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On the signature of black holes on the quenched stellar mass function

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Pith's one-line read This paper argues that the low-mass slope of the quenched central stellar mass function is controlled by the scatter in black hole mass at fixed stellar mass, and that matching the observed slope requires a scatter of at least 0.5 dex.

desk verdict A clean, well-tested trend linking black hole mass scatter to the quenched central galaxy mass function slope, with a plausible but not fully secure lower bound of sigma_BH >= 0.5 dex. read the letter →

arxiv 2412.04553 v1 pith:B7YKX5WA submitted 2024-12-05 astro-ph.GA

Antonio J. Porras-Valverde , John C. Forbes This is my paper · ORCID
classification astro-ph.GA
keywords blackholemassscatterquenchedcentralstellarfunctionAGNfeedbackquenchingsemi-analyticmodelscosmologicalsimulationsgalaxyformationhole–galaxyco-evolution
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper argues that the low-mass slope of the quenched central stellar mass function (QCSMF) encodes the scatter in supermassive black hole masses at a fixed stellar mass. By modifying the semi-analytic model Dark Sage and comparing other semi-analytic models and cosmological hydrodynamic simulations, the authors show that larger scatter in black hole mass spreads quenching over a wider range of stellar masses and flattens the low-mass slope. Reproducing the observed slope from Peng et al. (2012) requires $\sigma(\log_{10} M_{\mathrm{BH}} | M_*) \gtrsim 0.5$ dex. If true, the QCSMF low-mass slope becomes an indirect diagnostic of black hole–galaxy co-evolution and of AGN feedback physics.

What carries the argument

The central object is the quenched central stellar mass function (QCSMF) and its low-mass powerlaw slope $\alpha \equiv d\log_{10}\Phi/d\log_{10} M_*$ measured just below the QCSMF peak near $10^{11}\,M_\odot$. The relationship between $\alpha$ and the black hole mass scatter $\sigma_{\mathrm{BH}}$ is quantified through a toy model in which quenching occurs when a black hole exceeds a critical mass $M_{\mathrm{BH,crit}}$; expanding the quenched fraction $f_Q$ gives $d\log f_Q/d\log M_*$ decreasing with increasing $\sigma_{\mathrm{BH}}$, and requiring this slope to approach the Peng et al. (2010) limit $1+\beta$ at a particular stellar mass yields a quadratic equation for $\sigma_{\mathrm{BH}}$, giving roughly $0.78$ dex for fiducial parameters.

What would settle it

Measure the intrinsic scatter of the $M_{\mathrm{BH}}\text{--}M_*$ relation from a large sample with accurate black hole masses and a well-characterized selection function; if the true scatter comes out below about $0.4$ dex, the paper’s required floor of $0.5$ dex would be contradicted unless AGN feedback is considerably more gradual than the implemented models. Alternatively, construct a model with $\sigma_{\mathrm{BH}} < 0.3$ dex and a gradual feedback ramp that still reproduces the P12 slope, which would weaken the claimed causal link to scatter.

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Extended reading notes

Core claim

The central claim is that the scatter in black hole mass at a fixed stellar mass, $\sigma_{\mathrm{BH}}$, is the dominant factor setting the low-mass slope $\alpha = d\log_{10}\Phi/d\log_{10} M_*$ of the quenched central stellar mass function. Across Dark Sage variants and in TNG100, EAGLE, SAGE, SHARK, and UniverseMachine, higher $\sigma_{\mathrm{BH}}$ yields shallower slopes, because quenching then occurs over a broader range of stellar masses. A comparison with the observed QCSMF slope implies $\sigma(\log_{10} M_{\mathrm{BH}} | M_*) \gtrsim 0.5$ dex, consistent with direct measurements of the $M_{\mathrm{BH}}\text{--}M_*$ scatter that include all galaxy types. The paper further shows that a sudden black-hole-threshold quenching law with large scatter can mimic the single Schechter function of quenched centrals that Peng et al. (2010) attributed to star-formation-rate-proportional quenching, suggesting the success of that ansatz may be coincidental.

Load-bearing premise

The inference that $\sigma_{\mathrm{BH}} \gtrsim 0.5$ dex depends on comparing a hand-chosen local powerlaw slope fitted just below the QCSMF peak in each model to the single Schechter slope that Peng et al. (2012) fitted over a broader mass range, and the paper itself notes that few model QCSMFs are single Schechter functions and that the P12 data show an upturn below the completeness limit.

Editorial extensions

If this is right

  • Higher black hole mass scatter produces shallower QCSMF low-mass slopes, and the observed shallow slope implies $\sigma_{\mathrm{BH}} \gtrsim 0.5$ dex.
  • Direct scatter measurements that include all morphological types and active galaxies (around 0.5–0.8 dex) are consistent with this bound, while the tighter scatter seen for classical bulges alone would require more gradual AGN feedback.
  • The Peng et al. (2010) quenching law with $\eta \propto$ SFR may be a coincidence: since more massive galaxies have higher star formation rates and more likely host massive black holes, a black-hole-threshold law with large scatter reproduces the same QCSMF shape.
  • Successful galaxy formation models must produce black hole growth decoupled from stellar mass growth; adjusting the AGN feedback recipe alone is unlikely to yield the required scatter.

Reading between the lines

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

  • If the relation holds at higher redshift, the low-mass QCSMF slope should evolve as the scatter in the $M_{\mathrm{BH}}\text{--}M_*$ relation evolves, offering a cross-check that existing surveys could attempt.
  • A direct, selection-function-corrected measurement of the intrinsic $M_{\mathrm{BH}}\text{--}M_*$ scatter in a mass-complete local sample to better than $0.1$ dex precision would either confirm or contradict the inferred floor of $0.5$ dex.
  • The paper’s toy model suggests a degeneracy between $\sigma_{\mathrm{BH}}$ and the gradualness of feedback; a testable extension is to fit both parameters jointly using the full QCSMF shape rather than a single slope.
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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

4 major / 6 minor

Summary. The paper proposes that the low-mass slope of the quenched central stellar mass function (QCSMF) is a sensitive diagnostic of the scatter in black hole mass at fixed stellar mass, σ(log10 MBH | M*). Using the Dark Sage semi-analytic model with controlled modifications to the black hole population and to AGN feedback, the authors find that increasing the imposed BH mass scatter systematically flattens the QCSMF low-mass slope. This trend is reported across several independent models (TNG, EAGLE, SAGE, SHARK, UniverseMachine). Comparing the model slopes with the Peng et al. (2012) single-Schechter fit to observed quenched centrals, the paper infers σBH ≳ 0.5 dex and argues this is consistent with direct observational estimates of BH–galaxy scaling relation scatter. A toy model in which quenching occurs above a critical BH mass is used to interpret the relation between slope and scatter.

Significance. If the central claim is correct, the QCSMF slope provides a novel, observationally cheap probe of BH–galaxy co-evolution and AGN feedback, complementary to direct measurements of the MBH–M* relation. The paper's clear strength is the controlled Dark Sage experiment: within a fixed feedback prescription, varying only the imposed BH scatter yields a monotonic trend, and the qualitative agreement across independent simulation and SAM families is encouraging. The authors are also candid about limitations, noting in footnote 3 that the observed P12 data deviate from a single Schechter function below the completeness limit. However, the quantitative lower bound σBH ≳ 0.5 dex is not yet securely established: it rests on comparing hand-fitted local model slopes with a global observed Schechter slope, and the toy-model inversion used to interpret the comparison fits rather than independently predicts σBH. These issues are load-bearing for the abstract's main quantitative claim.

major comments (4)
  1. [Section 5 and Figure 4] The model slopes α are obtained from hand-chosen fitting ranges with no reported uncertainties, and the observational anchor is P12's single-Schechter slope for the entire quenched-central population. Footnote 3 concedes that the P12 data rise above their best-fit Schechter function below the completeness limit, so the observed QCSMF may itself be non-Schechter; in that case a global Schechter slope is not necessarily equal to the local low-mass slope measured in the models. Because the σBH ≳ 0.5 dex lower bound is read off the intersection of the model trend with the P12 line in Figure 4, the authors should provide uncertainties on α, test the sensitivity of α to the chosen fitting range, and ideally measure a local slope from the P12 data over the same mass range used for the models.
  2. [Section 6, Eq. (6)] The value σBH ≈ 0.78 dex is obtained by solving Eq. (6), which is derived by requiring the toy-model slope to equal the P10 slope 1+β at a chosen M*. It is therefore a fitted value, not an independent prediction, and the abstract's σBH ≳ 0.5 dex should be attributed to the simulation trend under an assumed observed slope rather than to the toy model. In addition, Eq. (5) linearizes the normal CDF about z=0, but for the fiducial parameters (γ=1, A=MBH,crit=10^8 Msun, M*=10^10 Msun, σBH≈0.78) one has z≈−1.25, well outside the regime where the expansion is accurate; using the full CDF derivative would change the inferred σBH.
  3. [Sections 2 and 6] The fixed conditional distribution models impose a lognormal scatter at fixed halo mass, whereas the abstract and Section 6 state the constraint as σ(log10 MBH | M*), scatter at fixed stellar mass. Halo mass at fixed stellar mass is itself scattered, so the two quantities are not identical. Figure 4's horizontal error bars, described as 'the span of scatters within the stellar mass range used to estimate α,' do not establish that the model quantity is the same as the observational quantity. The authors should either compute and report the effective scatter at fixed stellar mass in the models, or reframe the observational comparison to match the quantity actually varied in the simulations.
  4. [Section 7 and Figure 4] The toy-model curves in Figure 4 are labeled as predictions for α, but Eq. (5) actually gives d log fQ/d log M*, and the paper's own relation d log fQ/d log M* = α − αblue implies that the plotted quantity differs from α by the slope of the star-forming SMF. Since αblue is not specified for the toy model or for each simulation, the direct overlay of Eq. (5) on the model α values in Figure 4 is not well-defined. The authors should plot the same quantity on both axes, or explain how the offset is accounted for.
minor comments (6)
  1. [Figure 1 caption] The caption sentence listing observational data is garbled, with '(Wright et al. 2017) (grey diamonds dashed line)' appearing after the list and the P12 label in the right-hand panel not being explained.
  2. [Section 5] The chosen fitting ranges for α should be reported explicitly, for example in a table, since they are currently visible only as short overplotted lines in Figures 3 and 6.
  3. [Equation (5)] The definition y=11γ−log A+log MBH,crit implicitly assumes that masses are in solar masses; this should be stated, and log10 notation should be used consistently.
  4. [Section 7] The text says the P10 argument 'only applies near M*' and then immediately says that P10 point out the formula 'if extended to lower masses, predicts Equation 2'; this is confusing and should be rephrased.
  5. [Footnote 1] The statement that no artificial neural networks were used is irrelevant to the scientific content and could be removed.
  6. [Author affiliation and text] Several LaTeX artifacts remain, such as 'Porras-V alverde' and 'M¯ u' in the affiliation block; these should be corrected in the production version.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the sigma_BH >= 0.5 dex inference rests on a forward-modeled alpha-sigma_BH trend, not on a toy-model inversion.

full rationale

The central derivation is self-contained. The alpha versus sigma_BH trend is measured from controlled Dark Sage variants with imposed scatter and reproduced by external TNG100, EAGLE, SHARK, SAGE, and UniverseMachine; the simulated alpha values are not analytically forced to equal the P12 target. The sigma_BH >= 0.5 dex bound is read off where the model trend crosses P12's observed alpha, which is an inverse use of a forward-modeled relation, not a tautology. The toy model of Section 6 inverts its own forward relation to show that sigma_BH ~ 0.78 reproduces the P10 slope, but the paper explicitly presents this as a consistency check and does not use it as the primary evidence for the lower bound. The hand-chosen local powerlaw fits and footnote 3's admission that the P12 data deviate from a single Schechter function are comparability limitations, not circular steps. The one self-citation (Porras-Valverde et al. 2024) motivates model modifications and interpretation, but it is not load-bearing because the central trend is corroborated by independent external simulations and by controlled variation of an input parameter.

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

The paper introduces no new physical entities; all new content is a diagnostic relation between known quantities. The free parameters listed above are the model choices on which the central claim depends.

free parameters (4)
  • sigma_BH (imposed lognormal scatter in BH mass at fixed halo mass) = 0 to 1 dex in experiments; inferred >= 0.5 dex
    Central control parameter in the fixed conditional distribution Dark Sage models; the paper's main inference is a constraint on this quantity.
  • M_BH,crit (critical BH mass for quenching) = 10^8 Msun
    Threshold in the 'BHs turn off cooling' and 'BHs remove ISM' feedback models and in the toy model (Eq. 3); enters the inferred sigma_BH through y in Eq. 6.
  • A (normalization of the toy model median M_BH-M* relation) = 10^8 Msun
    Set to 10^8 Msun in Eq. 6, so log A fixes y together with M_BH,crit; the toy model sigma_BH result depends on this choice.
  • gamma (powerlaw index of the toy model median M_BH-M* relation) = 1
    Assumed approximately 1 and used as gamma=1 in Eq. 6; affects the quadratic solution and hence the predicted sigma_BH.
assumptions (7)
  • domain assumption Mass quenching is independent of environment and environment quenching applies only to satellites (P10/P12 framework)
    The identification of the QCSMF as mass-quenched centrals rests on this framework from Peng et al. 2010, 2012.
  • domain assumption Quenched galaxies are defined by log10(sSFR/yr^-1) < -11
    Standard threshold used to split star-forming and quenched populations; at low masses it can misclassify outliers below the main sequence, which the authors note may affect the QCSMF upturn.
  • ad hoc to paper The QCSMF below its peak can be represented by a single powerlaw over a hand-chosen mass range
    Section 5 states the fit range is chosen by hand for each model; this subjective choice determines every alpha value and is not reproducible from the text alone.
  • domain assumption Scatter imposed at fixed halo mass translates to the scatter at fixed stellar mass in the alpha-sigma_BH plane
    The fixed conditional distribution models impose sigma_BH at fixed halo mass, while the paper's constraint concerns sigma(log10 M_BH | M*); horizontal error bars in Fig 4 span this difference.
  • ad hoc to paper The toy model median BH mass relation M_BH,med = A (M*/10^11 Msun)^gamma with A=10^8 Msun and gamma=1 holds at low stellar masses
    Used to derive Eqs. 5 and 6; the inferred sigma_BH approximately 0.78 dex is sensitive to this assumed relation.
  • domain assumption In simulations, black hole activity is the only viable mechanism to quench central galaxies
    Section 7 states this explicitly; it is needed to interpret the alpha-sigma_BH trend as causal rather than an artifact of other quenching channels.
  • standard math Standard mathematics: Schechter function, Gaussian CDF, Taylor expansion
    Used in Eqs. 1, 3, and 5 without proof.

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Pith. "Pith review of On the signature of black holes on the quenched stellar mass function." pith.science (2026). https://pith.science/paper/B7YKX5WA

@misc{pith2026241204553,
  author       = {Pith},
  title        = {Pith review of: On the signature of black holes on the quenched stellar mass function},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/B7YKX5WA}},
  note         = {Machine review of arXiv:2412.04553}
}
abstract

As star-forming galaxies approach or exceed a stellar mass around $10^{11} M_\odot$, they are increasingly likely to be quenched in a process generically called mass quenching. Central galaxies, which are quenched via mass rather than environmental quenching, therefore accumulate in a peak around this characteristic mass. While a number of processes may influence the shape of the quenched central stellar mass function (QCSMF), we find that its low-mass slope is strongly affected by the scatter in the mass of black holes at a given stellar mass, with higher scatters in the black hole population yielding shallower slopes. Higher scatters in the black hole mass spread out the stellar mass range over which quenching occurs, leading to shallower slopes. This trend holds across a variety of semi-analytic models and cosmological hydrodynamic simulations. A comparison with observations provides indirect evidence for a large scatter in black hole mass $\sigma(\log_{10}(M_\mathrm{BH})|M_*) \gtrsim 0.5$ dex, and a joint constraint on AGN feedback physics and the co-evolution of galaxies and black holes.

Figures

Figures reproduced from arXiv: 2412.04553 by the authors.

Figure 1
Figure 1. shows the SMF for all and central galax￾ies at z = 0. Overall, Dark Sage, TNG100, and SHARK models align with observations from Baldry et al. (2008), Bernardi et al. (2013), and (Wright et al. 2017) within 0.6 dex. However, discrepancies increase 1 No artificial neural networks were used in writing this work. 9.5 10.0 10.5 11.0 11.5 log10( M? [M ] ) 10−6 10−5 10−4 10−3 10−2 10−1 Φ [dex − 1 Mpc − 3 ] All DS fiducial … view at source ↗
Figure 2
Figure 2. SMF of central star-forming (−11 < log10(sSFR [yr−1 ]), solid blue line) and quenched galaxies (−11 > log10(sSFR [yr−1 ]), solid red line) at z = 0 for Dark Sage (DS) fiducial (top left), TNG100 (bottom left), fixed condi￾tional distribution models (top three panels), and SMBH seeding models (bottom three panels) using the instantaneous AGN feedback (first column), black holes turn off cooling (second column), and b… view at source ↗
Figure 3
Figure 3. Similar to figure 2, but here we only show quenched galaxies for TNG100, Dark Sage fiducial and its modified models. The black dashed line shows the low-end fit to obtain the slope α. We highlight differences in α with varying BH growth and quenching models. (2021) find a smaller scatter of 0.28–0.39 dex when fo￾cusing only on classical bulges and pseudobulges. How￾ever, when all morphological types and both active … view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Faint-end slope α of the SMF of central quenched galaxies as a function of the black hole mass scatter at fixed stellar mass. Horizontal error bars show the span of scatters within the stellar mass range used to estimate α. We show the following models: Dark Sage fiduc…
Figure 5
Figure 5. Figure 5: SMF of central star-forming (−11 < log10(sSFR [yr−1 ]), solid blue line) and quenched galaxies (−11 > log10(sSFR [yr−1 ]), solid red line) at z = 0 for both Dark Sage modifications: the SMBH seeding models (top three pan￾els) and the fixed conditional distribution mode…
Figure 6
Figure 6. Figure 6: Similar to figure 5, but here we only show quenched galaxies for Dark Sage modified models. The black dashed line shows the low-end fit to obtain the slope α. We highlight differences in α with varying BH growth and quenching models [PITH_FULL_IMAGE:figures/full_fig_p…

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Works this paper leans on

47 extracted references · 7 canonical work pages · cited by 1 Pith paper

  1. [1]

    2013, MNRAS, 436, 3286, doi: 10.1093/mnras/stt1812

    Danforth, C. 2013, MNRAS, 436, 3286, doi: 10.1093/mnras/stt1812

    doi:10.1093/mnras/stt1812 2013

  2. [2]

    2018, ApJ, 857, 60, doi: 10.3847/1538-4357/aab494

    Arav, N., Liu, G., Xu, X., et al. 2018, ApJ, 857, 60, doi: 10.3847/1538-4357/aab494

    doi:10.3847/1538-4357/aab494 2018

  3. [4]

    K., Driver, S

    Baldry, I. K., Driver, S. P., Loveday, J., et al. 2012, MNRAS, 421, 621, doi: 10.1111/j.1365-2966.2012.20340.x

    work page arXiv 2012

  4. [5]

    H., Hearin, A

    Behroozi, P., Wechsler, R. H., Hearin, A. P., & Conroy, C. 2019, Monthly Notices of the Royal Astronomical Society, 488, 3143, doi: 10.1093/mnras/stz1182

    doi:10.1093/mnras/stz1182 2019

  5. [6]

    N., Treu, T., Ding, X., et al

    Bennert, V. N., Treu, T., Ding, X., et al. 2021, ApJ, 921, 36, doi: 10.3847/1538-4357/ac151a

    doi:10.3847/1538-4357/ac151a 2021

  6. [7]

    C., & Manne-Nicholas, E

    Bentz, M. C., & Manne-Nicholas, E. 2018, ApJ, 864, 146, doi: 10.3847/1538-4357/aad808

    doi:10.3847/1538-4357/aad808 2018

  7. [8]

    K., et al

    Bernardi, M., Meert, A., Sheth, R. K., et al. 2013, Monthly Notices of the Royal Astronomical Society, 436, 697, doi: 10.1093/mnras/stt1607

    doi:10.1093/mnras/stt1607 2013

  8. [9]

    G., Benson, A

    Bower, R. G., Benson, A. J., Malbon, R., et al. 2006, MNRAS, 370, 645, doi: 10.1111/j.1365-2966.2006.10519.x

    work page arXiv 2006

Show all 47 references
  1. [10]

    2012, A&A, 545, L3, doi: 10.1051/0004-6361/201219538

    Chon, G., B¨ ohringer, H., Krause, M., & Tr¨ umper, J. 2012, A&A, 545, L3, doi: 10.1051/0004-6361/201219538

    2012 doi

  2. [11]

    J., Springel, V., White, S

    Croton, D. J., Springel, V., White, S. D., et al. 2006, Monthly Notices of the Royal Astronomical Society, 365, 11, doi: 10.1111/j.1365-2966.2005.09675.x

    2006

  3. [12]

    J., Stevens, A

    Croton, D. J., Stevens, A. R. H., Tonini, C., et al. 2016, The Astrophysical Journal Supplement Series, 222, 22, doi: 10.3847/0067-0049/222/2/22

    2016 doi

  4. [13]

    2020, The Astrophysical Journal, 888, 37, doi: 10.3847/1538-4357/ab5b90

    Ding, X., Silverman, J., Treu, T., et al. 2020, The Astrophysical Journal, 888, 37, doi: 10.3847/1538-4357/ab5b90

    2020 doi

  5. [14]

    J., Faber, S

    Fang, J. J., Faber, S. M., Koo, D. C., & Dekel, A. 2013, ApJ, 776, 63, doi: 10.1088/0004-637X/776/1/63

    2013 doi

  6. [15]

    2000, ApJL, 539, L9, doi: 10.1086/312838

    Ferrarese, L., & Merritt, D. 2000, ApJL, 539, L9, doi: 10.1086/312838

    2000 doi

  7. [16]

    E., Strader, J., & Ho, L

    Greene, J. E., Strader, J., & Ho, L. C. 2020, ARA&A, 58, 257, doi: 10.1146/annurev-astro-032620-021835

    2020 doi

  8. [17]

    2015, ApJ, 803, 48, doi: 10.1088/0004-637X/803/1/48

    Guo, F. 2015, ApJ, 803, 48, doi: 10.1088/0004-637X/803/1/48

    2015 doi

  9. [18]

    Hunter, J. D. 2007, Computing In Science and Engineering, 9

    2007

  10. [19]

    Kormendy, J., & Ho, L. C. 2013, ARA&A, 51, 511, doi: 10.1146/annurev-astro-082708-101811

    2013 doi

  11. [20]

    Lagos, C. d. P., Bravo, M., Tobar, R., et al. 2024, MNRAS, 531, 3551, doi: 10.1093/mnras/stae1024

    2024 doi

  12. [21]

    R., Tremaine, S., Richstone, D., & Faber, S

    Lauer, T. R., Tremaine, S., Richstone, D., & Faber, S. M. 2007, ApJ, 670, 249, doi: 10.1086/522083

    2007 doi

  13. [22]

    Li, J. I. H., Shen, Y., Ho, L. C., et al. 2023, ApJ, 954, 173, doi: 10.3847/1538-4357/acddda

    2023 doi

  14. [23]

    1998, AJ, 115, 2285, doi: 10.1086/300353

    Magorrian, J., Tremaine, S., Richstone, D., et al. 1998, AJ, 115, 2285, doi: 10.1086/300353

    1998 doi

  15. [24]

    2018, MNRAS, 480, 5113, doi: 10.1093/mnras/sty2206

    Marinacci, F., Vogelsberger, M., Pakmor, R., et al. 2018, MNRAS, 480, 5113, doi: 10.1093/mnras/sty2206

    2018 doi

  16. [25]

    C., Schaller, M., et al

    McAlpine, S., Helly, J. C., Schaller, M., et al. 2016, Astronomy and Computing, 15, 72, doi: 10.1016/j.ascom.2016.02.004

    2016 doi

  17. [26]

    R., & Nulsen, P

    McNamara, B. R., & Nulsen, P. E. J. 2007, ARA&A, 45, 117, doi: 10.1146/annurev.astro.45.051806.110625

    2007 arXiv

  18. [27]

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

    Naiman, J. P., Pillepich, A., Springel, V., et al. 2018, MNRAS, 477, 1206, doi: 10.1093/mnras/sty618

    2018 doi

  19. [28]

    2018, MNRAS, 475, 624, doi: 10.1093/mnras/stx3040 —

    Nelson, D., Pillepich, A., Springel, V., et al. 2018, MNRAS, 475, 624, doi: 10.1093/mnras/stx3040 —. 2019, MNRAS, 490, 3234, doi: 10.1093/mnras/stz2306

    2018 doi

  20. [29]

    G., Mobasher, B., et al

    Pacifici, C., Iyer, K. G., Mobasher, B., et al. 2023, ApJ, 944, 141, doi: 10.3847/1538-4357/acacff

    2023 doi

  21. [30]

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

    Peng, Y.-j., Lilly, S. J., Renzini, A., & Carollo, M. 2012, ApJ, 757, 4, doi: 10.1088/0004-637X/757/1/4

    2012 doi

  22. [31]

    J., Kovaˇ c, K., et al

    Peng, Y.-j., Lilly, S. J., Kovaˇ c, K., et al. 2010, ApJ, 721, 193, doi: 10.1088/0004-637X/721/1/193 P´ erez, F., & Granger, B. E. 2007, IEEE Journals & Magazines, 9, 21, doi: 10.1109/MCSE.2007.53

    2010 doi

  23. [32]

    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

    2018 doi

  24. [33]

    J., Forbes, J

    Porras-Valverde, A. J., Forbes, J. C., Somerville, R. S., et al. 2024, ApJ, 976, 148, doi: 10.3847/1538-4357/ad7b0f

    2024 doi

  25. [34]

    E., & Volonteri, M

    Reines, A. E., & Volonteri, M. 2015, ApJ, 813, 82, doi: 10.1088/0004-637X/813/2/82

    2015 doi

  26. [35]

    P., Tollerud, E

    Robitaille, T. P., Tollerud, E. J., Greenfield, P., et al. 2013, Astronomy and Astrophysics, 558, 33, doi: 10.1051/0004-6361/201322068

    2013 doi

  27. [36]

    1976, ApJ, 203, 297, doi: 10.1086/154079

    Schechter, P. 1976, ApJ, 203, 297, doi: 10.1086/154079

    1976 doi

  28. [37]

    J., Horne, K., et al

    Shen, Y., Grier, C. J., Horne, K., et al. 2024, ApJS, 272, 26, doi: 10.3847/1538-4365/ad3936

    2024 doi

  29. [38]

    2015, Monthly Notices of the Royal Astronomical Society, 452, 575, doi: 10.1093/mnras/stv1340

    Sijacki, D., Vogelsberger, M., Genel, S., et al. 2015, Monthly Notices of the Royal Astronomical Society, 452, 575, doi: 10.1093/mnras/stv1340

    2015 doi

  30. [39]

    Silk, J., & Rees, M. J. 1998, A&A, 331, L1, doi: 10.48550/arXiv.astro-ph/9801013

    1998 doi arXiv

  31. [40]

    S., Hopkins, P

    Somerville, R. S., Hopkins, P. F., Cox, T. J., Robertson, B. E., & Hernquist, L. 2008, Monthly Notices of the Royal Astronomical Society, 391, 481, doi: 10.1111/j.1365-2966.2008.13805.x

    2008

  32. [41]

    S., Popping, G., & Trager, S

    Somerville, R. S., Popping, G., & Trager, S. C. 2015, Monthly Notices of the Royal Astronomical Society, 453, 4337, doi: 10.1093/mnras/stv1877 10

    2015 doi

  33. [42]

    2018, MNRAS, 475, 676, doi: 10.1093/mnras/stx3304

    Springel, V., Pakmor, R., Pillepich, A., et al. 2018, MNRAS, 475, 676, doi: 10.1093/mnras/stx3304

    2018 doi

  34. [43]

    Stevens, A. R. H., Croton, D. J., & Mutch, S. J. 2016, Monthly Notices of the Royal Astronomical Society, 461, 859, doi: 10.1093/mnras/stw1332

    2016 doi

  35. [44]

    A., Bell, E

    Terrazas, B. A., Bell, E. F., Woo, J., & Henriques, B. M. B. 2017, ApJ, 844, 170, doi: 10.3847/1538-4357/aa7d07

    2017 doi

  36. [45]

    2002, ApJ, 574, 740, doi: 10.1086/341002 Van Der Walt, S., Colbert, S

    Tremaine, S., Gebhardt, K., Bender, R., et al. 2002, ApJ, 574, 740, doi: 10.1086/341002 Van Der Walt, S., Colbert, S. C., & Varoquaux, G. 2011, Computing in Science and Engineering, 13, 22, doi: 10.1109/MCSE.2011.37

    2002 doi

  37. [46]

    E., et al

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

    2020 doi

  38. [47]

    M., & Koo, D

    Woo, J., Dekel, A., Faber, S. M., & Koo, D. C. 2015, MNRAS, 448, 237, doi: 10.1093/mnras/stu2755

    2015 doi

  39. [48]

    H., Robotham, A

    Wright, A. H., Robotham, A. S. G., Driver, S. P., et al. 2017, MNRAS, 470, 283, doi: 10.1093/mnras/stx1149 11 APPENDIX A. A. SMF OF CENTRAL STAR-FORMING AND QUENCHED GALAXIES FOR SMBH SEEDING AND ALL FIXED CONDITIONAL DISTRIBUTION MODELS AT Z = 0 Figure 5 shows the SMF of cent...

    2017 doi

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.