is imported from Kelty-Stephen & Mangalam (2022), which is not a self-citation; whether it is valid for short, nonstationary, finite N=20 series is an external-validity/correctness concern, not circularity. The citation to Smith & Thacker (2024) is motivational and not load-bearing for the final 'decreasing variance prevents true ergodicity' claim, which rests on the paper's own toy model (Sec 3.3.1) and block-scrambling experiment. The only genuine by-construction element is the internally fitted Sample SFMS, which by definition serves as the zero-point of its own residuals; the paper explicitly notes that removing the temporal bias is expected for this definition. This partial circularity is bounded: all core conclusions are repeated with two externally fixed observed SFMSs and the abstract's 'regardless of SFMS definition' claim is thus independently supported. I also flag the internal inconsistency in Table 2 (Sample SFMS, 1 Gyr blocks, α_sSFR7 = 1.13±0.09, above the 'ergodic' threshold) as a robustness problem, but it is not a circular step. Overall: one partial non-central fit-related circularity; score 3.

Axiom & Free-Parameter Ledger

2 free parameters · 5 axioms · 0 invented entities

The central analysis rests on three kinds of imported content: (1) the TM metric from Thirumalai et al. (1989) and its block-scrambling control from Kelty-Stephen & Mangalam (2022); (2) the FIRE-2 simulation physics, which is taken as ground truth; (3) the choice to define ergodicity only through SFMS deviations. The only free parameters introduced in this paper are the five coefficients of the sample-fitted SFMS (Eq. 2) and a hand-set bursty/smooth threshold; the sample SFMS fit is partially circular because deviations are measured from it, though external SFMS definitions provide an independent check. No new physical entities are postulated.

free parameters (2)
  • Sample SFMS coefficients (a0, b0, a1, b1, b2) = not reported in text (fit to all FIRE-2 snapshots)
    Eq. (2) is fitted to the same simulation data used to define SFMS deviations; five free parameters set the reference line for the 'Sample' SFMS.
  • Bursty-to-smooth transition threshold (normalized moving standard deviation) = 0.3
    Chosen by hand to match the commonly accepted 0.3 dex scatter of the SFMS (Section 3.2.1); it determines the quoted transition escape velocity (~200.82 km/s) but is not a central ergodicity parameter.
axioms (5)
  • domain assumption TM metric convergence to zero is a necessary indicator of ergodicity (Thirumalai et al. 1989).
    Used in Section 3.3; the paper also argues it is not sufficient and adds stationarity/block-scrambling checks.
  • domain assumption For ergodic systems, block-scrambling the time series should preserve TM-metric convergence.
    Assumed in Section 3.4, citing Kelty-Stephen & Mangalam (2022); this is the load-bearing diagnostic assumption for distinguishing apparent from true ergodicity.
  • domain assumption FIRE-2's subgrid model faithfully represents the physical processes governing SFMS variability and stellar mass assembly.
    All conclusions in Sections 3–5 inherit the fidelity of the FIRE-2 model described in Section 2.1.
  • domain assumption Deviations from the SFMS are a sufficient low-dimensional projection for the ergodicity question.
    Section 1 restricts ergodicity to SFMS deviations, excluding full SFH shape, quiescent galaxies, and assembly details.
  • domain assumption The time-varying, non-random galaxy sample can be treated as an ensemble for the TM metric.
    Section 3.3.2 notes the number of galaxies changes per snapshot and the sample is availability-driven; the TM metric still requires a well-defined ensemble average.

pith-pipeline@v1.3.0-alltime-deepseek · 21899 in / 14645 out tokens · 148076 ms · 2026-08-01T16:22:38.373581+00:00 · methodology

0 comments
read the original abstract

We investigate the ergodicity of star formation in simulated galaxies from the FIRE-2 (Feedback In Realistic Environments) project. We restrict ergodicity considerations to being related to deviations from the star-forming main sequence (SFMS), and in turn whether ensemble averages across populations match time-averaged star formation histories (SFHs) based on simulated observable properties. We find that in these high-resolution simulations the deviations of individual galaxies from the SFMS tend to approach ergodic behavior over time, regardless of the SFMS definition adopted and the star formation estimator used. This trend persists when galaxy morphology, as traced by S\'ersic index, is considered despite the spheroid-dominated morphologies showing a smaller range of SFMS deviations than the disk-dominated morphologies. Unsurprisingly, we find more rapid convergence to ergodic behavior for star formation estimators based on shorter time scales ($10^{7}$ years) as opposed to longer ($10^{9}$ years). We caution that these findings should be considered in the context of the current sample and that further studies, particularly of high redshift evolution and the impact of active galactic nuclei should be investigated.

Figures

Figures reproduced from arXiv: 2607.18005 by Fraser M. Smith, Robert J. Thacker.

Figure 1
Figure 1. Figure 1: SFMS relation for all galaxies analyzed. Simulation results are plotted for all z = 4.0 to z = 0.0 values. The expected SFMSs from Popesso et al. (2023) and Speagle et al. (2014) are displayed in green and yellow, respectively. The SFMS constructed from the FIRE-2 data is shown in blue. Solid lines indicate the SFMS calculated at z = 4.0 and dashed lines show the expected SFMSs at z = 0.0. Error bars show … view at source ↗
Figure 2
Figure 2. Figure 2: Evolution of galaxies in log(SFR/M⊙ yr−1 )- log(M∗/M⊙) over time in the low-mass regime. Black lines correspond to the Popesso et al. (2023) SFMS fit. Error bars show the accepted scatter of 0.3 dex. range of positions relative to the main sequence. Par￾ticularly, disks are more common at low redshift since sufficient mass infall and time result in most halos form￾ing stable disks. 3.2. SFMS Deviations 9.0… view at source ↗
Figure 4
Figure 4. Figure 4: Evolution of galaxies in log(SFR/M⊙ yr−1 )- log(M∗/M⊙) over time in the high-mass regime. Black lines correspond to the Popesso et al. (2023) SFMS fit. Error bars show the accepted scatter of 0.3 dex. snapshots, SFR7) and over long timescales (averaged over 800 Myr, SFR9). The deviations follow the forms below: ∆sSF R7 = < sSF Rgal >10 Myr − < sSF RSFMS >10 Myr [yr−1 ], (3) ∆sSF R9 = < sSF Rgal >800 Myr − … view at source ↗
Figure 6
Figure 6. Figure 6: Comparison between SFMS deviations on long (SFR9) and short timescales (SFR7). Black lines indicate equivalence. Red boxes surround outlier points from a Milky Way-Andromeda pair. SFMS deviations are plotted logarith￾mically for comparison with the SFMS. In our higher-mass (Milky Way-like) systems, we see a profound and sudden transition from high SFR vari￾ability (i.e. bursty) to low SFR variability (i.e.… view at source ↗
Figure 8
Figure 8. Figure 8: Sample average and standard deviation of differ￾ent noise models. The average of both samples are displayed in the left panel and the standard deviation of the samples is shown in the right panel. While both models produce av￾erages around 1.0, only the normal noise model produces a mostly stationary variance. Ωe(t) = 1 Ngal N Xgal i=1 (< ∆sSF Rgal − ∆sSF RSFMS > (t) −< ∆sSF Rgal − ∆sSF RSFMS >(t))2 [yr−2 … view at source ↗
Figure 10
Figure 10. Figure 10: Diagram visualizing the pairwise block￾scrambling routine used in this work. For an odd number of blocks, an extra swap is performed to ensure all blocks occupy positions different from their original. 5 10 10 21 10 20 10 19 10 18 10 17 [y r 2 ] P23 5 10 S14 5 10 Sample Age of Universe [Gyr] [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: TM metric as a function of cosmic time after block-scrambling. Columns separate different SFMS forms considered. The red lines denote SFR deviations on long timescales (800 Myr, sSFR9) and the blue lines are for short timescales (∼20-25 Myr, sSFR7). The time series is divided into 500 Myr blocks and the maximum number of unique, non-overlapping block-scrambles is performed. Logarithmic axes for the TM met… view at source ↗

discussion (0)

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

Reference graph

Works this paper leans on

89 extracted references · 12 canonical work pages · 1 internal anchor

  1. [1]

    C., Ilbert, O., Ciesla, L., et al

    Arango-Toro, R. C., Ilbert, O., Ciesla, L., et al. 2025, A&A, 696, A159, doi: 10.1051/0004-6361/202452519

    doi:10.1051/0004-6361/202452519 2025

  2. [2]

    Lambert, J. C. 2016, ApJ, 821, 90, doi: 10.3847/0004-637X/821/2/90

    doi:10.3847/0004-637x/821/2/90 2016

  3. [3]

    2024, Nano Express, 5, 015021, doi: 10.1088/2632-959X/ad2999

    Baccetti, V., Zhu, R., Kuncic, Z., & Caravelli, F. 2024, Nano Express, 5, 015021, doi: 10.1088/2632-959X/ad2999

    doi:10.1088/2632-959x/ad2999 2024

  4. [4]

    Baggen, J. F. W., van Dokkum, P., Labb´ e, I., et al. 2023, ApJL, 955, L12, doi: 10.3847/2041-8213/acf5ef

    doi:10.3847/2041-8213/acf5ef 2023

  5. [5]

    S., et al

    Barden, M., Rix, H.-W., Somerville, R. S., et al. 2005, ApJ, 635, 959, doi: 10.1086/497679

    doi:10.1086/497679 2005

  6. [6]

    G., Cava, A., et al

    Barro, G., P´ erez-Gonz´ alez, P. G., Cava, A., et al. 2019, ApJS, 243, 22, doi: 10.3847/1538-4365/ab23f2 15

    doi:10.3847/1538-4365/ab23f2 2019

  7. [7]

    2012, MNRAS, 423, 2558, doi: 10.1111/j.1365-2966.2012.21058.x

    Bauer, A., & Springel, V. 2012, MNRAS, 423, 2558, doi: 10.1111/j.1365-2966.2012.21058.x

    arXiv 2012

  8. [8]

    2025, ApJ, 984, 117, doi: 10.3847/1538-4357/adc721

    Bosi, M., Lapi, A., Boco, L., et al. 2025, ApJ, 984, 117, doi: 10.3847/1538-4357/adc721

    doi:10.3847/1538-4357/adc721 2025

  9. [9]

    2013, ApJ, 779, 115, doi: 10.1088/0004-637X/779/2/115 Camps-Fari˜ na, A., Chamorro-Cazorla, M., & S´ anchez, S

    Bovy, J., & Rix, H.-W. 2013, ApJ, 779, 115, doi: 10.1088/0004-637X/779/2/115 Camps-Fari˜ na, A., Chamorro-Cazorla, M., & S´ anchez, S. F. 2026, A&A, 706, A56, doi: 10.1051/0004-6361/202450700

    doi:10.1088/0004-637x/779/2/115 2013

  10. [10]

    K., Kereˇ s, D., Wetzel, A., et al

    Chan, T. K., Kereˇ s, D., Wetzel, A., et al. 2018, MNRAS, 478, 906, doi: 10.1093/mnras/sty1153

    doi:10.1093/mnras/sty1153 2018

  11. [11]

    G., & Metzler, R

    Cherstvy, A. G., & Metzler, R. 2015, JChPh, 142, 144105, doi: 10.1063/1.4917077

    work page doi:10.1063/1.4917077 2015

  12. [12]

    2016, A&A, 592, A19, doi: 10.1051/0004-6361/201527772

    Citro, A., Pozzetti, L., Moresco, M., & Cimatti, A. 2016, A&A, 592, A19, doi: 10.1051/0004-6361/201527772

    doi:10.1051/0004-6361/201527772 2016

  13. [13]

    Clauwens, B., Schaye, J., Franx, M., & Bower, R. G. 2018, MNRAS, 478, 3994, doi: 10.1093/mnras/sty1229

    doi:10.1093/mnras/sty1229 2018

  14. [14]

    W., Papovich, C., Finkelstein, S

    Cole, J. W., Papovich, C., Finkelstein, S. L., et al. 2025, ApJ, 979, 193, doi: 10.3847/1538-4357/ad9a6a

    doi:10.3847/1538-4357/ad9a6a 2025

  15. [15]

    A., Schaye, J., Bower, R

    Crain, R. A., Schaye, J., Bower, R. G., et al. 2015, MNRAS, 450, 1937, doi: 10.1093/mnras/stv725

    doi:10.1093/mnras/stv725 2015

  16. [16]

    Croft, R. A. C., Di Matteo, T., Springel, V., & Hernquist, L. 2009, MNRAS, 400, 43, doi: 10.1111/j.1365-2966.2009.15446.x

    arXiv 2009

  17. [17]

    J., Abraham, R

    Damjanov, I., McCarthy, P. J., Abraham, R. G., et al. 2009, ApJ, 695, 101, doi: 10.1088/0004-637X/695/1/101

    doi:10.1088/0004-637x/695/1/101 2009

  18. [18]

    S., Asada, Y., et al

    Desprez, G., Martis, N. S., Asada, Y., et al. 2024, MNRAS, 530, 2935, doi: 10.1093/mnras/stae1084

    doi:10.1093/mnras/stae1084 2024

  19. [19]

    2022, MNRAS, 513, 256, doi: 10.1093/mnras/stac884

    Dimauro, P., Daddi, E., Shankar, F., et al. 2022, MNRAS, 513, 256, doi: 10.1093/mnras/stac884

    doi:10.1093/mnras/stac884 2022

  20. [20]

    2018, MNRAS, 473, 1930, doi: 10.1093/mnras/stx2482

    El-Badry, K., Quataert, E., Wetzel, A., et al. 2018, MNRAS, 473, 1930, doi: 10.1093/mnras/stx2482

    doi:10.1093/mnras/stx2482 2018

  21. [21]

    P., Whitler, L., et al

    Endsley, R., Stark, D. P., Whitler, L., et al. 2024, MNRAS, 533, 1111, doi: 10.1093/mnras/stae1857

    doi:10.1093/mnras/stae1857 2024

  22. [22]

    L., Bagley, M

    Finkelstein, S. L., Bagley, M. B., Ferguson, H. C., et al. 2023, ApJL, 946, L13, doi: 10.3847/2041-8213/acade4

    doi:10.3847/2041-8213/acade4 2023

  23. [23]

    S., et al

    Garrison-Kimmel, S., Wetzel, A., Bullock, J. S., et al. 2017, MNRAS, 471, 1709, doi: 10.1093/mnras/stx1710

    doi:10.1093/mnras/stx1710 2017

  24. [24]

    F., et al

    Garrison-Kimmel, S., Wetzel, A., Hopkins, P. F., et al. 2019a, MNRAS, 489, 4574, doi: 10.1093/mnras/stz2507

    doi:10.1093/mnras/stz2507

  25. [25]

    F., Wetzel, A., et al

    Garrison-Kimmel, S., Hopkins, P. F., Wetzel, A., et al. 2019b, MNRAS, 487, 1380, doi: 10.1093/mnras/stz1317

    doi:10.1093/mnras/stz1317

  26. [26]

    1993, ApJ, 419, 469, doi: 10.1086/173500

    Gavazzi, G. 1993, ApJ, 419, 469, doi: 10.1086/173500

    work page doi:10.1086/173500 1993

  27. [27]

    2018, MNRAS, 474, 3976, doi: 10.1093/mnras/stx3078

    Genel, S., Nelson, D., Pillepich, A., et al. 2018, MNRAS, 474, 3976, doi: 10.1093/mnras/stx3078

    doi:10.1093/mnras/stx3078 2018

  28. [28]

    A., & Monaghan, J

    Gingold, R. A., & Monaghan, J. J. 1977, MNRAS, 181, 375, doi: 10.1093/mnras/181.3.375

    doi:10.1093/mnras/181.3.375 1977

  29. [29]

    W., Jarrett, T

    Graham, A. W., Jarrett, T. H., & Cluver, M. E. 2024, MNRAS, 527, 10059, doi: 10.1093/mnras/stad3795

    doi:10.1093/mnras/stad3795 2024

  30. [30]

    Grand, R. J. J., Springel, V., G´ omez, F. A., et al. 2016, MNRAS, 459, 199, doi: 10.1093/mnras/stw601

    doi:10.1093/mnras/stw601 2016

  31. [31]

    2011, ApJ, 742, 76, doi: 10.1088/0004-637X/742/2/76

    Guedes, J., Callegari, S., Madau, P., & Mayer, L. 2011, ApJ, 742, 76, doi: 10.1088/0004-637X/742/2/76

    doi:10.1088/0004-637x/742/2/76 2011

  32. [32]

    B., Stern, J., Faucher-Gigu` ere, C.-A., et al

    Gurvich, A. B., Stern, J., Faucher-Gigu` ere, C.-A., et al. 2023, MNRAS, 519, 2598, doi: 10.1093/mnras/stac3712

    doi:10.1093/mnras/stac3712 2023

  33. [33]

    2023, ApJS, 265, 5, doi: 10.3847/1538-4365/acaaa9

    Harikane, Y., Ouchi, M., Oguri, M., et al. 2023, ApJS, 265, 5, doi: 10.3847/1538-4365/acaaa9

    doi:10.3847/1538-4365/acaaa9 2023

  34. [34]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2

    doi:10.1038/s41586-020-2649-2 2020

  35. [35]

    Haskell, P., Das, S., Smith, D. J. B., et al. 2024, MNRAS, 530, L7, doi: 10.1093/mnrasl/slae019

    doi:10.1093/mnrasl/slae019 2024

  36. [36]

    Hopkins, P. F. 2015, MNRAS, 450, 53, doi: 10.1093/mnras/stv195

    doi:10.1093/mnras/stv195 2015

  37. [37]

    F., Bundy, K., Croton, D., et al

    Hopkins, P. F., Bundy, K., Croton, D., et al. 2010, ApJ, 715, 202, doi: 10.1088/0004-637X/715/1/202

    doi:10.1088/0004-637x/715/1/202 2010

  38. [38]

    F., Wetzel, A., Kereˇ s, D., et al

    Hopkins, P. F., Wetzel, A., Kereˇ s, D., et al. 2018, MNRAS, 480, 800, doi: 10.1093/mnras/sty1690

    doi:10.1093/mnras/sty1690 2018

  39. [39]

    F., Gurvich, A

    Hopkins, P. F., Gurvich, A. B., Shen, X., et al. 2023, MNRAS, 525, 2241, doi: 10.1093/mnras/stad1902

    doi:10.1093/mnras/stad1902 2023

  40. [40]

    Hunter, J. D. 2007, Computing in Science and Engineering, 9, 90, doi: 10.1109/MCSE.2007.55

    doi:10.1109/mcse.2007.55 2007

  41. [41]

    G., Speagle, J

    Iyer, K. G., Speagle, J. S., Caplar, N., et al. 2024, ApJ, 961, 53, doi: 10.3847/1538-4357/acff64

    doi:10.3847/1538-4357/acff64 2024

  42. [42]

    G., Tacchella, S., Genel, S., et al

    Iyer, K. G., Tacchella, S., Genel, S., et al. 2020, MNRAS, 498, 430, doi: 10.1093/mnras/staa2150

    doi:10.1093/mnras/staa2150 2020

  43. [43]

    S., Rose, C., Vanderhoof, B

    Kartaltepe, J. S., Rose, C., Vanderhoof, B. N., et al. 2023, ApJL, 946, L15, doi: 10.3847/2041-8213/acad01

    doi:10.3847/2041-8213/acad01 2023

  44. [44]

    2014, MNRAS, 437, L41, doi: 10.1093/mnrasl/slt136

    Kaviraj, S. 2014, MNRAS, 437, L41, doi: 10.1093/mnrasl/slt136

    work page doi:10.1093/mnrasl/slt136 2014

  45. [45]

    S., Zentner, A

    Kazantzidis, S., Bullock, J. S., Zentner, A. R., Kravtsov, A. V., & Moustakas, L. A. 2008, ApJ, 688, 254, doi: 10.1086/591958

    doi:10.1086/591958 2008

  46. [46]

    G., & Mangalam, M

    Kelty-Stephen, D. G., & Mangalam, M. 2022, Chaos Solitons and Fractals, 163, 112568, doi: 10.1016/j.chaos.2022.112568 —. 2023, Physica A Statistical Mechanics and its Applications, 617, 128651, doi: 10.1016/j.physa.2023.128651 Labb´ e, I., van Dokkum, P., Nelson, E., et al. 2023, Nature, 616, 266, doi: 10.1038/s41586-023-05786-2

    arXiv 2022

  47. [47]

    2018, MNRAS, 474, 5437, doi: 10.1093/mnras/stx3055 Lanoisel´ ee, Y., & Grebenkov, D

    Laigle, C., Pichon, C., Arnouts, S., et al. 2018, MNRAS, 474, 5437, doi: 10.1093/mnras/stx3055 Lanoisel´ ee, Y., & Grebenkov, D. S. 2016, PhRvE, 93, 052146, doi: 10.1103/PhysRevE.93.052146

    doi:10.1093/mnras/stx3055 2018

  48. [48]

    H., Park, C., Hwang, H

    Lee, J. H., Park, C., Hwang, H. S., & Kwon, M. 2024, ApJ, 966, 113, doi: 10.3847/1538-4357/ad3448

    doi:10.3847/1538-4357/ad3448 2024

  49. [49]

    Speagle, J. S. 2019, ApJ, 876, 3, doi: 10.3847/1538-4357/ab133c

    doi:10.3847/1538-4357/ab133c 2019

  50. [50]

    2024, A&A, 691, A248, doi: 10.1051/0004-6361/202348341 16

    Leroy, L., Elbaz, D., Magnelli, B., et al. 2024, A&A, 691, A248, doi: 10.1051/0004-6361/202348341 16

    work page doi:10.1051/0004-6361/202348341 2024

  51. [51]

    J., Xu, R., Stansby, P

    Lind, S. J., Xu, R., Stansby, P. K., & Rogers, B. D. 2012, Journal of Computational Physics, 231, 1499, doi: 10.1016/j.jcp.2011.10.027

    work page doi:10.1016/j.jcp.2011.10.027 2012

  52. [52]

    2014, ARA&A, 52, 415, doi: 10.1146/annurev-astro-081811-125615

    Madau, P., & Dickinson, M. 2014, ARA&A, 52, 415, doi: 10.1146/annurev-astro-081811-125615

    doi:10.1146/annurev-astro-081811-125615 2014

  53. [53]

    Mangalam, M., Metzler, R., & Kelty-Stephen, D. G. 2023, Physical Review Research, 5, 023144, doi: 10.1103/PhysRevResearch.5.023144

    work page doi:10.1103/physrevresearch.5.023144 2023

  54. [54]

    2018, MNRAS, 480, 2266, doi: 10.1093/mnras/sty1936

    Pichon, C. 2018, MNRAS, 480, 2266, doi: 10.1093/mnras/sty1936

    doi:10.1093/mnras/sty1936 2018

  55. [55]

    2019, MNRAS, 484, 915, doi: 10.1093/mnras/stz030

    Matthee, J., & Schaye, J. 2019, MNRAS, 484, 915, doi: 10.1093/mnras/stz030

    doi:10.1093/mnras/stz030 2019

  56. [56]

    S., & Schombert, J

    McGaugh, S. S., & Schombert, J. M. 2014, AJ, 148, 77, doi: 10.1088/0004-6256/148/5/77

    doi:10.1088/0004-6256/148/5/77 2014

  57. [57]

    C., et al

    Mercedes-Feliz, J., Angl´ es-Alc´ azar, D., Hayward, C. C., et al. 2023, MNRAS, 524, 3446, doi: 10.1093/mnras/stad2079

    doi:10.1093/mnras/stad2079 2023

  58. [58]

    P., Naab, T., & White, S

    Moster, B. P., Naab, T., & White, S. D. M. 2020, MNRAS, 499, 4748, doi: 10.1093/mnras/staa3019

    work page doi:10.1093/mnras/staa3019 2020

  59. [59]

    J., Conselice, C

    Mundy, C. J., Conselice, C. J., Duncan, K. J., et al. 2017, MNRAS, 470, 3507, doi: 10.1093/mnras/stx1238

    doi:10.1093/mnras/stx1238 2017

  60. [60]

    G., Weiner, B

    Noeske, K. G., Weiner, B. J., Faber, S. M., et al. 2007, ApJL, 660, L43, doi: 10.1086/517926

    doi:10.1086/517926 2007

  61. [61]

    2018, MNRAS, 475, 648, doi: 10.1093/mnras/stx3112

    Pillepich, A., Nelson, D., Hernquist, L., et al. 2018, MNRAS, 475, 648, doi: 10.1093/mnras/stx3112

    doi:10.1093/mnras/stx3112 2018

  62. [62]

    2023, A&A, 672, A164, doi: 10.1051/0004-6361/202244560

    Poitevineau, R., Castignani, G., & Combes, F. 2023, A&A, 672, A164, doi: 10.1051/0004-6361/202244560

    work page doi:10.1051/0004-6361/202244560 2023

  63. [63]

    2023, MNRAS, 519, 1526, doi: 10.1093/mnras/stac3214

    Popesso, P., Concas, A., Cresci, G., et al. 2023, MNRAS, 519, 1526, doi: 10.1093/mnras/stac3214

    doi:10.1093/mnras/stac3214 2023

  64. [64]

    V., et al

    Rodriguez-Gomez, V., Pillepich, A., Sales, L. V., et al. 2016, MNRAS, 458, 2371, doi: 10.1093/mnras/stw456

    doi:10.1093/mnras/stw456 2016

  65. [65]

    G., Chechkin, A

    Safdari, H., Cherstvy, A. G., Chechkin, A. V., et al. 2015, Journal of Physics A Mathematical General, 48, 375002, doi: 10.1088/1751-8113/48/37/375002

    work page doi:10.1088/1751-8113/48/37/375002 2015

  66. [66]

    2020, MNRAS, 491, 1471, doi: 10.1093/mnras/stz3054 S´ anchez, S

    Samuel, J., Wetzel, A., Tollerud, E., et al. 2020, MNRAS, 491, 1471, doi: 10.1093/mnras/stz3054 S´ anchez, S. F., Avila-Reese, V., Rodr ´ ıguez-Puebla, A., et al. 2019, MNRAS, 482, 1557, doi: 10.1093/mnras/sty2730 Scholz-D ´ ıaz, L., Mart ´ ın-Navarro, I., & Falc´ on-Barroso, J. 2023, MNRAS, 518, 6325, doi: 10.1093/mnras/stac3422

    doi:10.1093/mnras/stz3054 2020

  67. [67]

    2015, A&A, 575, A74, doi: 10.1051/0004-6361/201425017

    Schreiber, C., Pannella, M., Elbaz, D., et al. 2015, A&A, 575, A74, doi: 10.1051/0004-6361/201425017

    doi:10.1051/0004-6361/201425017 2015

  68. [68]

    Wyse, R. F. G. 2024, ApJL, 961, L39, doi: 10.3847/2041-8213/ad1bf0

    doi:10.3847/2041-8213/ad1bf0 2024

  69. [69]

    Quenching of Galaxies at Cosmic Noon: Understanding the Effect of Environment

    Singh, A., Guaita, L., Hibon, P., et al. 2024, arXiv e-prints, arXiv:2411.12722, doi: 10.48550/arXiv.2411.12722

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2411.12722 2024

  70. [70]

    M., & Thacker, R

    Smith, F. M., & Thacker, R. J. 2024, MNRAS, 532, 4774, doi: 10.1093/mnras/stae1759

    work page doi:10.1093/mnras/stae1759 2024

  71. [71]

    2022, MNRAS, 516, 883, doi: 10.1093/mnras/stac2214

    Sorini, D., Dav´ e, R., Cui, W., & Appleby, S. 2022, MNRAS, 516, 883, doi: 10.1093/mnras/stac2214

    doi:10.1093/mnras/stac2214 2022

  72. [72]

    Silverman, J. D. 2014, ApJS, 214, 15, doi: 10.1088/0067-0049/214/2/15

    doi:10.1088/0067-0049/214/2/15 2014

  73. [73]

    2016, Scientific Reports, 6, 30948, doi: 10.1038/srep30948

    Spiechowicz, J., Luczka, J., & H¨ anggi, P. 2016, Scientific Reports, 6, 30948, doi: 10.1038/srep30948

    work page doi:10.1038/srep30948 2016

  74. [74]

    M., et al

    Stephenson, J., Rodr ´ ıguez-Puebla, A., Faber, S. M., et al. 2024, MNRAS, 532, 4217, doi: 10.1093/mnras/stae1735 S¨ uzen, M. 2014, PhRvE, 90, 032141, doi: 10.1103/PhysRevE.90.032141

    doi:10.1093/mnras/stae1735 2024

  75. [75]

    M., et al

    Tacchella, S., Dekel, A., Carollo, C. M., et al. 2016, MNRAS, 457, 2790, doi: 10.1093/mnras/stw131

    doi:10.1093/mnras/stw131 2016

  76. [76]

    M., F¨ orster Schreiber, N

    Tacchella, S., Carollo, C. M., F¨ orster Schreiber, N. M., et al. 2018, ApJ, 859, 56, doi: 10.3847/1538-4357/aabf8b

    doi:10.3847/1538-4357/aabf8b 2018

  77. [77]

    D., & Kirkpatrick, T

    Thirumalai, D., Mountain, R. D., & Kirkpatrick, T. R. 1989, Phys. Rev. A., 39, 3563, doi: 10.1103/PhysRevA.39.3563

    doi:10.1103/physreva.39.3563 1989

  78. [78]

    F., Faucher-Gigu` ere, C.-A., et al

    Torrey, P., Hopkins, P. F., Faucher-Gigu` ere, C.-A., et al. 2020, MNRAS, 497, 5292, doi: 10.1093/mnras/staa2222 van den Bosch, F. C., & Ogiya, G. 2018, MNRAS, 475, 4066, doi: 10.1093/mnras/sty084

    doi:10.1093/mnras/staa2222 2020

  79. [79]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Medicine, 17, 261, doi: 10.1038/s41592-019-0686-2

    doi:10.1038/s41592-019-0686-2 2020

  80. [80]

    2020, Nature Reviews Physics, 2, 42, doi: 10.1038/s42254-019-0127-2

    Vogelsberger, M., Marinacci, F., Torrey, P., & Puchwein, E. 2020, Nature Reviews Physics, 2, 42, doi: 10.1038/s42254-019-0127-2

    doi:10.1038/s42254-019-0127-2 2020

Showing first 80 references.