Pith. sign in

REVIEW 4 major objections 6 minor 2 cited by

Modeling Cosmic Rays at AGN Jet-Driven Shock Fronts

T0 review · 4 major / 6 minor · reviewed 2026-08-09 · deepseek-v4-flash

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

Pith's one-line read Cosmic rays generated at the jet's large-scale shock front, rather than at the black hole, are what let an AGN jet suppress cooling flows and quench star formation in a massive halo.

desk verdict A useful, honest simulation study showing that where AGN cosmic rays are injected (shock front vs black hole vicinity) changes whether feedback self-regulates; the central result is probably robust but rests on an acknowledged toy prescription that needs sharper validation. read the letter →

arxiv 2502.00927 v1 pith:UYSLVMFA submitted 2025-02-02 astro-ph.GA astro-ph.HE

Kung-Yi Su , Greg L. Bryan , Philip F. Hopkins , Priyamvada Natarajan , Sam B. Ponnada , Razieh Emami , Yue Samuel Lu This is my paper · ORCID
classification astro-ph.GAastro-ph.HE
keywords AGNfeedbackcosmicraysjet-drivenshockscoolingflowsgalaxyquenchingmagnetohydrodynamicsimulationsjetprecessioncircumgalacticmedium
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

Massive galaxies and clusters stay red and dead only if something stops their hot gas from cooling and forming stars. This paper argues that the location of cosmic-ray production in AGN jets is that something. Using magnetohydrodynamic simulations of a $10^{14}\,M_\odot$ halo with live black-hole accretion, it shows that cosmic rays made at the large-scale jet-driven shock front, rather than near the black hole, leave the jet with a higher energy flux, spread the cosmic rays to larger radii, and suppress cooling flows and star formation more effectively. The paper also identifies a sweet spot for jet precession, periods of roughly tens of megayears, that puts the shock front at the cooling radius where it does the most work.

What carries the argument

The central mechanism is a particle-spawning jet with a deceleration trigger for cosmic-ray injection. New gas particles are launched from the black hole; when one decelerates to one quarter of its launch velocity, which for a strong shock in $ gamma=5/3$ gas is the post-shock velocity, the code deposits a fixed cosmic-ray energy per unit mass at that location, modeling Fermi acceleration at the large-scale jet cocoon shock. The argument then runs through the cosmic-ray pressure gradient: shock-injected cosmic rays build a pressure gradient that offsets gravity at the cooling radius, while black-hole-injected cosmic rays push on dense gas near the hole, suppress accretion, and starve the jet.

What would settle it

Run one of the live-accretion jet models with an explicit on-the-fly shock finder that deposits cosmic rays wherever strong shocks are actually detected; if the differences between black-hole injection and shock-front injection in accretion duty cycle and star formation shrink or vanish, the proposed mechanism is not the operative one. Alternatively, measure the gamma-ray radial profile of a cool-core cluster hosting a precessing jet: the model predicts a less centrally concentrated gamma-ray flux for jets with roughly 10 to 100 Myr precession than for black-hole-injected cosmic rays.

Watch

Extended reading notes

Core claim

This paper argues that where cosmic rays are put into an AGN jet matters as much as how much energy they carry. In simulations of a cool-core $10^{14}\,M_\odot$ halo, injecting cosmic rays near the black hole inflates pressure in the accretion region, throttles black-hole accretion, and leaves the jet too weak to reach large radii; this produces episodic accretion and little impact on the cooling flow. Injecting the same cosmic-ray energy at the large-scale jet-driven shock front instead preserves a higher overall jet energy flux, disperses cosmic rays to larger radii, and maintains a cosmic-ray pressure gradient at roughly $10$\,--\,$30$ kpc that balances gravity near the cooling radius. The result is more effective, longer-lived suppression of the cooling flow and of star formation. The paper further finds that a precession period of roughly tens of megayears places the shock front at the inner circumgalactic medium, which is where the cooling flow is strongest, and that the combination of shock-front injection, a $ lesssim$100 Myr precession period, and a cosmic-ray energy fraction near 0.3 of the jet energy produces the strongest quenching seen in its live-accretion runs.

Load-bearing premise

The result rests on treating a jet particle's slowdown to one quarter of its launch speed as a reliable stand-in for crossing a strong shock, and on depositing a fixed cosmic-ray energy per unit mass at that moment without strictly conserving total energy.

Editorial extensions

If this is right

  • At fixed jet power, shock-front cosmic-ray injection keeps the black-hole accretion duty cycle long, roughly 0.5 to 1 Gyr, rather than episodic on a 100 to 200 Myr timescale, so the jet carries more total energy to the halo.
  • A jet precession period of roughly 10 to 100 Myr places the shock front at a few tens of kiloparsecs, near the cooling radius; non-precessing jets push cosmic rays into a narrow beam beyond 100 kpc and barely suppress the cooling flow.
  • Raising the cosmic-ray fraction to about 0.3 of the jet energy, with shock-front injection and a 100 Myr precession period, gives the strongest quenching, with star formation rates below about $5\,M_\odot\,\mathrm{yr}^{-1}$ sustained with duty cycles of at least 0.5 Gyr.
  • Predicted gamma-ray fluxes stay below the Fermi-LAT upper limit of about $1.8\times10^{42}\,\mathrm{erg\,s^{-1}}$, and shock-front injection lowers the central gamma-ray flux because the cosmic-ray distribution is less concentrated.
  • Including cosmic rays from supernovae adds to the cosmic-ray pressure and works together with AGN cosmic rays to suppress cooling flows more thoroughly.

Reading between the lines

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

  • If the injection locus is the controlling variable, sub-grid AGN feedback implementations that deposit cosmic rays only at the black hole will systematically underestimate cosmic-ray pressure support at the cooling radius and overestimate the episodicity of black-hole accretion.
  • The same logic gives a direct observational discriminant: cool-core clusters hosting precessing jets with tens-of-megayear periods should show flatter gamma-ray radial profiles than those with steady jets, a signature that deeper gamma-ray observations could test.
  • Because the velocity-deceleration trigger is only a proxy for shock acceleration, rerunning the live-accretion cases with an explicit shock finder and continuous cosmic-ray injection would test whether the optimal precession period shifts with the actual shock location.
  • The proposed optimal precession timescale of roughly tens of megayears suggests that jet direction changes on that timescale may be a physical requirement for efficient AGN self-regulation, not just a numerical choice.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. This paper uses FIRE-2 MHD simulations of a ~10^14 Msun halo to compare AGN jet feedback models in which cosmic rays (CRs) are injected either in the black hole vicinity or at large-scale jet-driven shock fronts, using both constant-flux jets and live black hole accretion, with variations in precession, opening angle, jet velocity, and CR energy fraction. The central claim is that injection at the jet-driven shock front produces more extended CR distributions, preserves a higher sustained jet energy flux, lengthens the black hole accretion duty cycle, and suppresses cooling flows and star formation more effectively than injection near the black hole. The paper also identifies an 'optimal' precession period of order tens of Myr that places shocks near the cooling radius, and it estimates gamma-ray fluxes, finding all runs below the Fermi-LAT Coma limit.

Significance. If the shock-front injection prescription is physically faithful, the result is significant: it identifies the CR production locus, not just the total CR energy budget, as a control on AGN feedback self-regulation, and it proposes a numerically practical method for including large-scale CR acceleration in galaxy simulations. The paper is honest about its limitations in Sec. 5.3 and does not overclaim success: even the most successful run only intermittently reaches SFR < 5 Msun/yr, and stricter quenching definitions yield even shorter periods. Strengths include the systematic parameter coverage, live gravitational-torque black hole accretion rather than fixed energy injection, anisotropic CR transport with streaming and diffusion, and a central comparison that is not defined in terms of fitted parameters. The main weaknesses are that the shock trigger is an unvalidated kinematic proxy and that the energy budget at CR injection is not conserved in a quantified way; these issues directly affect the headline comparison.

major comments (4)
  1. [Sec. 5.3] The shock-front CR injection model rests on the assertion that a spawned jet particle whose velocity decelerates to 1/4 of its launch velocity 'has almost surely encountered a strong shock.' This is a kinematic trigger, not a shock identification: deceleration to that level can occur through adiabatic expansion, turbulent drag, or numerical dissipation without a strong shock, while a real shock crossing need not reduce the particle speed to exactly one quarter. The BH-vs-shock comparison in Sec. 4.1 and Figs. 6, 8, and 9 could therefore trace where the v = v_launch/4 contour lies rather than where physical CR acceleration at shocks occurs. Because this assumption is load-bearing for the central claim, I request an independent check: for at least a subset of runs, identify actual shock surfaces in post-processing (e.g., via velocity jumps, entropy jumps, or Mach-number criteria) and compare them with the injection sites, or vary the trigger threshold to demonstrate that the qualitative contrast is robust. The paper's own Sec. 5.3 limitation statement correctly flags this as a toy model, but the central claim is currently conditional on this trigger tracking real shocks.
  2. [Sec. 5.3] The statement that 'we did not strictly conserve energy when injecting cosmic ray energy at the shock front' is a concern for the mechanism claimed in Secs. 3.2 and 4.1, because the quenching effect is attributed to CR pressure gradients. If CR energy is added at the trigger without a compensating subtraction from the gas kinetic or thermal energy, or from the jet energy budget defined in Eq. (5), the CR pressure support could be artificially enhanced. The paper says the injected CRs contribute less than 0.3% of the energy budget, but no derivation, table, or figure supports this number, and it appears in tension with the nominal CR energy fractions of 0.1-0.3 quoted in Tables 2 and 3. Please clarify the implementation (what energy, if anything, is removed at injection) and provide a quantitative energy-accounting check for a representative shock-injection run.
  3. [Sec. 4.5 and Abstract] The identification of an 'optimal range of jet precession periods (~ tens of Myr)' goes beyond the simulated values. The constant-flux and live-accretion runs include precession periods of 10 Myr and 100 Myr, plus an effectively very short period for the isotropic wind, but no intermediate period such as 30-50 Myr. The finding that 100 Myr places shocks near 30 kpc while 10 Myr gives faster early suppression does not uniquely determine a 'tens of Myr' optimum; it is an interpolation between two points. Please add an intermediate-period run or soften the claim to 'periods between 10 and 100 Myr' with an explicit caveat that the location of the optimum is not resolved by the present grid.
  4. [Sec. 5.2, Eq. (8), Fig. 12] The gamma-ray 'prediction' is partially circular. The fiducial diffusivity kappa_CR = 1e29 cm^2/s was adopted because, as stated in Sec. 2, previous work matching observed gamma-ray luminosities requires this value. Equation (8) and the accumulated gamma-ray fluxes in Fig. 12 use this same kappa_CR, so the statement that all runs fall within the Fermi-LAT Coma limit is a consistency check with a parameter already calibrated to gamma-ray data, not an independent prediction. Please state this explicitly in Sec. 5.2 and, if possible, show the sensitivity of the gamma-ray fluxes to kappa_CR (for example, a factor of 3 variation) so the reader can judge whether the conclusion is robust.
minor comments (6)
  1. [Sec. 2.3] 'sbugrid' is a typo for 'subgrid'; please correct.
  2. [Sec. 5.1] The heading 'Eenergy flux required for cosmic rays to quench star formation' contains a typo ('Eenergy').
  3. [Tables 2 and 3] In both table captions, 'processing period' should be 'precession period'; the Table 2 caption also repeats 'completely completely'.
  4. [Figs. 6 and 8] The legend entry 'all CR at BH' is not defined in the captions; please identify the run (live-v3e3-CRBH1) and note that its CR energy fraction is order unity.
  5. [Tables 2 and 3] The parenthetical values in the magnetic-energy columns (mass-weighted averages) are easy to misread; a footnote or explicit header would improve clarity.
  6. [Sec. 5.2, Eq. (8)] The notation switches between n_H and n_gas in the same equation; please define n_H explicitly and use a consistent symbol.

Circularity Check

1 steps flagged · score 4.0 of 10

Fermi-LAT consistency check reuses a diffusivity calibrated to gamma-ray luminosities in the authors' prior work; the central injection-site simulation comparison is otherwise self-contained.

  1. fitted input called prediction [Sec. 2 (cosmic-ray transport, fiducial kappa) and Sec. 5.2 (Eq. 8, gamma-ray implications)]
    "Previous studies (Chan et al. 2019; Hopkins et al. 2019, 2021c) demonstrated that matching observed γ-ray luminosities requires a diffusivity of κCR ∼ 1029 cm2 s−1 ... Thus, we adopt this as our fiducial value."

    The gamma-ray 'prediction' in Sec. 5.2 is computed from the steady-state relation PCR ∼ ˙ECR/(12π˜κr) using exactly this κCR. Because κCR was chosen, in the authors' own FIRE papers, to reproduce observed γ-ray luminosities, the subsequent Fermi-LAT consistency check (Eq. 8 compared to the Coma upper limit) is a re-importation of the same calibrated transport coefficient rather than an independent falsification. The circularity is localized: the central BH-vicinity versus shock-front comparison is a direct simulation experiment and does not depend on this fitted parameter.

full rationale

The paper's main claim is a controlled simulation comparison between two CR injection prescriptions, and the qualitative conclusions (higher sustained jet energy flux, longer BH accretion duty cycle, more effective quenching for shock-front injection) emerge from the coupled accretion/jet dynamics rather than from a fit. No parameter is fitted to the target quantities in the main comparison. The v/4-deceleration shock trigger is an acknowledged toy-model assumption (Sec. 5.3) and a robustness/correctness caveat, not a circular step. The only partially circular element is the ancillary gamma-ray estimate, which adopts the gamma-ray-calibrated diffusivity of prior same-author papers and then presents the resulting gamma-ray flux as a check against Fermi-LAT. That step is not load-bearing for the central claim, so the overall circularity score is moderate rather than severe.

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

The central claim rests on the FIRE-2/GIZMO simulation framework, the gravitational-torque accretion model, and an ad hoc shock-injection trigger. No new physical entity is introduced. The main free parameters are scanned inputs, except the diffusivity, which was calibrated to gamma-ray observations and later used for gamma-ray predictions.

free parameters (6)
  • CR diffusivity kappa_CR = 1e29 cm^2/s
    Adopted from prior FIRE papers, where it was chosen to match observed gamma-ray luminosities; used in all runs and in the analytic gamma-ray estimate (Sec. 5.2).
  • CR shock-injection velocity threshold = 1/4 of jet launch velocity
    Toy-model trigger: CRs are deposited when spawned jet particles decelerate to 25% of launch speed, assumed to mark a strong shock (Sec. 2.3).
  • Jet precession period = 10 Myr and 100 Myr (5 kyr for isotropic run)
    Scanned by hand; the claim of an optimal 'tens of Myr' range is interpolated between these values.
  • CR energy fraction in jet = 0.1, 0.3, 1.0
    Scanned input; no fitting involved.
  • Jet velocity = 3e3, 1e4, 3e4 km/s
    Scanned input; the study concludes an optimal velocity near 1e4 km/s (Sec. 4.5).
  • SNe CR fraction f_CR,SNe = 0.1
    Standard FIRE value, used in one live-accretion run.
assumptions (6)
  • domain assumption FIRE-2 star formation and stellar feedback model reproduces the ISM physics needed here.
    Adopted from Hopkins et al. 2018; all runs rely on it.
  • domain assumption Single-bin ultrarelativistic CR model with constant diffusivity and Alfven-speed streaming is adequate.
    Sec. 2; simplified transport model from prior FIRE work.
  • domain assumption Gravitational torque accretion model with an alpha-disk reservoir is valid for BH growth.
    Sec. 2.2; alternative Bondi accretion not tested.
  • ad hoc to paper Jet particle deceleration to 1/4 launch velocity marks a strong shock and a valid CR injection site.
    Sec. 2.3; explicit toy model, acknowledged in Sec. 5.3.
  • domain assumption Initial conditions replicate z=0 cool-core cluster profiles for a 10^14 solar mass halo.
    Sec. 2.1; from Su et al. 2019, 2020, 2021, 2024.
  • domain assumption Diffusivity calibrated to gamma-ray observations remains valid for these cluster conditions.
    Sec. 2; used to predict gamma-ray fluxes in Sec. 5.2, creating partial circularity.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Modeling Cosmic Rays at AGN Jet-Driven Shock Fronts." pith.science (2026). https://pith.science/paper/UYSLVMFA

@misc{pith2026250200927,
  author       = {Pith},
  title        = {Pith review of: Modeling Cosmic Rays at AGN Jet-Driven Shock Fronts},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UYSLVMFA}},
  note         = {Machine review of arXiv:2502.00927}
}
abstract

Active Galactic Nuclei (AGN) feedback is a key physical mechanism proposed to regulate star formation, primarily in massive galaxies. In particular, cosmic rays associated with AGN jets have the potential to efficiently suppress cooling flows and quench star formation. The locus of cosmic ray production and their coupling to gas play a crucial role in the overall self-regulation process. To investigate this in detail, we conduct high-resolution, non-cosmological MHD simulations of a massive $10^{14} {\rm M_\odot}$ halo using the FIRE-2 (Feedback In Realistic Environments) stellar feedback model. We explore a variety of AGN jet feedback scenarios with cosmic rays, examining different values for the cosmic ray energy fraction in jets, cosmic ray coupling sites (in the black hole vicinity versus at the large-scale jet-driven shock front), and jet precession parameters. Our findings indicate that when cosmic rays are injected near the black hole, they efficiently inhibit black hole accretion by suppressing the density before the jet propagates out to large radii. As a result, this leads to episodic black hole accretion, with the jet not having sufficient energy flux to reach large radii and impact cooling flows. Conversely, if the cosmic rays are injected at the jet-driven shock front, not only does the jet sustain a higher overall energy flux for an extended period, but it also disperses cosmic rays out to larger radii, more effectively suppressing the cooling flow. Furthermore, the period and angle of jet precession can influence the position of shock fronts. We identify an optimal range of jet precession periods ($\sim$ tens of Myr) that generates shocks at the inner circumgalactic medium, where cooling flows are most severe. We report that this specific configuration offers the most effective scenario for cosmic rays at the shock front to suppress the cooling flow and star formation.

Figures

Figures reproduced from arXiv: 2502.00927 by the authors.

Figure 1
Figure 1. Star formation rate (top) and core baryonic mass within 30 kpc (bottom) for the constant jet energy flux runs. With the same energy flux, injecting cosmic rays at the jet-driven shock front (CRshock0.1-pr100Myr) results in delayed suppression of star formation compared to injecting cos￾mic rays near the black hole (CRBH0.1-pr100Myr). Additionally, faster variations in wind direction (‘iso’, pr10Myr’, ‘pr100Myr’ from… view at source ↗
Figure 2
Figure 2. Mass-weighted median cosmic ray pressure profile (calculated over 0.95–1 Gyr or the last 50 Myr of the run) of the constant jet energy flux runs. The shaded region represents the 2% and 98% percentiles at each radius. Injecting cosmic rays at the large-scale jet-driven shock front (CRshock0.1-pr100Myr) produces a slightly more extended cosmic ray dis￾tribution compared to injecting cosmic rays near the black hole (C… view at source ↗
Figure 3
Figure 3. Morphological plot of the constant jet energy flux runs for a central slice within |y| < 10 kpc (at 1 Gyr or at the end of the run). The upper half shows the cosmic ray distribution, while the lower half displays the radial velocity. Blue curves represent the magnetic field lines. Injecting cosmic rays at the large-scale jet-driven shock front (CRshock0.1-pr100Myr) results in a slightly more extended cosmic ray dist… view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Comparison of gravitational, rotational, thermal pressure, and CR pressure gradient acceleration in the constant jet energy flux runs (averaged over 0.95–1 Gyr or the last 50 Myr of the run). The centrifugal acceleration is defined as GMenc/r2 − v 2 rot/r. In the core …
Figure 5
Figure 5. Figure 5: Mean gas density (top row), X-ray cooling luminosity-weighted density (second row), luminosity-weighted temperature (third row), and luminosity-weighted entropy (bottom row) versus radius, averaged over 0.95–1 Gyr or the last 50 Myr of the constant jet energy flux runs…
Figure 6
Figure 6. Figure 6: Comparison of star formation rate (first row), core baryonic mass within 30 kpc (second row), black hole accretion rate (third row), jet mass flux (fourth row), jet energy flux (fifth row), and cosmic ray energy flux (bottom row) for the live-accretion runs. Unless oth…
Figure 7
Figure 7. Figure 7: The density around the black hole vicinity for a subset of runs. Injecting cosmic rays near the black hole results in episodic suppression of density in the vicinity of the black hole during the first 0.5 Gyr. When a significant fraction of the energy is in cosmic rays…
Figure 8
Figure 8. Figure 8: Mass-weighted median cosmic ray pressure profile (calculated over 0.95–1 Gyr or the last 50 Myr of the run) for the live accretion runs. The shaded region represents the 2% and 98% percentiles at each radius. Unless otherwise mentioned, the plotted runs have a 100 Myr …
Figure 9
Figure 9. Figure 9: Morphological plot of the live accretion runs for a central slice within |y| < 10 kpc (at 1 Gyr or at the end of the run). The upper half shows the cosmic ray distribution, while the lower half displays the radial velocity. Blue curves represent the magnetic field line…
Figure 10
Figure 10. Figure 10: Comparison of gravitational, rotational, thermal pressure, and CR pressure gradient acceleration in the live accretion runs (averaged over 0.95–1 Gyr or the last 50 Myr of the run). The centrifugal acceleration is defined as GMenc/r2 − v 2 rot/r. In the core region, w…
Figure 11
Figure 11. Figure 11: Mean gas density (top row), X-ray cooling luminosity-weighted density (second row), luminosity-weighted temperature (third row), and luminosity￾weighted entropy (bottom row) versus radius, averaged over 0.95–1 Gyr or the last 50 Myr of the live accretion runs. Unless …
Figure 12
Figure 12. Figure 12: Comparison of the accumulated gamma-ray energy flux up to a certain radius for all the runs (averaged over 0.95–1 Gyr or the last 50 Myr of the run). Unless otherwise mentioned, the plotted runs have a 100 Myr precession period and 0.1 energy in CRs. All runs have gam…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Time-Dependent Cosmic Ray Halos from Bursty Star Formation and Active Galactic Nuclei: Semi-Analytic Formalism and Galaxy Formation Implications

    astro-ph.GA 2025-09 conditional novelty 6.0 of 10

    Time-dependent injection from bursty star formation or episodic black hole accretion substantially modifies cosmic ray pressure profiles in massive galaxy halos, flattening them at large radii relative to steady-state...

  2. CRESCENDO II: Spectral cosmic rays with improved energy losses and realistic supernova seeding

    astro-ph.HE 2026-07 conditional novelty 5.0 of 10

    CRESCENDO's spectral cosmic-ray solver now includes improved energy-loss processes, non-ultra-relativistic energy/pressure integrals, and supernova-remnant template injection.

Reference graph

Works this paper leans on

92 extracted references · 9 canonical work pages · cited by 2 Pith papers

  1. [1]

    Ackermann M., et al., 2016, @doi [ ] 10.3847/0004-637X/819/2/149 , http://adsabs.harvard.edu/abs/2016ApJ...819..149A 819, 149

    doi:10.3847/0004-637x/819/2/149 2016

  2. [2]

    F., Feldmann R., Torrey P., Wetzel A., Kere s D., 2017, @doi [ ] 10.1093/mnrasl/slx161 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.472L.109A 472, L109

    Angl \'e s-Alc \'a zar D., Faucher-Gigu \`e re C.-A., Quataert E., Hopkins P. F., Feldmann R., Torrey P., Wetzel A., Kere s D., 2017, @doi [ ] 10.1093/mnrasl/slx161 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.472L.109A 472, L109

    doi:10.1093/mnrasl/slx161 2017

  3. [3]

    K., Glazebrook K., Brinkmann J., Ivezi \'c Z ., Lupton R

    Baldry I. K., Glazebrook K., Brinkmann J., Ivezi \'c Z ., Lupton R. H., Nichol R. C., Szalay A. S., 2004, @doi [ ] 10.1086/380092 , http://adsabs.harvard.edu/abs/2004ApJ...600..681B 600, 681

    doi:10.1086/380092 2004

  4. [4]

    F., McIntosh D

    Bell E. F., McIntosh D. H., Katz N., Weinberg M. D., 2003, @doi [ ] 10.1086/378847 , http://adsabs.harvard.edu/abs/2003ApJS..149..289B 149, 289

    doi:10.1086/378847 2003

  5. [5]

    A., McNamara B

    B \^ rzan L., Rafferty D. A., McNamara B. R., Wise M. W., Nulsen P. E. J., 2004, @doi [ ] 10.1086/383519 , https://ui.adsabs.harvard.edu/abs/2004ApJ...607..800B 607, 800

    doi:10.1086/383519 2004

  6. [6]

    R., Eisenstein D., Hogg D

    Blanton M. R., Eisenstein D., Hogg D. W., Schlegel D. J., Brinkmann J., 2005, @doi [ ] 10.1086/422897 , http://adsabs.harvard.edu/abs/2005ApJ...629..143B 629, 143

    doi:10.1086/422897 2005

  7. [8]

    S., Quinn T

    Butsky I. S., Quinn T. R., 2018, @doi [ ] 10.3847/1538-4357/aaeac2 , http://adsabs.harvard.edu/abs/2018ApJ...868..108B 868, 108

    doi:10.3847/1538-4357/aaeac2 2018

  8. [9]

    S., Nakum S., Ponnada S

    Butsky I. S., Nakum S., Ponnada S. B., Hummels C. B., Ji S., Hopkins P. F., 2023, @doi [ ] 10.1093/mnras/stad671 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.521.2477B 521, 2477

    doi:10.1093/mnras/stad671 2023

Show all 92 references
  1. [10]

    K., Kere s D., Hopkins P

    Chan T. K., Kere s D., Hopkins P. F., Quataert E., Su K. Y., Hayward C. C., Faucher-Gigu \`e re C. A., 2019, @doi [ ] 10.1093/mnras/stz1895 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.3716C 488, 3716

    2019 doi

  2. [11]

    P., Naab T., Johansson P

    Choi E., Ostriker J. P., Naab T., Johansson P. H., 2012, @doi [ ] 10.1088/0004-637X/754/2/125 , http://adsabs.harvard.edu/abs/2012ApJ...754..125C 754, 125

    2012 doi

  3. [12]

    P., Naab T., Oser L., Moster B

    Choi E., Ostriker J. P., Naab T., Oser L., Moster B. P., 2015, @doi [ ] 10.1093/mnras/stv575 , http://adsabs.harvard.edu/abs/2015MNRAS.449.4105C 449, 4105

    2015 doi

  4. [13]

    P., 2001, @doi [ ] 10.1086/320053 , http://adsabs.harvard.edu/abs/2001ApJ...551..131C 551, 131

    Ciotti L., Ostriker J. P., 2001, @doi [ ] 10.1086/320053 , http://adsabs.harvard.edu/abs/2001ApJ...551..131C 551, 131

    2001 doi

  5. [14]

    P., Proga D., 2009, @doi [ ] 10.1088/0004-637X/699/1/89 , http://adsabs.harvard.edu/abs/2009ApJ...699...89C 699, 89

    Ciotti L., Ostriker J. P., Proga D., 2009, @doi [ ] 10.1088/0004-637X/699/1/89 , http://adsabs.harvard.edu/abs/2009ApJ...699...89C 699, 89

    2009 doi

  6. [16]

    Dekel A., Birnboim Y., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10145.x , http://adsabs.harvard.edu/abs/2006MNRAS.368....2D 368, 2

    2006

  7. [17]

    P., Emsellem E., 2017, @doi [ ] 10.1093/mnras/stx473 , http://adsabs.harvard.edu/abs/2017MNRAS.468..751E 468, 751

    Eisenreich M., Naab T., Choi E., Ostriker J. P., Emsellem E., 2017, @doi [ ] 10.1093/mnras/stx473 , http://adsabs.harvard.edu/abs/2017MNRAS.468..751E 468, 751

    2017 doi

  8. [19]

    C., 2012, @doi [ ] 10.1146/annurev-astro-081811-125521 , http://adsabs.harvard.edu/abs/2012ARA

    Fabian A. C., 2012, @doi [ ] 10.1146/annurev-astro-081811-125521 , http://adsabs.harvard.edu/abs/2012ARA

    2012 doi

  9. [20]

    C., Arnaud K

    Fabian A. C., Arnaud K. A., Bautz M. W., Tawara Y., 1994, @doi [ ] 10.1086/187633 , http://adsabs.harvard.edu/abs/1994ApJ...436L..63F 436, L63

    1994 doi

  10. [21]

    Feldmann R., Mayer L., 2015, @doi [ ] 10.1093/mnras/stu2207 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.446.1939F 446, 1939

    2015 doi

  11. [22]

    Gaspari M., S a dowski A., 2017, @doi [ ] 10.3847/1538-4357/aa61a3 , http://adsabs.harvard.edu/abs/2017ApJ...837..149G 837, 149

    2017 doi

  12. [23]

    W., O'Shea B

    Glines F. W., O'Shea B. W., Voit G. M., 2020, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2020arXiv200400021G p. arXiv:2004.00021

    2020 arXiv

  13. [24]

    Y., Hopkins P

    Grudi \'c M. Y., Hopkins P. F., Quataert E., Murray N., 2019, @doi [ ] 10.1093/mnras/sty3386 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.483.5548G 483, 5548

    2019 doi

  14. [26]

    H \"a ring N., Rix H.-W., 2004, @doi [ ] 10.1086/383567 , http://adsabs.harvard.edu/abs/2004ApJ...604L..89H 604, L89

    2004 doi

  15. [27]

    Hernquist L., 1990, @doi [ ] 10.1086/168845 , http://adsabs.harvard.edu/abs/1990ApJ...356..359H 356, 359

    1990 doi

  16. [28]

    C., Alexander D

    Hickox R. C., Alexander D. M., 2018, @doi [ ] 10.1146/annurev-astro-081817-051803 , https://ui.adsabs.harvard.edu/abs/2018ARA&A..56..625H 56, 625

    2018 doi

  17. [29]

    F., 2015, @doi [ ] 10.1093/mnras/stv195 , http://adsabs.harvard.edu/abs/2015MNRAS.450...53H 450, 53

    Hopkins P. F., 2015, @doi [ ] 10.1093/mnras/stv195 , http://adsabs.harvard.edu/abs/2015MNRAS.450...53H 450, 53

    2015 doi

  18. [30]

    F., 2016, @doi [ ] 10.1093/mnras/stw1578 , http://adsabs.harvard.edu/abs/2016MNRAS.462..576H 462, 576

    Hopkins P. F., 2016, @doi [ ] 10.1093/mnras/stw1578 , http://adsabs.harvard.edu/abs/2016MNRAS.462..576H 462, 576

    2016 doi

  19. [31]

    F., 2017, @doi [ ] 10.1093/mnras/stw3306 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.466.3387H 466, 3387

    Hopkins P. F., 2017, @doi [ ] 10.1093/mnras/stw3306 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.466.3387H 466, 3387

    2017 doi

  20. [32]

    F., Quataert E., 2010, @doi [ ] 10.1111/j.1365-2966.2010.17064.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.407.1529H 407, 1529

    Hopkins P. F., Quataert E., 2010, @doi [ ] 10.1111/j.1365-2966.2010.17064.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.407.1529H 407, 1529

    2010

  21. [33]

    F., Quataert E., 2011, @doi [ ] 10.1111/j.1365-2966.2011.18542.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.415.1027H 415, 1027

    Hopkins P. F., Quataert E., 2011, @doi [ ] 10.1111/j.1365-2966.2011.18542.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.415.1027H 415, 1027

    2011

  22. [34]

    F., Raives M

    Hopkins P. F., Raives M. J., 2016, @doi [ ] 10.1093/mnras/stv2180 , http://adsabs.harvard.edu/abs/2016MNRAS.455...51H 455, 51

    2016 doi

  23. [35]

    F., Hernquist L., Cox T

    Hopkins P. F., Hernquist L., Cox T. J., Di Matteo T., Martini P., Robertson B., Springel V., 2005, @doi [ ] 10.1086/432438 , http://adsabs.harvard.edu/abs/2005ApJ...630..705H 630, 705

    2005 doi

  24. [36]

    F., Hernquist L., Cox T

    Hopkins P. F., Hernquist L., Cox T. J., Di Matteo T., Robertson B., Springel V., 2006, @doi [ ] 10.1086/499298 , http://adsabs.harvard.edu/abs/2006ApJS..163....1H 163, 1

    2006 doi

  25. [37]

    F., Narayanan D., Murray N., 2013, @doi [ ] 10.1093/mnras/stt723 , http://adsabs.harvard.edu/abs/2013MNRAS.432.2647H 432, 2647

    Hopkins P. F., Narayanan D., Murray N., 2013, @doi [ ] 10.1093/mnras/stt723 , http://adsabs.harvard.edu/abs/2013MNRAS.432.2647H 432, 2647

    2013 doi

  26. [38]

    F., et al., 2018, @doi [ ] 10.1093/mnras/sty1690 , https://ui.adsabs.harvard.edu/#abs/2018MNRAS.480..800H 480, 800

    Hopkins P. F., et al., 2018, @doi [ ] 10.1093/mnras/sty1690 , https://ui.adsabs.harvard.edu/#abs/2018MNRAS.480..800H 480, 800

    2018 doi

  27. [39]

    F., et al., 2019, @doi [ ] 10.1093/mnras/stz3321 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.tmp.2993H p

    Hopkins P. F., et al., 2019, @doi [ ] 10.1093/mnras/stz3321 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.tmp.2993H p. 2993

    2019 doi

  28. [40]

    F., Chan T

    Hopkins P. F., Chan T. K., Ji S., Hummels C. B., Kere s D., Quataert E., Faucher-Gigu \`e re C.-A., 2021a, @doi [ ] 10.1093/mnras/staa3690 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.501.3640H 501, 3640

    doi

  29. [41]

    F., Chan T

    Hopkins P. F., Chan T. K., Squire J., Quataert E., Ji S., Kere s D., Faucher-Gigu \`e re C.-A., 2021b, @doi [ ] 10.1093/mnras/staa3692 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.501.3663H 501, 3663

    doi

  30. [42]

    F., Squire J., Chan T

    Hopkins P. F., Squire J., Chan T. K., Quataert E., Ji S., Kere s D., Faucher-Gigu \`e re C.-A., 2021c, @doi [ ] 10.1093/mnras/staa3691 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.501.4184H 501, 4184

    doi

  31. [43]

    F., Wellons S., Angl \'e s-Alc \'a zar D., Faucher-Gigu \`e re C.-A., Grudi \'c M

    Hopkins P. F., Wellons S., Angl \'e s-Alc \'a zar D., Faucher-Gigu \`e re C.-A., Grudi \'c M. Y., 2022a, @doi [ ] 10.1093/mnras/stab3458 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.510..630H 510, 630

    doi

  32. [44]

    F., Squire J., Butsky I

    Hopkins P. F., Squire J., Butsky I. S., Ji S., 2022b, @doi [ ] 10.1093/mnras/stac2909 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.517.5413H 517, 5413

    doi

  33. [45]

    J., Buote D

    Humphrey P. J., Buote D. A., 2013, @doi [ ] 10.1093/mnras/stt1414 , http://adsabs.harvard.edu/abs/2013MNRAS.436.2879H 436, 2879

    2013 doi

  34. [46]

    J., Buote D

    Humphrey P. J., Buote D. A., Brighenti F., Flohic H. M. L. G., Gastaldello F., Mathews W. G., 2012, @doi [ ] 10.1088/0004-637X/748/1/11 , http://adsabs.harvard.edu/abs/2012ApJ...748...11H 748, 11

    2012 doi

  35. [47]

    Ji S., et al., 2020, @doi [ ] 10.1093/mnras/staa1849 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.496.4221J 496, 4221

    2020 doi

  36. [49]

    H., Dav \'e R., 2005, @doi [ ] 10.1111/j.1365-2966.2005.09451.x , http://adsabs.harvard.edu/abs/2005MNRAS.363....2K 363, 2

    Kere s D., Katz N., Weinberg D. H., Dav \'e R., 2005, @doi [ ] 10.1111/j.1365-2966.2005.09451.x , http://adsabs.harvard.edu/abs/2005MNRAS.363....2K 363, 2

    2005

  37. [50]

    H., 2009, @doi [ ] 10.1111/j.1365-2966.2009.14924.x , http://adsabs.harvard.edu/abs/2009MNRAS.396.2332K 396, 2332

    Kere s D., Katz N., Dav \'e R., Fardal M., Weinberg D. H., 2009, @doi [ ] 10.1111/j.1365-2966.2009.14924.x , http://adsabs.harvard.edu/abs/2009MNRAS.396.2332K 396, 2332

    2009

  38. [51]

    Kroupa P., 2002, @doi [Science] 10.1126/science.1067524 , http://adsabs.harvard.edu/abs/2002Sci...295...82K 295, 82

    2002 doi

  39. [52]

    Leitherer C., et al., 1999, @doi [ ] 10.1086/313233 , http://adsabs.harvard.edu/abs/1999ApJS..123....3L 123, 3

    1999 doi

  40. [53]

    L., Ruszkowski M., Voit G

    Li Y., Bryan G. L., Ruszkowski M., Voit G. M., O'Shea B. W., Donahue M., 2015, @doi [ ] 10.1088/0004-637X/811/2/73 , http://adsabs.harvard.edu/abs/2015ApJ...811...73L 811, 73

    2015 doi

  41. [54]

    Li Y.-P., et al., 2018, @doi [ ] 10.3847/1538-4357/aade8b , https://ui.adsabs.harvard.edu/abs/2018ApJ...866...70L 866, 70

    2018 doi

  42. [55]

    S., Somerville R., Lahav O., Ellis R., 2003, @doi [ ] 10.1046/j.1365-8711.2003.06729.x , http://adsabs.harvard.edu/abs/2003MNRAS.343..871M 343, 871

    Madgwick D. S., Somerville R., Lahav O., Ellis R., 2003, @doi [ ] 10.1046/j.1365-8711.2003.06729.x , http://adsabs.harvard.edu/abs/2003MNRAS.343..871M 343, 871

    2003

  43. [56]

    McDonald M., Veilleux S., Mushotzky R., 2011, @doi [ ] 10.1088/0004-637X/731/1/33 , http://adsabs.harvard.edu/abs/2011ApJ...731...33M 731, 33

    2011 doi

  44. [57]

    McDonald M., et al., 2013, @doi [ ] 10.1088/0004-637X/774/1/23 , http://adsabs.harvard.edu/abs/2013ApJ...774...23M 774, 23

    2013 doi

  45. [58]

    Mernier F., et al., 2017, @doi [ ] 10.1051/0004-6361/201630075 , https://ui.adsabs.harvard.edu/abs/2017A&A...603A..80M 603, A80

    2017 doi

  46. [59]

    S., Reiprich T

    Mittal R., Hudson D. S., Reiprich T. H., Clarke T., 2009, @doi [ ] 10.1051/0004-6361/200810836 , http://adsabs.harvard.edu/abs/2009A

    2009 doi

  47. [60]

    F., Frenk C

    Navarro J. F., Frenk C. S., White S. D. M., 1996, @doi [ ] 10.1086/177173 , http://adsabs.harvard.edu/abs/1996ApJ...462..563N 462, 563

    1996 doi

  48. [61]

    P., et al., 2008, @doi [ ] 10.1086/588212 , http://adsabs.harvard.edu/abs/2008ApJ...681.1035O 681, 1035

    O'Dea C. P., et al., 2008, @doi [ ] 10.1086/588212 , http://adsabs.harvard.edu/abs/2008ApJ...681.1035O 681, 1035

    2008 doi

  49. [62]

    P., 2018, @doi [ ] 10.3847/1538-4357/aaae07 , http://adsabs.harvard.edu/abs/2018ApJ...856..115P 856, 115

    Pellegrini S., Ciotti L., Negri A., Ostriker J. P., 2018, @doi [ ] 10.3847/1538-4357/aaae07 , http://adsabs.harvard.edu/abs/2018ApJ...856..115P 856, 115

    2018 doi

  50. [63]

    B., Fabian A

    Peres C. B., Fabian A. C., Edge A. C., Allen S. W., Johnstone R. M., White D. A., 1998, @doi [ ] 10.1046/j.1365-8711.1998.01624.x , http://adsabs.harvard.edu/abs/1998MNRAS.298..416P 298, 416

    1998

  51. [64]

    R., Fabian A

    Peterson J. R., Fabian A. C., 2006, @doi [ ] 10.1016/j.physrep.2005.12.007 , http://adsabs.harvard.edu/abs/2006PhR...427....1P 427, 1

    2006 doi

  52. [65]

    B., et al., 2024, @doi [ ] 10.1093/mnrasl/slae017 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.530L...1P 530, L1

    Ponnada S. B., et al., 2024, @doi [ ] 10.1093/mnrasl/slae017 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.530L...1P 530, L1

    2024 doi

  53. [66]

    Pozzetti L., et al., 2010, @doi [ ] 10.1051/0004-6361/200913020 , http://adsabs.harvard.edu/abs/2010A

    2010 doi

  54. [67]

    A., McNamara B

    Rafferty D. A., McNamara B. R., Nulsen P. E. J., 2008, @doi [ ] 10.1086/591240 , http://adsabs.harvard.edu/abs/2008ApJ...687..899R 687, 899

    2008 doi

  55. [68]

    H., B \"o hringer H., 2002, @doi [ ] 10.1086/338753 , http://adsabs.harvard.edu/abs/2002ApJ...567..716R 567, 716

    Reiprich T. H., B \"o hringer H., 2002, @doi [ ] 10.1086/338753 , http://adsabs.harvard.edu/abs/2002ApJ...567..716R 567, 716

    2002 doi

  56. [69]

    Sijacki D., Springel V., Di Matteo T., Hernquist L., 2007, @doi [ ] 10.1111/j.1365-2966.2007.12153.x , http://adsabs.harvard.edu/abs/2007MNRAS.380..877S 380, 877

    2007

  57. [70]

    J., 1998, , http://adsabs.harvard.edu/abs/1998A

    Silk J., Rees M. J., 1998, , http://adsabs.harvard.edu/abs/1998A

    1998

  58. [71]

    S., Hopkins P

    Somerville R. S., Hopkins P. F., Cox T. J., Robertson B. E., Hernquist L., 2008, @doi [ ] 10.1111/j.1365-2966.2008.13805.x , https://ui.adsabs.harvard.edu/abs/2008MNRAS.391..481S 391, 481

    2008

  59. [72]

    Springel V., 2000, @doi [ ] 10.1046/j.1365-8711.2000.03187.x , http://adsabs.harvard.edu/abs/2000MNRAS.312..859S 312, 859

    2000

  60. [73]

    Springel V., White S. D. M., 1999, @doi [ ] 10.1046/j.1365-8711.1999.02613.x , http://adsabs.harvard.edu/abs/1999MNRAS.307..162S 307, 162

    1999

  61. [74]

    E., B \"o hringer H., Schuecker P., Nord B., 2006, @doi [ ] 10.1086/506248 , http://adsabs.harvard.edu/abs/2006ApJ...648..956S 648, 956

    Stanek R., Evrard A. E., B \"o hringer H., Schuecker P., Nord B., 2006, @doi [ ] 10.1086/506248 , http://adsabs.harvard.edu/abs/2006ApJ...648..956S 648, 956

    2006 doi

  62. [75]

    Stern J., Fielding D., Faucher-Gigu \`e re C.-A., Quataert E., 2019, @doi [ ] 10.1093/mnras/stz1859 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.2549S 488, 2549

    2019 doi

  63. [76]

    W., Moskalenko I

    Strong A. W., Moskalenko I. V., 1998, @doi [ ] 10.1086/306470 , http://adsabs.harvard.edu/abs/1998ApJ...509..212S 509, 212

    1998 doi

  64. [77]

    W., Porter T

    Strong A. W., Porter T. A., Digel S. W., J \'o hannesson G., Martin P., Moskalenko I. V., Murphy E. J., Orlando E., 2010, @doi [ ] 10.1088/2041-8205/722/1/L58 , http://adsabs.harvard.edu/abs/2010ApJ...722L..58S 722, L58

    2010 doi

  65. [78]

    E., Miller E

    Su Y., White III R. E., Miller E. D., 2013, @doi [ ] 10.1088/0004-637X/775/2/89 , http://adsabs.harvard.edu/abs/2013ApJ...775...89S 775, 89

    2013 doi

  66. [79]

    Su Y., Buote D., Gastaldello F., Brighenti F., 2015, @doi [ ] 10.1088/0004-637X/805/2/104 , http://adsabs.harvard.edu/abs/2015ApJ...805..104S 805, 104

    2015 doi

  67. [80]

    F., Hayward C

    Su K.-Y., Hopkins P. F., Hayward C. C., Faucher-Gigu \`e re C.-A., Kere s D., Ma X., Robles V. H., 2017, @doi [ ] 10.1093/mnras/stx1463 , http://adsabs.harvard.edu/abs/2017MNRAS.471..144S 471, 144

    2017 doi

  68. [81]

    Su K.-Y., et al., 2019, @doi [ ] 10.1093/mnras/stz1494 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.487.4393S 487, 4393

    2019 doi

  69. [82]

    Su K.-Y., et al., 2020, @doi [ ] 10.1093/mnras/stz3011 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.491.1190S 491, 1190

    2020 doi

  70. [83]

    Su K.-Y., et al., 2021, @doi [ ] 10.1093/mnras/stab2021 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.507..175S 507, 175

    2021 doi

  71. [84]

    Su K.-Y., et al., 2024, @doi [ ] 10.1093/mnras/stae1629 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.532.2724S 532, 2724

    2024 doi

  72. [85]

    Tamura T., et al., 2001, @doi [ ] 10.1051/0004-6361:20000038 , http://adsabs.harvard.edu/abs/2001A

    2001 doi

  73. [86]

    Torrey P., et al., 2020, @doi [ ] 10.1093/mnras/staa2222 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.497.5292T 497, 5292

    2020 doi

  74. [87]

    V., Porter T

    Trotta R., J \'o hannesson G., Moskalenko I. V., Porter T. A., Ruiz de Austri R., Strong A. W., 2011, @doi [ ] 10.1088/0004-637X/729/2/106 , http://adsabs.harvard.edu/abs/2011ApJ...729..106T 729, 106

    2011 doi

  75. [89]

    C., Reynolds C

    Vernaleo J. C., Reynolds C. S., 2006, @doi [ ] 10.1086/504029 , http://adsabs.harvard.edu/abs/2006ApJ...645...83V 645, 83

    2006 doi

  76. [90]

    M., Donahue M., Bryan G

    Voit G. M., Donahue M., Bryan G. L., McDonald M., 2015, @doi [ ] 10.1038/nature14167 , https://ui.adsabs.harvard.edu/abs/2015Natur.519..203V 519, 203

    2015 doi

  77. [91]

    Weinberger R., et al., 2018, @doi [ ] 10.1093/mnras/sty1733 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.479.4056W 479, 4056

    2018 doi

  78. [92]

    Werner N., et al., 2013, @doi [ ] 10.1088/0004-637X/767/2/153 , http://adsabs.harvard.edu/abs/2013ApJ...767..153W 767, 153

    2013 doi

  79. [93]

    R., Tinker J

    Wetzel A. R., Tinker J. L., Conroy C., 2012, @doi [ ] 10.1111/j.1365-2966.2012.21188.x , http://adsabs.harvard.edu/abs/2012MNRAS.424..232W 424, 232

    2012

  80. [94]

    G., 2019, @doi [ ] 10.1093/mnras/stz1705 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488..280W 488, 280

    Wiener J., Zweibel E. G., 2019, @doi [ ] 10.1093/mnras/stz1705 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488..280W 488, 280

    2019 doi

  81. [95]

    P., Li Y.-P., Ciotti L., 2018, @doi [ ] 10.3847/1538-4357/aad37e , https://ui.adsabs.harvard.edu/abs/2018ApJ...864....6Y 864, 6

    Yoon D., Yuan F., Gan Z.-M., Ostriker J. P., Li Y.-P., Ciotti L., 2018, @doi [ ] 10.3847/1538-4357/aad37e , https://ui.adsabs.harvard.edu/abs/2018ApJ...864....6Y 864, 6

    2018 doi

  82. [96]

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

    ENTRY address author booktitle chapter edition editor howpublished institution 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 a...

  83. [97]

    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 gl...

  84. [98]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.stat...

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.