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Shedding light on the star formation rate-halo accretion rate connection and halo quenching mechanism via DECODE, the Discrete statistical sEmi-empiriCal mODEl

T0 review · 3 major / 6 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read A single monotonic star-formation–halo relation plus halo quenching explains galaxy statistics up to $z\sim2$.

desk verdict New abundance-matching-based SFR-HAR framework with a real TNG validation; the high-mass halo-quenching story holds up, but the paper's low-mass quenching claim is input-dependent and should be recast as conditional. read the letter →

arxiv 2502.06942 v2 pith:5C3VULP6 submitted 2025-02-10 astro-ph.GA

Hao Fu , Lumen Boco , Francesco Shankar , Andrea Lapi , Mohammadreza Ayromlou , Daniel Roberts , Yingjie Peng , Aldo Rodríguez-Puebla
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Feng Yuan Cressida Cleland Simona Mei Nicola Menci
This is my paper · ORCID
classification astro-ph.GA
keywords starformationrate-haloaccretionraterelationabundancematchinggalaxyquenchinghalostellarmassfunctionmass-halosemi-empiricalmodel
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 a single, monotonic relation between a galaxy's star formation rate and the rate at which its dark matter halo accretes mass, calibrated by matching observed star-forming number counts to simulated halo counts, can account for the bulk of the local star-forming galaxy population. It then asks whether the classic halo-quenching picture, where star formation shuts off once a halo crosses roughly $10^{12}\,M_\odot$, explains the quenched population. The model integrates these assigned star formation rates along dark-matter merger trees to predict stellar mass functions, quenched fractions, and the stellar mass–halo mass relation. The authors find that monotonic star formation rate–halo accretion rate matching plus halo quenching reproduces the high-mass end and the quenched statistics up to $z\sim2$, but that the low-mass end of the stellar mass function and the steep low-mass stellar mass–halo mass slope require additional quenching in the least massive haloes. The payoff is a minimal, transparent description of galaxy assembly that marks precisely where new physics must enter.

What carries the argument

The load-bearing object is the star formation rate–halo accretion rate (SFR–HAR) relation: a monotonic mapping constructed by abundance matching the observed SFR number density with the simulated halo accretion rate number density via a one-equation formalism, with Gaussian scatter in SFR at fixed HAR as the only adjustable parameter. This relation converts dark-matter accretion histories into galaxy star formation histories. The same mapping is validated for self-consistency: feeding a large cosmological hydrodynamical simulation's own SFR and HAR functions into the abundance-matching recipe recovers the simulation's mean SFR–HAR relation at all redshifts studied, and a comparison of co-evolving SFR and HAR tracks shows no appreciable time delay. Quenching enters as a truncation: when the host halo crosses the threshold mass, the assigned SFR is dropped to zero, and the built-up stellar mass thereafter grows only by mergers.

What would settle it

A decisive check would be a direct, per-galaxy measurement of star formation rate and host halo accretion rate at $z\sim1$: if the rank-ordered relation is non-monotonic, bends at high accretion rates, or shows asymmetric scatter well beyond 0.4 dex, the abundance-matching anchor collapses. A cheaper calculation is to re-run the pipeline with an alternative low-mass SFR function census of the kind used in the paper's Appendix A; the paper itself reports that this reverses the low-mass conclusion, so the choice of census decides whether low-mass quenching is actually required.

Watch

Extended reading notes

Core claim

The central discovery is that the galaxy–halo connection can be derived, not fitted: rank-order the observed star formation rate function against the halo accretion rate function at each redshift, with a single 0.4 dex scatter, and the resulting relation assigns a star formation rate to every halo in a cosmological merger tree. Stellar mass is then the time integral of that rate, truncated instantaneously when the host halo crosses a quenching threshold near $10^{12}\,M_\odot$ with 0.4 dex of dispersion. This recipe predicts the local and $z\sim1\text{--}2$ stellar mass functions, quenched fractions, and stellar mass–halo mass relations in broad agreement with data, supporting three specific claims: (1) a monotonic star formation rate–halo accretion rate relation is sufficient for the number densities of the bulk of star-forming galaxies; (2) halo quenching alone accounts for the quenched population and for the flat high-mass end of the stellar mass–halo mass relation and the steep bright end of the stellar mass function; and (3) matching the steep low-mass end of the stellar mass–halo mass relation and the flat faint end of the stellar mass function requires an extra quenching process in haloes below roughly $10^{11}\,M_\odot$.

Load-bearing premise

Everything downstream rests on treating the adopted star formation function as a complete census of star-forming galaxies and on removing all haloes above $10^{12}\,M_\odot$ from the abundance matching, so the inferred SFR-HAR relation is calibrated only below the quenching threshold and then extrapolated to every halo's earlier accretion history.

Editorial extensions

If this is right

  • If correct, the bulk of the observed galaxy population at $z\lesssim2$ can be predicted from halo accretion histories alone, without a large calibrated set of baryonic feedback parameters.
  • The high-mass end of the stellar mass function and the flat high-mass stellar mass–halo mass slope are explained by halo quenching, so the model locates the need for additional feedback mainly in low-mass haloes and at $z\gtrsim2$.
  • Because stellar masses are integrals of assigned SFRs, the model produces self-consistent predictions for the stellar mass–halo mass relation and its scatter, including decreasing scatter with halo mass, which can be tested against weak-lensing and dynamical measurements.
  • The same pipeline can be rerun with updated empirical SFR censuses from new surveys; Appendix A already shows that alternative inputs reverse the low-mass conclusion, making the low-mass quenching requirement a directly testable data-driven claim.
  • The predicted quenched fractions at $z\sim2$ fall below the latest high-redshift data, suggesting the model can be used to single out the redshift range where a second quenching channel becomes necessary.

Reading between the lines

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

  • A natural next test would replace the rank-ordered abundance matching with a direct, per-galaxy measurement of SFR and host halo accretion rate at a few redshifts, to check whether the 0.4 dex symmetric scatter and monotonicity hold outside the simulation used for calibration.
  • If low-mass quenching is confirmed, the same machinery could quantify how much of the faint-end slope is produced by supernova-driven outflows versus satellite stripping by rerunning the model with a mass-dependent quenching threshold.
  • Since a delayed-quenching variant with 1–2 Gyr exponential decline leaves the outputs essentially unchanged, the model's discriminating power appears to reside in the threshold mass and its scatter rather than the quenching timescale, an interpretation worth testing with more gradual quenching recipes.
  • Starting the assembly at higher redshift using early-Universe luminosity functions would test whether the overabundance of high-redshift quenched galaxies seen in recent surveys is a missing second channel for quenching or a problem with the assumed SFR census.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper presents DECODE, a semi-empirical model that assigns galaxy star formation rates along dark matter halo accretion histories via an abundance-matched SFR-HAR relation, integrates these SFRs to build stellar masses, and tests a halo-quenching scenario against observed SMFs, quenched fractions, and SMHM relations from z=0 to z~2. The authors validate the monotonic SFR-HAR assumption against TNG100, use the Mancuso et al. (2016) SFR function as the observational input, and conclude that halo quenching alone can reproduce the high-mass galaxy population while additional low-mass quenching is needed to match the steep low-mass end of the SMHM relation and the flat low-mass end of the SMF.

Significance. If the central claims hold, the paper provides a useful, transparent framework with minimal free parameters, and the TNG-based check gives independent support for the monotonic SFR-HAR hypothesis and for the abundance-matching procedure itself. The model's ability to predict quenched fractions and SMHM relations as outputs rather than inputs is a genuine strength, as is the direct integration of SFRs along merger trees. However, the headline low-mass-quenching conclusion is currently tied to one specific input SFR function, and the quenched-fraction comparison depends on a dispersion parameter that is tuned to the data; these caveats reduce the significance of the paper's third conclusion until the robustness is quantified.

major comments (3)
  1. [Sect. 2.2 and Appendix A] Result 3, that 'additional quenching processes in the least massive haloes are needed,' is not robust to the choice of input SFR function. The abundance matching in Eq. (1) uses Mancuso et al. (2016) as the census of star-forming galaxies, and Sect. 2.2 states that switching to alternative SFR functions yields 'qualitatively similar results.' Appendix A, however, reports that Fujimoto et al. (2023) produces a high excess of low-mass galaxies and lower quenched fractions, while Sargent et al. (2012) predicts 'opposite results.' Since the low-mass excess that motivates the ad hoc 10^11 Msun quenching cut in Sect. 4.2.1 is generated by the SFR-HAR relation derived from this input, the inference is input-dependent. I request a quantitative re-analysis with at least one alternative SFR function, or a clear and quantitative justification for preferring Mancuso et al. (2016), before Result 3 is presented as a general finding. The Conclusions bullet that 'some of the latest current data on the SMF do not require this additional ingredient' should also be reconciled with the abstract's categorical statement of Result 3.
  2. [Sect. 2.4] The quenched-fraction comparison is not fully independent because the 0.4 dex dispersion around the quenching threshold is explicitly fitted to the data ('we found a value of 0.4 dex to best suit the outputs to the data'). Since the model's claim that halo quenching is 'sufficient' to reproduce the statistics of quenched galaxies is assessed through this comparison, the fitted nature of this parameter should be stated more prominently, and the sensitivity of the high-mass conclusions to sigma_quench should be quantified rather than only described qualitatively as flattening or sharpening the quenched-fraction curves.
  3. [Sect. 2.2] The abundance matching removes all haloes above Mh,lim = 10^12 Msun from the HAR function, so the SFR-HAR relation at high HAR is not constrained by observations but is effectively imposed by the quenching hypothesis under test. This means the predicted high-mass SMF and quenched fraction partly encode the assumption being tested. I ask the authors to clarify how this truncation affects the claimed agreement at the high-mass end and to state explicitly what part of the high-mass prediction is a test of halo quenching rather than a consequence of the input truncation.
minor comments (6)
  1. [Eq. (1)] The notation used for the scatter is defined only in the text; please define mu = d log SFR/d log HAR directly after Eq. (1) and clarify how mu is evaluated when the SFR-HAR relation bends at low redshift, as in Fig. 6.
  2. [Fig. 2] The axis labels in the upper and lower panels of Fig. 2 appear as 'Mh [M yr 1]' and should be typeset as log10(Mh/[M_sun yr^-1]) and log10(SFR/[M_sun yr^-1]) for clarity.
  3. [Sect. 4.2.1] The phrase 'by including the energy release, for example from strong SN feedback' overstates what is implemented; the model simply shuts down star formation in haloes below 10^11 Msun, and this should be described as an ad hoc test rather than a feedback model.
  4. [Sect. 5] There is a typo in the sentence 'a strong and instantaneous halo quenching could be not a realist scenario'; this should read 'may not be a realistic scenario.'
  5. [Sect. 2.3.2] The footnote defining 'surviving' as 'those centrals that have become satellites' is confusing; please clarify whether these are central galaxies that have survived as distinct objects after infall or something else.
  6. [Sect. 3] The TNG test in Sect. 3 is a self-consistency check of the abundance-matching technique using simulation inputs; the text should state explicitly that this test does not independently validate the observational SFR function or the halo-quenching recipe.

Circularity Check

1 steps flagged · score 6.0 of 10

The quenched-fraction 'prediction' is partly circular because the 0.4 dex dispersion around the quenching threshold is fitted to the data whose match is later presented as an independent test; the low-mass quenching conclusion is input-sensitive rather than forced.

  1. fitted input called prediction [Sect. 2.4 (halo quenching treatment) and Sect. 4.3 (quenched fractions presented as test)]
    "Since physically not all galaxies quench when their host halo reaches exactly Mh,lim∼ 1012 M⊙, we included some dispersion around this quenching halo mass, for which we found a value of 0.4 dex to best suit the outputs to the data. By increasing or decreasing this parameter the shape of the quenched fractions as a function of stellar mass would simply flatten or sharpen."

    The paper explicitly tunes the dispersion around Mh,lim to the data and states that this parameter directly sets the shape of the quenched-fraction curve. Section 4.3 then calls 'the predicted relative fractions of quenched galaxies as a function of stellar mass or environment' an 'independent and robust test of the quenching mechanism.' The agreement of f_quench(M*) with observations is therefore partly imposed at input time rather than independently predicted. The high-mass SMF and SMHM results remain independent outputs, so the circularity is partial rather than complete.

full rationale

The core abundance-matching pipeline is not circular: the SFR-HAR relation in Eq. (1) is constructed from an observed SFR function (Mancuso et al. 2016) and a simulated HAR function, and the stellar masses are genuine time-integrals of the assigned SFRs. The SMF, SMHM high-mass end, and merger statistics are therefore real predictions with external content. The principal circular step is the quenching dispersion: the 0.4 dex spread around the quenching halo mass is fitted 'to best suit the outputs to the data,' yet the quenched-fraction curve is later showcased as an independent test. Because the paper states that this parameter controls the flattening/sharpening of f_quench(M*), the match of the quenched-fraction shape is partly by construction. The low-mass quenching conclusion (Sect. 4.2.1) is a model-selection inference rather than an equation-level circularity: adding a 10^11 Msun cut and then reporting an improved fit supports sufficiency, not necessity. It is also flagged here as a robustness limitation, since Sect. 2.2 claims alternative SFR functions give 'qualitatively similar results,' while Appendix A reports that Sargent et al. (2012) 'predicts opposite results' for the low-mass behavior. The TNG comparisons are explicitly self-consistency checks and are not load-bearing for the astronomical claims. Overall, the central halo-quenching result has independent content, but one headline prediction reduces partly to a fitted parameter, giving a partial-circularity score of 6.

Assumptions & free parameters 3 free parameters · 6 assumptions · 0 invented entities

The model is transparent in its assumptions, but the headline comparisons depend on one dispersion fitted to the data (sigma_quench = 0.4 dex), an ad hoc low-mass quenching cut at 10^11 Msun, and an input SFR function whose choice changes the low-mass conclusions in Appendix A. No new physical entities are postulated.

free parameters (3)
  • sigma_log10(SFR)|HAR = 0.4 dex
    Intrinsic scatter in SFR at fixed HAR assumed in the abundance matching (Eq. 1); value taken from the TNG simulation, not fitted to the target data, and the paper states results are insensitive in 0.3-0.5 dex.
  • sigma_quench = 0.4 dex
    Dispersion around the quenching halo mass Mh,lim; explicitly chosen 'to best suit the outputs to the data' (Sect. 2.4), so the quenched-fraction and SMF shapes are partly fitted.
  • Mh,low (low-mass quenching cut) = 10^11 Msun
    Ad hoc threshold introduced in Sect. 4.2.1 to suppress star formation in low-mass haloes ('simply shutting down the star formation in haloes with mass ≲ 10^11 Msun') to bring the faint end of the SMF and SMHM slope into agreement with observations.
assumptions (6)
  • domain assumption A monotonic, one-to-one SFR-HAR relation exists and is recoverable by the abundance matching formula of Eq. (1) with Gaussian scatter.
    The paper validates this in the TNG simulation (Sect. 3) but for real galaxies it is an assumption; the relation is extrapolated to all halo growth histories after being constrained only below Mh,lim.
  • domain assumption The Mancuso et al. (2016) SFR function is a complete census of star-forming galaxies at z<3.
    Used as the observational input for abundance matching (Sect. 2.2); Appendix A shows alternative SFR functions change the low-mass SMF and quenched fractions in opposite directions.
  • domain assumption Dark matter halo accretion histories from Tinker et al. (2008) HMF and SatGen/Parkinson et al. (2008) merger trees are accurate.
    HAR functions and merger histories are generated from these analytic tools (Sect. 2.1), not from direct N-body data.
  • domain assumption All haloes above Mh,lim = 10^12 Msun host no star-forming galaxies; such haloes are removed from the HAR function and star formation is truncated at the threshold.
    This encodes the halo-quenching hypothesis being tested (Sect. 2.2 and 2.4), so the high-mass behavior is imposed rather than predicted.
  • domain assumption Instantaneous truncation of SFR upon crossing the quenching threshold is a valid representation of quenching.
    The paper tests a time-delayed version and finds minimal effect on the SMF (Sect. 5), but the instantaneous drop is the baseline recipe.
  • ad hoc to paper Satellite galaxies can be initialized from the model's own predicted SMHM relation for centrals.
    Sect. 2.3.2 initializes infalling satellite stellar masses from the SMHM relation that DECODE itself predicts, which is internally self-consistent but not an independent constraint.

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Cite this review

Pith. "Pith review of Shedding light on the star formation rate-halo accretion rate connection and halo quenching mechanism via DECODE, the Discrete statistical sEmi-empiriCal mODEl." pith.science (2026). https://pith.science/paper/5C3VULP6

@misc{pith2026250206942,
  author       = {Pith},
  title        = {Pith review of: Shedding light on the star formation rate-halo accretion rate connection and halo quenching mechanism via DECODE, the Discrete statistical sEmi-empiriCal mODEl},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5C3VULP6}},
  note         = {Machine review of arXiv:2502.06942}
}
read the original abstract

Aims: The relative roles of the physical mechanisms involved in quenching galaxy star formation are still unclear. We tackle this fundamental problem with our cosmological semi-empirical model DECODE (Discrete statistical sEmi-empiriCal mODEl), designed to predict galaxy stellar mass assembly histories, from minimal input assumptions. Methods: Specifically, in this work the star formation history of each galaxy is calculated along its progenitor dark matter halo by assigning at each redshift a star formation rate extracted from a monotonic star formation rate-halo accretion rate (SFR-HAR) relation derived from abundance matching between the (observed) SFR function and the (numerically predicted) HAR function, a relation that is also predicted by the TNG100 simulation. SFRs are integrated across cosmic time to build up the mass of galaxies, which may halt their star formation following input physical quenching recipes. Results: In this work we test the popular halo quenching scenario and we find that: 1) the assumption of a monotonic relation between SFR and HAR allows to reproduce the number densities of the bulk of star-forming galaxies in the local Universe; 2) the halo quenching is sufficient to reproduce the statistics of the quenched galaxies and flat (steep) high-mass end of the SMHM relation (SMF); and 3) to align with the observed steep (flat) low-mass end of the SMHM (SMF) additional quenching processes in the least massive haloes are needed. Conclusions: DECODE is an invaluable tool and will pave the way to investigate the origin of newly observed high-redshift objects from the latest ongoing facilities such as JWST and Euclid.

Figures

Figures reproduced from arXiv: 2502.06942 by the authors.

Figure 1
Figure 1. Cartoon showing the methodology used to form and evolve galaxies in decode. The SFR-HAR relation at each redshift is computed via abundance matching between the observed SFR function and HAR function from simulations. SFRs are assigned to galaxies following the accretion rate history of their dark matter haloes. We drop the SFR instantaneously when the galaxy is quenched. Galaxy stellar mass growths are constructed … view at source ↗
Figure 2
Figure 2. Upper panel: Halo accretion rate function at redshifts z = 0, 1, 2 and 3, for the halo quenching scenario where haloes hosting star￾forming galaxies are removed by the mass threshold Mh ∼ 1012 M⊙. Lower panel: Star formation rate function from Mancuso et al. (2016) in the same redshift bins, as labelled. Magnelli et al. 2024). In Appendix A, we show that the Mancuso et al. (2016) fits are aligned with other determin… view at source ↗
Figure 3
Figure 3. Schematic view of the halo quenching mass threshold. Galaxy living in haloes with mass above the threshold (black solid line) sup￾press their star formation due to shock heating. The area below the threshold and below the dashed line represents the region where cold streams can exist and still allow star formation. gering star formation. On the other hand, below this threshold rapid cooling dominates over the shock … view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Distribution of the TNG central subhaloes/galaxies on the star formation rate-halo accretion rate plane (blue dots), and mean scaling relation from the TNG (blue lines) and computed using the TNG’s inputs via the abundance matching described in Sect. 2.2 (orange dashed…
Figure 6
Figure 6. Figure 6: Star formation rate-halo accretion rate relation at redshifts z = 0, 1, 2 and 3, from the abundance matching using as input the star forma￾tion rate function from Mancuso et al. (2016). 4. Results In this Section, we present decode’s predictions for the galaxy stellar …
Figure 7
Figure 7. Figure 7: Example of evolution of a galaxy of stellar mass M⋆ ∼ 1011.5 M⊙ from the catalogue, for the case of halo quenching. The upper and lower panels show the evolution in mass and growth rate, respectively. The blue solid and dotted lines show the halo mass assembly and accr…
Figure 8
Figure 8. Figure 8: Distribution of the galaxies in decode’s catalogue on the M⋆ −Mh plane at redshifts z = 0, 1 and, 2, for the halo quenching scenario. The colour bar represents the star formation rate of the galaxies. The red dotted, solid and dashed lines show the scenarios with no qu…
Figure 9
Figure 9. Figure 9: Upper panel: Same as [PITH_FULL_IMAGE:figures/full_fig_p009_9.png]
Figure 10
Figure 10. Figure 10: Fraction of quenched galaxies as a function of stellar mass at redshifts in the same redshift bins. The predictions from decode are shown for central galaxies only (blue solid lines). We compare decode’s predictions with the observed quenched fractions from the COSMOS…
Figure 11
Figure 11. Figure 11: Stellar mass function of quenched galaxies as predicted by de￾code (coloured solid lines) at redshifts z = 0, 1, and 2, compared to those inferred by COSMOS2015 (Davidzon et al. 2017, coloured trian￾gles with error bars) and COSMOS2020 (Weaver et al. 2023, coloured sq…

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Works this paper leans on

166 extracted references · 37 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in " " * FUNCTION format....

  3. [3]

    B., Christensen , C

    Akins , H. B., Christensen , C. R., Brooks , A. M., et al. 2021, , 909, 139

    2021

  4. [4]

    2019, , 488, 4916

    Allen , M., Behroozi , P., & Ma , C.-P. 2019, , 488, 4916

    2019

  5. [5]

    2012, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol

    Amiaux , J., Scaramella , R., Mellier , Y., et al. 2012, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 8442, Space Telescopes and Instrumentation 2012: Optical, Infrared, and Millimeter Wave, ed. M. C. Clampin , G. G. Fazio , H. A. MacEwen , & J. Oschmann , Jacobus M., 84420Z

    2012

  6. [6]

    2015, , 810, 74

    Aversa , R., Lapi , A., de Zotti , G., Shankar , F., & Danese , L. 2015, , 810, 74

    2015

  7. [7]

    M., Nelson , D., & White , S

    Ayromlou , M., Kauffmann , G., Yates , R. M., Nelson , D., & White , S. D. M. 2021, , 505, 492

    2021

  8. [8]

    M., Lim , S., D'Eugenio , F., et al

    Baker , W. M., Lim , S., D'Eugenio , F., et al. 2024, arXiv e-prints, arXiv:2410.14773

    arXiv 2024

Show all 166 references
  1. [9]

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

    Baldry , I. K., Glazebrook , K., Brinkmann , J., et al. 2004, , 600, 681

    2004

  2. [10]

    H., Hearin , A

    Behroozi , P., Wechsler , R. H., Hearin , A. P., & Conroy , C. 2019, , 488, 3143

    2019

  3. [11]

    S., Conroy , C., & Wechsler , R

    Behroozi , P. S., Conroy , C., & Wechsler , R. H. 2010, , 717, 379

    2010

  4. [12]

    K., et al

    Bernardi , M., Meert , A., Sheth , R. K., et al. 2017, , 467, 2217

    2017

  5. [13]

    & Dekel , A

    Birnboim , Y. & Dekel , A. 2003, , 345, 349

    2003

  6. [14]

    R., Hogg , D

    Blanton , M. R., Hogg , D. W., Bahcall , N. A., et al. 2003, , 594, 186

    2003

  7. [15]

    Bluck , A. F. L., Conselice , C. J., Ormerod , K., et al. 2024, , 961, 163

    2024

  8. [16]

    2021, , 907, 110

    Boco , L., Lapi , A., Chruslinska , M., et al. 2021, , 907, 110

    2021

  9. [17]

    2019, , 881, 157

    Boco , L., Lapi , A., Goswami , S., et al. 2019, , 881, 157

    2019

  10. [18]

    2023, arXiv e-prints, arXiv:2307.13036

    Boco , L., Lapi , A., Shankar , F., et al. 2023, arXiv e-prints, arXiv:2307.13036

    2023 arXiv

  11. [19]

    J., & Combes , F

    Bournaud , F., Jog , C. J., & Combes , F. 2007, , 476, 1179

    2007

  12. [20]

    J., Illingworth , G

    Bouwens , R. J., Illingworth , G. D., Franx , M., et al. 2009, , 705, 936

    2009

  13. [21]

    G., Benson , A

    Bower , R. G., Benson , A. J., Malbon , R., et al. 2006, , 370, 645

    2006

  14. [22]

    2008, , 383, 93

    Boylan-Kolchin , M., Ma , C.-P., & Quataert , E. 2008, , 383, 93

    2008

  15. [23]

    Bravo , M., Robotham , A. S. G., Lagos , C. d. P., et al. 2023, , 522, 4481

    2023

  16. [24]

    B., Governato , F., Ro s kar , R., et al

    Brook , C. B., Governato , F., Ro s kar , R., et al. 2011, , 415, 1051

    2011

  17. [25]

    C., McLeod , D

    Carnall , A. C., McLeod , D. J., McLure , R. J., et al. 2023, , 520, 3974

    2023

  18. [26]

    Cattaneo , A., Blaizot , J., Devriendt , J. E. G., et al. 2017, , 471, 1401

    2017

  19. [27]

    M., & Guiderdoni , B

    Cattaneo , A., Dekel , A., Faber , S. M., & Guiderdoni , B. 2008, , 389, 567

    2008

  20. [28]

    2020, , 497, 279

    Cattaneo , A., Koutsouridou , I., Tollet , E., Devriendt , J., & Dubois , Y. 2020, , 497, 279

    2020

  21. [29]

    A., Warnick , K., & Knebe , A

    Cattaneo , A., Mamon , G. A., Warnick , K., & Knebe , A. 2011, , 533, A5

    2011

  22. [30]

    2003, , 115, 763

    Chabrier , G. 2003, , 115, 763

    2003

  23. [31]

    2014, , 562, A21

    Cicone , C., Maiolino , R., Sturm , E., et al. 2014, , 562, A21

    2014

  24. [32]

    L., De Zotti , G., & Danese , L

    Cirasuolo , M., Shankar , F., Granato , G. L., De Zotti , G., & Danese , L. 2005, , 629, 816

    2005

  25. [33]

    Clauwens , B., Schaye , J., Franx , M., & Bower , R. G. 2018, , 478, 3994

    2018

  26. [34]

    G., Baugh , C

    Cole , S., Lacey , C. G., Baugh , C. M., & Frenk , C. S. 2000, , 319, 168

    2000

  27. [35]

    Cowie , L. L. & Songaila , A. 1977, , 266, 501

    1977

  28. [36]

    L., Songaila , A., Hu , E

    Cowie , L. L., Songaila , A., Hu , E. M., & Cohen , J. G. 1996, , 112, 839

    1996

  29. [37]

    J., Springel , V., White , S

    Croton , D. J., Springel , V., White , S. D. M., et al. 2006, , 365, 11

    2006

  30. [38]

    J., Stevens , A

    Croton , D. J., Stevens , A. R. H., Tonini , C., et al. 2016, , 222, 22

    2016

  31. [39]

    2012, , 539, A31

    Cucciati , O., Tresse , L., Ilbert , O., et al. 2012, , 539, A31

    2012

  32. [40]

    M., Valentino , F., et al

    Daddi , E., Rich , R. M., Valentino , F., et al. 2022, , 926, L21

    2022

  33. [41]

    2017, , 605, A70

    Davidzon , I., Ilbert , O., Laigle , C., et al. 2017, , 605, A70

    2017

  34. [42]

    A., Norris , M

    Davison , T. A., Norris , M. A., Pfeffer , J. L., Davies , J. J., & Crain , R. A. 2020, , 497, 81

    2020

  35. [43]

    & Birnboim , Y

    Dekel , A. & Birnboim , Y. 2006, , 368, 2

    2006

  36. [44]

    & Birnboim , Y

    Dekel , A. & Birnboim , Y. 2008, , 383, 119

    2008

  37. [45]

    C., Birnboim , Y., Mandelker , N., & Li , Z

    Dekel , A., Sarkar , K. C., Birnboim , Y., Mandelker , N., & Li , Z. 2023, , 523, 3201

    2023

  38. [46]

    & Silk , J

    Dekel , A. & Silk , J. 1986, , 303, 39

    1986

  39. [47]

    2019, , 489, 4135

    Dimauro , P., Huertas-Company , M., Daddi , E., et al. 2019, , 489, 4135

    2019

  40. [48]

    2024, , 527, 2139

    Dome , T., Tacchella , S., Fialkov , A., et al. 2024, , 527, 2139

    2024

  41. [49]

    T., McLeod , D

    Donnan , C. T., McLeod , D. J., Dunlop , J. S., et al. 2023, , 518, 6011

    2023

  42. [50]

    T., McLure , R

    Donnan , C. T., McLure , R. J., Dunlop , J. S., et al. 2024, , 533, 3222

    2024

  43. [51]

    D., et al

    Donnari , M., Pillepich , A., Joshi , G. D., et al. 2021 a , , 500, 4004

    2021

  44. [52]

    2021 b , , 506, 4760

    Donnari , M., Pillepich , A., Nelson , D., et al. 2021 b , , 506, 4760

    2021

  45. [53]

    2020, , 642, A83

    Dvornik , A., Hoekstra , H., Kuijken , K., et al. 2020, , 642, A83

    2020

  46. [54]

    2019, , 631, A175

    Erfanianfar , G., Finoguenov , A., Furnell , K., et al. 2019, , 631, A175

    2019

  47. [55]

    2024, arXiv e-prints, arXiv:2405.13491

    Euclid Collaboration , Mellier , Y., Abdurro'uf , et al. 2024, arXiv e-prints, arXiv:2405.13491

    2024

  48. [56]

    Fabian , A. C. 2012, , 50, 455

    2012

  49. [57]

    J., Faber , S

    Fang , J. J., Faber , S. M., Koo , D. C., & Dekel , A. 2013, , 776, 63

    2013

  50. [58]

    & Mayer , L

    Feldmann , R. & Mayer , L. 2015, , 446, 1939

    2015

  51. [59]

    2018, , 866, 136

    Foltz , R., Wilson , G., Muzzin , A., et al. 2018, , 866, 136

    2018

  52. [60]

    V., Hirschmann , M., et al

    Fontanot , F., Macci \`o , A. V., Hirschmann , M., et al. 2015, , 451, 2968

    2015

  53. [61]

    2022, , 516, 3206

    Fu , H., Shankar , F., Ayromlou , M., et al. 2022, , 516, 3206

    2022

  54. [62]

    2024, arXiv e-prints, arXiv:2406.07605

    Fu , H., Shankar , F., Ayromlou , M., et al. 2024, arXiv e-prints, arXiv:2406.07605

    2024 arXiv

  55. [63]

    2023, arXiv e-prints, arXiv:2303.01658

    Fujimoto , S., Kohno , K., Ouchi , M., et al. 2023, arXiv e-prints, arXiv:2303.01658

    2023 arXiv

  56. [64]

    P., Mather , J

    Gardner , J. P., Mather , J. C., Clampin , M., et al. 2006, , 123, 485

    2006

  57. [65]

    Gensior , J., Kruijssen , J. M. D., & Keller , B. W. 2020, , 495, 199

    2020

  58. [66]

    2000, , 141, 371

    Girardi , L., Bressan , A., Bertelli , G., & Chiosi , C. 2000, , 141, 371

    2000

  59. [67]

    2010, , 463, 203

    Governato , F., Brook , C., Mayer , L., et al. 2010, , 463, 203

    2010

  60. [68]

    L., De Zotti , G., Silva , L., Bressan , A., & Danese , L

    Granato , G. L., De Zotti , G., Silva , L., Bressan , A., & Danese , L. 2004, , 600, 580

    2004

  61. [69]

    J., Shankar , F., & Conselice , C

    Grylls , P. J., Shankar , F., & Conselice , C. J. 2020 a , , 499, 2265

    2020

  62. [70]

    J., Shankar , F., Leja , J., et al

    Grylls , P. J., Shankar , F., Leja , J., et al. 2020 b , , 491, 634

    2020

  63. [71]

    Gunn , J. E. & Gott , J. Richard, I. 1972, , 176, 1

    1972

  64. [72]

    2011, , 413, 101

    Guo , Q., White , S., Boylan-Kolchin , M., et al. 2011, , 413, 101

    2011

  65. [73]

    & White , S

    Guo , Q. & White , S. D. M. 2008, , 384, 2

    2008

  66. [74]

    P., Pereira , M

    Haines , C. P., Pereira , M. J., Smith , G. P., et al. 2015, , 806, 101

    2015

  67. [75]

    P., Pereira , M

    Haines , C. P., Pereira , M. J., Smith , G. P., et al. 2013, , 775, 126

    2013

  68. [76]

    2023, , 265, 5

    Harikane , Y., Ouchi , M., Oguri , M., et al. 2023, , 265, 5

    2023

  69. [77]

    Hatton , S., Devriendt , J. E. G., Ninin , S., et al. 2003, , 343, 75

    2003

  70. [78]

    Henriques , B. M. B., White , S. D. M., Lilly , S. J., et al. 2019, , 485, 3446

    2019

  71. [79]

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

    Hopkins , P. F., Croton , D., Bundy , K., et al. 2010 a , , 724, 915

    2010

  72. [80]

    F., Somerville , R

    Hopkins , P. F., Somerville , R. S., Cox , T. J., et al. 2009, , 397, 802

    2009

  73. [81]

    F., Younger , J

    Hopkins , P. F., Younger , J. D., Hayward , C. C., Narayanan , D., & Hernquist , L. 2010 b , , 402, 1693

    2010

  74. [82]

    2020, , 492, 3685

    Huang , S., Leauthaud , A., Hearin , A., et al. 2020, , 492, 3685

    2020

  75. [83]

    2021, , 502, 621

    Jiang , F., Dekel , A., Freundlich , J., et al. 2021, , 502, 621

    2021

  76. [84]

    D., Pillepich , A., Nelson , D., et al

    Joshi , G. D., Pillepich , A., Nelson , D., et al. 2021, , 508, 1652

    2021

  77. [85]

    2017, , 464, 4977

    Katsianis , A., Tescari , E., Blanc , G., & Sargent , M. 2017, , 464, 4977

    2017

  78. [86]

    H., & Dav \'e , R

    Kere s , D., Katz , N., Weinberg , D. H., & Dav \'e , R. 2005, , 363, 2

    2005

  79. [87]

    & Cattaneo , A

    Koutsouridou , I. & Cattaneo , A. 2022, , 516, 4194

    2022

  80. [88]

    N., Cen , R., Ostriker , J

    Lackner , C. N., Cen , R., Ostriker , J. P., & Joung , M. R. 2012, , 425, 641

    2012

  81. [89]

    Lagos , C. D. P., Bravo , M., Tobar , R., et al. 2023, arXiv e-prints, arXiv:2309.02310

    2023 arXiv

  82. [90]

    2017, , 847, 13

    Lapi , A., Mancuso , C., Bressan , A., & Danese , L. 2017, , 847, 13

    2017

  83. [91]

    2018 a , , 857, 22

    Lapi , A., Pantoni , L., Zanisi , L., et al. 2018 a , , 857, 22

    2018

  84. [92]

    2018 b , , 859, 2

    Lapi , A., Salucci , P., & Danese , L. 2018 b , , 859, 2

    2018

  85. [93]

    Larson , R. B. 1974, , 169, 229

    1974

  86. [94]

    B., Tinsley , B

    Larson , R. B., Tinsley , B. M., & Caldwell , C. N. 1980, , 237, 692

    1980

  87. [95]

    & Yi , S

    Lee , J. & Yi , S. K. 2013, , 766, 38

    2013

  88. [96]

    Leitner , S. N. & Kravtsov , A. V. 2011, , 734, 48

    2011

  89. [97]

    S., Johnson , B

    Leja , J., Speagle , J. S., Johnson , B. D., et al. 2020, , 893, 111

    2020

  90. [98]

    2016, , 832, 29

    Lian , J., Yan , R., Zhang , K., & Kong , X. 2016, , 832, 29

    2016

  91. [99]

    Liepold , E. R. & Ma , C.-P. 2024, arXiv e-prints, arXiv:2407.14595

    2024 arXiv

  92. [100]

    2014, , 782, 33

    Lin , L., Jian , H.-Y., Foucaud , S., et al. 2014, , 782, 33

    2014

  93. [101]

    2023, , 959, 5

    Lyu , C., Peng , Y., Jing , Y., et al. 2023, , 959, 5

    2023

  94. [102]

    & Dickinson , M

    Madau , P. & Dickinson , M. 2014, , 52, 415

    2014

  95. [103]

    2024, , 688, A55

    Magnelli , B., Adscheid , S., Wang , T.-M., et al. 2024, , 688, A55

    2024

  96. [104]

    2019, , 881, 74

    Man , Z.-Y., Peng , Y.-J., Shi , J.-J., et al. 2019, , 881, 74

    2019

  97. [105]

    2016, , 823, 128

    Mancuso , C., Lapi , A., Shi , J., et al. 2016, , 823, 128

    2016

  98. [106]

    2018, , 480, 5113

    Marinacci , F., Vogelsberger , M., Pakmor , R., et al. 2018, , 480, 5113

    2018

  99. [107]

    2009, , 707, 250

    Martig , M., Bournaud , F., Teyssier , R., & Dekel , A. 2009, , 707, 250

    2009

  100. [108]

    2018, , 475, 3700

    Mart \' n-Navarro , I., Vazdekis , A., Falc \'o n-Barroso , J., et al. 2018, , 475, 3700

    2018

  101. [109]

    A., et al

    Matthee , J., Schaye , J., Crain , R. A., et al. 2017, , 465, 2381

    2017

  102. [110]

    F., et al

    McCavana , T., Micic , M., Lewis , G. F., et al. 2012, , 424, 361

    2012

  103. [111]

    A., Grand , R

    Monachesi , A., G \'o mez , F. A., Grand , R. J. J., et al. 2019, , 485, 2589

    2019

  104. [112]

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

    Moster , B. P., Naab , T., & White , S. D. M. 2018, , 477, 1822

    2018

  105. [113]

    2013, , 777, 18

    Muzzin , A., Marchesini , D., Stefanon , M., et al. 2013, , 777, 18

    2013

  106. [114]

    P., Pillepich , A., Springel , V., et al

    Naiman , J. P., Pillepich , A., Springel , V., et al. 2018, , 477, 1206

    2018

  107. [115]

    2022, , 39, e002

    Nanayakkara , T., Esdaile , J., Glazebrook , K., et al. 2022, , 39, e002

    2022

  108. [116]

    2019, Computational Astrophysics and Cosmology, 6, 2

    Nelson , D., Springel , V., Pillepich , A., et al. 2019, Computational Astrophysics and Cosmology, 6, 2

    2019

  109. [117]

    P., Naab , T., Johansson , P

    Oser , L., Ostriker , J. P., Naab , T., Johansson , P. H., & Burkert , A. 2010, , 725, 2312

    2010

  110. [118]

    A., Tinker , J

    Oyarz \'u n , G. A., Tinker , J. L., Bundy , K., Xhakaj , E., & Wyithe , J. S. B. 2024, arXiv e-prints, arXiv:2409.03004

    2024 arXiv

  111. [119]

    2008, , 383, 557

    Parkinson , H., Cole , S., & Helly , J. 2008, , 383, 557

    2008

  112. [120]

    J., Kova c , K., et al

    Peng , Y.-j., Lilly , S. J., Kova c , K., et al. 2010, , 721, 193

    2010

  113. [121]

    J., Renzini , A., & Carollo , M

    Peng , Y.-j., Lilly , S. J., Renzini , A., & Carollo , M. 2012, , 757, 4

    2012

  114. [122]

    2004, , 612, 168

    Pietrinferni , A., Cassisi , S., Salaris , M., & Castelli , F. 2004, , 612, 168

    2004

  115. [123]

    2018 a , , 475, 648

    Pillepich , A., Nelson , D., Hernquist , L., et al. 2018 a , , 475, 648

    2018

  116. [124]

    2018 b , , 473, 4077

    Pillepich , A., Springel , V., Nelson , D., et al. 2018 b , , 473, 4077

    2018

  117. [125]

    2014, , 444, 237

    Pillepich , A., Vogelsberger , M., Deason , A., et al. 2014, , 444, 237

    2014

  118. [126]

    M., Bluck , A

    Piotrowska , J. M., Bluck , A. F. L., Maiolino , R., & Peng , Y. 2022, , 512, 1052

    2022

  119. [127]

    2020, , 641, A6

    Planck Collaboration , Aghanim , N., Akrami , Y., et al. 2020, , 641, A6

    2020

  120. [128]

    2017, , 465, 547

    Pontzen , A., Tremmel , M., Roth , N., et al. 2017, , 465, 547

    2017

  121. [129]

    2019, , 626, A56

    Posti , L., Fraternali , F., & Marasco , A. 2019, , 626, A56

    2019

  122. [130]

    C., Bower , R

    Qu , Y., Helly , J. C., Bower , R. G., et al. 2017, , 464, 1659

    2017

  123. [131]

    Reeves , A. M. M., Hudson , M. J., & Oman , K. A. 2023, , 522, 1779

    2023

  124. [132]

    1975, Memoires of the Societe Royale des Sciences de Liege, 8, 369

    Reimers , D. 1975, Memoires of the Societe Royale des Sciences de Liege, 8, 369

    1975

  125. [133]

    & Fusi Pecci , F

    Renzini , A. & Fusi Pecci , F. 1988, , 26, 199

    1988

  126. [134]

    2015, , 449, 49

    Rodriguez-Gomez , V., Genel , S., Vogelsberger , M., et al. 2015, , 449, 49

    2015

  127. [135]

    V., et al

    Rodriguez-Gomez , V., Pillepich , A., Sales , L. V., et al. 2016, , 458, 2371

    2016

  128. [136]

    T., B \'e thermin , M., Daddi , E., & Elbaz , D

    Sargent , M. T., B \'e thermin , M., Daddi , E., & Elbaz , D. 2012, , 747, L31

    2012

  129. [137]

    M., & Edmondson , E

    Schawinski , K., Dowlin , N., Thomas , D., Urry , C. M., & Edmondson , E. 2010, , 714, L108

    2010

  130. [138]

    2006, , 643, 14

    Shankar , F., Lapi , A., Salucci , P., De Zotti , G., & Danese , L. 2006, , 643, 14

    2006

  131. [139]

    2013, , 428, 109

    Shankar , F., Marulli , F., Bernardi , M., et al. 2013, , 428, 109

    2013

  132. [140]

    2014, , 439, 3189

    Shankar , F., Mei , S., Huertas-Company , M., et al. 2014, , 439, 3189

    2014

  133. [141]

    & Rees , M

    Silk , J. & Rees , M. J. 1998, , 331, L1

    1998

  134. [142]

    B., Jin , S., Magdis , G

    Sillassen , N. B., Jin , S., Magdis , G. E., et al. 2024, , 690, A55

    2024

  135. [143]

    S., Hopkins , P

    Somerville , R. S., Hopkins , P. F., Cox , T. J., Robertson , B. E., & Hernquist , L. 2008, , 391, 481

    2008

  136. [144]

    2010, , 401, 791

    Springel , V. 2010, , 401, 791

    2010

  137. [145]

    & Hernquist , L

    Springel , V. & Hernquist , L. 2003, , 339, 289

    2003

  138. [146]

    2018, , 475, 676

    Springel , V., Pakmor , R., Pillepich , A., et al. 2018, , 475, 676

    2018

  139. [147]

    Springel , V., White , S. D. M., Tormen , G., & Kauffmann , G. 2001, , 328, 726

    2001

  140. [148]

    F., Bryan , G

    Su , K.-Y., Hopkins , P. F., Bryan , G. L., et al. 2021, , 507, 175

    2021

  141. [149]

    M., et al

    Tacchella , S., Conroy , C., Faber , S. M., et al. 2022, , 926, 134

    2022

  142. [150]

    2010, , 404, 1775

    Thomas , D., Maraston , C., Schawinski , K., Sarzi , M., & Silk , J. 2010, , 404, 1775

    2010

  143. [151]

    V., Klypin , A., et al

    Tinker , J., Kravtsov , A. V., Klypin , A., et al. 2008, , 688, 709

    2008

  144. [152]

    Valentino , F., Brammer , G., Gould , K. M. L., et al. 2023, , 947, 20

    2023

  145. [153]

    G., et al

    van der Wel , A., Franx , M., van Dokkum , P. G., et al. 2014, , 788, 28

    2014

  146. [154]

    2016, , 459, 3251

    van Uitert , E., Cacciato , M., Hoekstra , H., et al. 2016, , 459, 3251

    2016

  147. [155]

    2024, arXiv e-prints, arXiv:2408.00824

    Vani , A., Ayromlou , M., Kauffmann , G., & Springel , V. 2024, arXiv e-prints, arXiv:2408.00824

    2024 arXiv

  148. [156]

    R., Davidzon , I., Toft , S., et al

    Weaver , J. R., Davidzon , I., Toft , S., et al. 2023, , 677, A184

    2023

  149. [157]

    J., et al

    Weibel , A., de Graaff , A., Setton , D. J., et al. 2024, arXiv e-prints, arXiv:2409.03829

    2024 arXiv

  150. [158]

    2017, , 465, 3291

    Weinberger , R., Springel , V., Hernquist , L., et al. 2017, , 465, 3291

    2017

  151. [159]

    F., et al

    Wellons , S., Faucher-Gigu \`e re , C.-A., Hopkins , P. F., et al. 2023, , 520, 5394

    2023

  152. [160]

    R., Tinker , J

    Wetzel , A. R., Tinker , J. L., & Conroy , C. 2012, , 424, 232

    2012

  153. [161]

    R., Tinker , J

    Wetzel , A. R., Tinker , J. L., Conroy , C., & van den Bosch , F. C. 2013, , 432, 336

    2013

  154. [162]

    M., van der Wel , A., et al

    Wuyts , S., F \"o rster Schreiber , N. M., van der Wel , A., et al. 2011, , 742, 96

    2011

  155. [163]

    2020, , 492, 1671

    Zanisi , L., Shankar , F., Lapi , A., et al. 2020, , 492, 1671

    2020

  156. [164]

    @esa (Ref

    \@ifclassloaded agu2001 natbib The agu2001 class already includes natbib coding, so you should not add it explicitly Type <Return> for now, but then later remove the command natbib from the document \@ifclassloaded aguplus natbib The aguplus class already includes natbib codin...

  157. [165]

    @stdbsttrue NAT@ctr \@lbibitem[ NAT@ctr ] \@lbibitem[#1]#2 \@extra@b@citeb \@ifundefined br@#2\@extra@b@citeb \@namedef br@#2 \@nameuse br@#2\@extra@b@citeb \@ifundefined b@#2\@extra@b@citeb @num @parse #2 [ @natanchorstart #2\@extra@b@citeb \@biblabel @num @natanchorend] @ifc...

  158. [166]

    @open @close @open @close and [1] URL: #1 \@ifundefined chapter * \@mkboth \@ifundefined NAT@sectionbib * \@mkboth * \@mkboth\@gobbletwo \@ifclassloaded amsart * \@ifclassloaded amsbook * \@ifundefined bib@heading @heading NAT@ctr thebibliography [1] @ \@biblabel NAT@ctr \@bib...

Pith tools

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