Pith. sign in

REVIEW 3 major objections 6 minor 118 references

Jellyfish galaxies with the IllustrisTNG simulations -- Supermassive black hole activity in dense environments with ram-pressure stripped satellites

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read According to the simulations studied here, ram pressure from the cluster medium compresses gas in jellyfish galaxies and can feed their central supermassive black holes, making them more likely to shine as AGN.

desk verdict Robust AGN excess in TNG jellyfish, but the ram-pressure-feeding mechanism is underdetermined without a matched control. read the letter →

arxiv 2506.05474 v2 pith:KU43T64F submitted 2025-06-05 astro-ph.GA

Shalini Kurinchi-Vendhan , Eric Rohr , Annalisa Pillepich , Elad Zinger , Mohammadreza Ayromlou , Gandhali D. Joshi This is my paper
classification astro-ph.GA
keywords jellyfishgalaxiesram-pressurestrippingsupermassiveblackholesactivegalacticnucleigalaxyclusterssatellitecosmologicalsimulationsholeaccretion
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 asks whether the same ram pressure that strips gas tails off jellyfish galaxies — cluster satellites moving through hot intracluster gas — can also feed their central supermassive black holes. Using two large cosmological simulations, the authors compare jellyfish galaxies with ordinary central galaxies and with other satellites of equal stellar mass. They find that jellyfish are more likely to host luminous active galactic nuclei (AGN), with an AGN fraction of 50–70 percent above $10^{44}$ erg s$^{-1}$ that rises with stellar mass. Following individual galaxies through their orbits, they see ram pressure, central gas density, and black-hole luminosity peaking near the first close passage to the cluster centre, and argue that ram pressure compresses central gas, lowers its sound speed, and thereby boosts Bondi accretion. The result matters because it suggests the environment, not just internal secular processes, can drive nuclear activity in a large fraction of stripped satellites.

What carries the argument

The load-bearing object is the Bondi-Hoyle-Lyttleton accretion prescription used in the simulation's black-hole model, $\dot{M}_{\rm Bondi} \propto M_{\rm SMBH}^2 \rho / c_s^3$, which converts the gas density $\rho$ and sound speed $c_s$ within a kiloparsec of the black hole directly into an accretion rate and hence into an AGN luminosity. This is paired with the classic ram-pressure formula $P_{\rm ram} = \rho_{\rm LBE}\, v_{\rm rel}^2$, computed from the gas in the local background environment around each satellite. Together they carry the causal argument: as a jellyfish approaches the cluster centre, rising $P_{\rm ram}$ is associated with higher central $\rho$ and lower central $c_s$, and the Bondi formula turns those changes into enhanced accretion and luminosity. The analysis also relies on the simulation's thermal-versus-kinetic AGN feedback threshold, because jellyfish almost always sit in the thermal, high-accretion mode, whereas gas-poor satellites fall into the kinetic, low-accretion mode.

What would settle it

Re-run the identical simulation suite with ram pressure artificially disabled while keeping orbital dynamics and gas content identical; if jellyfish still show the same central-gas density and AGN luminosity peaks near pericenter, the claim that ram pressure feeds SMBHs is falsified.

Watch

Extended reading notes

Core claim

According to the cosmological simulations analysed in this paper, jellyfish galaxies have more active supermassive black holes than centrals and other satellites of the same stellar mass: their SMBHs have higher accretion rates and luminosities, with nearly all jellyfish above $L_{\rm AGN} \gtrsim 10^{42}\ \mathrm{erg\,s^{-1}}$ and 50–70 percent above $10^{44}\ \mathrm{erg\,s^{-1}}$. The AGN fraction rises with stellar mass and is highest above $M_{\rm stellar} \gtrsim 10^{10}\,M_\odot$, where it declines for other galaxy populations but keeps rising for jellyfish. The paper attributes this to the environment: ram pressure from the cluster medium compresses gas within a kiloparsec of the nucleus, raising its density and lowering its sound speed, which in the Bondi accretion prescription raises the accretion rate and the AGN luminosity. The direct chain — ram-pressure peak, then central gas density and AGN luminosity peak near the first pericentric passage — is clearly visible in about half of the jellyfish, with another 15 percent showing delayed AGN enhancement; the remaining third shows enhanced central gas without an AGN response.

Load-bearing premise

The causal link assumes that the observed alignment between ram-pressure peaks, central gas density rises, and AGN luminosity peaks near the first pericentric passage is actually caused by the ram pressure, rather than by the purely orbital phase or by the fact that jellyfish are selected because they are gas-rich.

Editorial extensions

If this is right

  • Jellyfish galaxies are, on average, more likely than other galaxies of the same mass to host a luminous AGN, with 50–70 percent above $10^{44}$ erg s$^{-1}$ and a fraction that continues to rise with stellar mass even as other populations decline.
  • In about two-thirds of jellyfish, the first close passage to the cluster centre is accompanied by enhanced central gas density and a direct or delayed increase in AGN luminosity, indicating that ram pressure can act as an environmental trigger of black-hole accretion.
  • The one-third of jellyfish with compressed central gas but no AGN enhancement shows that ram-pressure feeding is not guaranteed; some additional condition — stellar mass, orbital configuration, or feedback state — determines whether the nuclear response occurs.
  • Jellyfish almost all remain in the high-accretion thermal feedback mode, while gas-poor satellites fall into the low-accretion kinetic mode; this reinforces the picture that the availability of central gas is the key link between environment and observable nuclear activity.
  • The same stripping that temporarily feeds the black hole ultimately removes the gas, so the environmental AGN boost is transient and coexists with the long-term quenching of satellite galaxies.

Reading between the lines

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

  • The quantitative AGN enhancement in the simulations is tied to the Bondi formula's steep dependence on sound speed ($c_s^{-3}$); a more realistic accretion model that accounts for angular momentum and radiation pressure could substantially change the magnitude or even the sign of the environmental effect, so the claim should be re-tested with higher-resolution runs that resolve the accretion regio
  • Because jellyfish are selected to be gas-rich, part of the reported AGN excess may be a selection effect rather than a direct consequence of ram pressure; a clean experiment would compare gas-rich satellites with identical gas fractions and orbital histories, with and without environmental stripping, to isolate the causal contribution.
  • The same feeding mechanism would plausibly apply in other dense gas environments, such as galaxy groups, protoclusters, or the early universe; a search for enhanced AGN incidence in compact groups or high-redshift protoclusters would be a direct observational test of the general mechanism.
  • The presence of about one-third of jellyfish with compressed central gas but no AGN response suggests that either the inflow's angular momentum or the AGN duty cycle controls whether environmental feeding actually lights up the black hole; this could be investigated by tracking the angular momentum and radial velocity structure of the gas within the Bondi radius in the simulations.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. This manuscript uses the IllustrisTNG simulations (TNG50 and TNG100) to study supermassive black hole (SMBH) activity in z=0 jellyfish galaxies identified by the Cosmological Jellyfish Zooniverse project, comparing them to central galaxies, all satellites, and gas-rich inspected satellites that are not classified as jellyfish. The authors report that jellyfish have higher AGN fractions than centrals and satellites of the same stellar mass, especially at M_stellar > 10^10 Msun, and that jellyfish have higher central gas densities and lower central sound speeds than other gas-rich satellites. From individual evolutionary tracks and a stacked time evolution, they claim that ram pressure peaks near first pericenter together with central gas density and sometimes AGN luminosity, and they interpret this as evidence that ram pressure compresses central gas and feeds SMBH accretion. The paper includes robustness tests with different AGN luminosity thresholds, a treatment of missing SMBHs, and a discussion of AGN feedback modes.

Significance. If the causal claim holds, this would be one of the first population-scale simulation results supporting the idea that ram pressure can directly fuel SMBHs in cluster satellites, providing a theoretical counterpart to some observational claims (e.g., GASP). The population-level finding that jellyfish have elevated AGN fractions is robust and is a valuable, falsifiable TNG prediction; the luminosity-cut tests and the comparison to gas-rich inspected satellites strengthen this part of the paper. The mechanistic claim (ram pressure compresses central gas and boosts accretion) is the novel and most important contribution, but it is currently the least supported part of the analysis.

major comments (3)
  1. [Section 4.1 and Figure 5] The stacked time evolution that forms the main evidence for ram-pressure feeding is shown only for jellyfish galaxies; there is no control sample of gas-rich inspected non-jellyfish satellites matched in stellar mass, gas fraction at infall and at z=0, and host halo mass. Because jellyfish are by construction gas-rich and are visually identified at a time when they show a stripped tail, the temporal alignment of P_ram, n_gas, and L_AGN near first pericenter could be a generic orbital-phase effect or a consequence of gas-rich selection rather than a causal effect of ram pressure on the SMBH. The manuscript itself states that the correlation between ram pressure and AGN luminosity is 'somewhat weak' (Figure 5 caption) and that only about 50% of jellyfish fall in Case 1. A matched control stack is needed to distinguish the proposed mechanism from the alternative that any gas-rich satellite near pericenter shows the same behavior.
  2. [Section 4.1, Cases 1-3] The classification of jellyfish into Cases 1, 2, and 3 is based on visual inspection of individual evolutionary tracks, with no quantitative criterion or blinded procedure described. The fractions (50%, 15%, 35%) are used to argue that ram pressure feeds SMBHs in a majority of jellyfish, but the subjectivity of this classification makes the quantitative claim difficult to evaluate. A reproducible measure, such as a cross-correlation or peak-triggered average of P_ram and L_AGN within a defined window around first pericenter, with a pre-specified threshold, would make the case fractions verifiable and would also allow the stacked result in Figure 5 to be interpreted more cleanly.
  3. [Sections 3.1, 5, and Appendix C] The paper acknowledges that jellyfish must be gas-rich and in the thermal AGN feedback mode to be classified as jellyfish (e.g., 'jellyfish are necessarily biased towards accretion and feedback in the thermal mode to even have the chance to retain gas and to appear ram-pressure stripped'). Since the Bondi accretion rate in Equation (1) scales with gas density and inversely with the cube of the sound speed, the higher n_gas and lower c_s of jellyfish could be a direct consequence of this selection rather than of ram-pressure compression during pericenter passage. The causal interpretation requires controlling for the pre-infall central gas density or gas fraction, or demonstrating that the pericentric increase in n_gas exceeds what is expected from the gas-rich selection alone. As written, the evidence does not exclude the simpler explanation that jellyfish are selected to have high central gas density, which directly raises their Bondi accretion rates.
minor comments (6)
  1. [Section 2.1.2] Equation (2) and the surrounding text refer to the 'Thompson' cross section; the standard spelling is 'Thomson' cross section.
  2. [Section 2.1.1] The text says 'utilizes the arepocode'; this should be 'Arepo code' with a capital letter for the code name.
  3. [Section 2.4.2 and Appendix A] The robustness of the results to the 'fudge' SMBH assignment for galaxies with missing black holes is not tested; excluding the 1-3% of jellyfish with lost SMBHs would confirm that the high AGN fractions are not driven by the assigned Gaussian values.
  4. [Figure 5 and Section 4.1] The text says 'we test, but we do not show, whether the peak in the AGN luminosity would be stronger in a high stellar mass bin'; this test could usefully be moved to an appendix since the mass dependence is a central part of the population-level result.
  5. [Section 4.1 and Figure 4] There are several instances where 'effect' is used where 'affect' is intended, for example 'ram pressure can indirectly effect SMBH activity' and 'could inhibit or enhance the impact of ram pressure on the SMBH'.
  6. [Figure 6 caption] The caption describes the inner r=2 ckpc as a 'dashed white circle' and r=1 ckpc as 'solid dashed circle'; the latter should likely read 'solid white circle'.

Circularity Check

1 steps flagged · score 3.0 of 10

Minor circularity in the Case-1 sub-stack; population-level AGN result is self-contained.

  1. self definitional [Section 4.1, Figure 5 caption and following paragraph]
    "we categorize them as 'Case 1' (Direct Effect), 'Case 2' (Delayed Effect), and 'Case 3' (No Effect) and quantify them in the above bar plot by visual inspection of the evolutionary tracks of all individual galaxies (as those of Figure 4). ... The time correlation between ram pressure and AGN luminosity is somewhat weak. However, when we stack across only jellyfish galaxies of the first case (green circles), a strong and timely increase of AGN luminosity manifests with an increase of ram pressure and central gas density."

    Case 1 is defined, by visual inspection, as galaxies whose P_ram peak directly corresponds to peaks in n_gas and L_AGN at first pericentric passage. Restacking exactly those galaxies and 'recovering' a strong P_ram-n_gas-L_AGN coincidence is therefore a restatement of the selection criterion, not an independent confirmation. The only unselected stacked diagnostic, the full-sample stack, shows only a 'somewhat weak' correlation, so this self-selected sub-stack carries part of the evidentiary weight for the causal claim. The 65 per cent figure (Case 1 + Case 2) similarly comes from the same visual classification and is not an independent test of ram-pressure feeding.

full rationale

The paper's headline population result—jellyfish have elevated AGN fractions—is not circular: the Zooniverse jellyfish classification uses gas-density morphology with no knowledge of SMBH accretion, and the comparison against inspected non-jellyfish gas-rich satellites controls for the f_gas>0.01 selection. The TNG model parameters were calibrated to global galaxy properties, not to jellyfish AGN fractions, and the GASP observations provide an external benchmark. The Bondi relation (Eq. 1) means SMBH luminosity responds to central gas density and sound speed by construction, but the finding that jellyfish differ from gas-matched satellites in n_gas and c_s is emergent and independent. The one circular element is the Case-1 stacked 'recovery': objects are placed in Case 1 precisely because their P_ram, n_gas, and L_AGN peaks coincide, so restacking them and reporting a coincident peak is a restatement of the classification, not a test. This step bolsters the causal interpretation but is not the sole basis for the AGN fraction comparison, so the overall circularity is minor. Self-citations to Zinger et al. 2024, Rohr et al. 2023, and Weinberger et al. 2017 provide data and model definitions rather than imported uniqueness theorems; none is load-bearing in a way that forces the conclusion.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim rests on the calibrated TNG subgrid model, the citizen-science jellyfish catalog, the LBE ram pressure measure, and the Bondi-based interpretation of central gas properties. No new physical entities are introduced. The main free parameters are selection thresholds and the ad hoc fudge SMBH assignment.

free parameters (4)
  • AGN luminosity threshold = 10^44 erg/s
    Fiducial cut defining AGN hosts; authors test 10^41 to 10^44 in Appendix B and find the jellyfish enhancement is clearest at the highest threshold.
  • Jellyfish score threshold = 0.8
    Visual classification threshold from Zinger et al. (2024); defines the jellyfish sample. Varying it would change sample size and possibly the derived fractions.
  • Gas fraction selection threshold = f_gas >= 0.01
    Defines inspected satellites; jellyfish are selected from gas-rich systems, which directly affects Bondi accretion rates.
  • Fudge SMBH distribution for missing black holes = Gaussian matched to similar-mass galaxies
    8.6 percent of galaxies lack an SMBH; the authors assign random mass and accretion rates from a Gaussian, an ad hoc choice to preserve sample completeness.
assumptions (5)
  • domain assumption The TNG subgrid model for SMBH seeding, Bondi accretion, and thermal/kinetic feedback (Weinberger et al. 2017; Pillepich et al. 2018a) provides a valid representation of AGN activity in galaxies.
    All AGN luminosities and accretion rates in this paper come from Equations 1-4 of this model, not from direct simulation of the black hole environment.
  • domain assumption The Cosmological Jellyfish Zooniverse visual classifications identify physically ram-pressure-stripped galaxies.
    The jellyfish sample is defined by a score of at least 0.8 from citizen scientists looking at random projections; the authors note about one third of true jellyfish are missed due to viewing angle and that contamination may be around 33 percent.
  • domain assumption The local background environment (LBE) ram pressure measurement (Ayromlou et al. 2019) captures the actual stripping force on the galaxy.
    P_ram is computed as rho_LBE times v_rel^2 within an adaptive shell, a standard approximation but not a direct measurement of the hydrodynamic force on the galaxy.
  • domain assumption Gas density and sound speed within 1 ckpc of the SMBH determine the Bondi accretion rate, so simulated central gas properties are a valid intermediary between ram pressure and AGN luminosity.
    Equation 1 has accretion proportional to rho divided by c_s^3; the paper uses this relation to interpret higher n_gas and lower c_s in jellyfish as the cause of higher L_AGN.
  • ad hoc to paper Temporal alignment of P_ram, n_gas, and L_AGN peaks near pericentric passage indicates causation by ram pressure.
    This is the interpretive step in Section 4.1 and Figures 4 and 5; it is not independently established and is acknowledged to be weak in the stacked sample.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Jellyfish galaxies with the IllustrisTNG simulations -- Supermassive black hole activity in dense environments with ram-pressure stripped satellites." pith.science (2026). https://pith.science/paper/KU43T64F

@misc{pith2026250605474,
  author       = {Pith},
  title        = {Pith review of: Jellyfish galaxies with the IllustrisTNG simulations -- Supermassive black hole activity in dense environments with ram-pressure stripped satellites},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KU43T64F}},
  note         = {Machine review of arXiv:2506.05474}
}
abstract

Jellyfish galaxies are extreme examples of how galaxies can transform due to dense environmental effects. These satellite galaxies suffer from ram-pressure stripping, leading to the formation of their distinctive gaseous tails. Some recent observational studies find that jellyfish galaxies are more likely to host active galactic nuclei (AGN) compared to central galaxies of the same mass, suggesting a link between ram pressure and supermassive black hole (SMBH) accretion. We use the IllustrisTNG cosmological-magnetohydrodynamical simulations, namely TNG50 and TNG100, to explore the presence of AGN in jellyfish galaxies with $M_{\rm{stellar}}\simeq10^{9.5-10.8}\,\rm{M}_\odot$ at redshift $z=0$ from the Zooniverse "Cosmological Jellyfish" citizen-science project. Compared to central galaxies, jellyfish are more likely to host an AGN ($L_{\rm AGN}\geq10^{44}\,\mathrm{erg\,s^{-1}}$) particularly at high stellar masses ($M_{\rm stellar}\gtrsim10^{10}\,\mathrm{M_\odot}$). Jellyfish are also more likely to host an AGN than satellites of the same mass, largely because many satellite galaxies are gas-poor and therefore have lower SMBH accretion rates. Compared to non-jellyfish satellites with similar gas content, jellyfish typically undergo stronger ram pressure and have higher central gas densities along with lower central gas sound speeds, although these effects are smaller at lower stellar masses ($M_{\rm stellar}\lesssim10^{10}\,\mathrm{M_\odot}$). Together with case studies of individual galaxies, our population analysis indicates that ram pressure can play a key role in fuelling AGN activity in a large fraction of jellyfish, where gas compression can lead to intense episodes of AGN feedback and star formation. Thus, it is essential to consider both environmental and secular processes for a more complete picture of satellite galaxy evolution.

Figures

Figures reproduced from arXiv: 2506.05474 by the authors.

Figure 1
Figure 1. Infographic of IllustrisTNG galaxies and cosmological jellyfish. In a selection of all galaxies from TNG50 and TNG100 with 𝑀stellar ≥ 109.5 M⊙ (gray), we consider satellites (blue) in hosts of total mass 𝑀200c ≥ 1011.5 M⊙ that may experience environmental effects compared to centrals (purple). The “Cosmological Jellyfish” Zooniverse project (Zinger et al. 2024) inspected a subset of gaseous satellites (pink; 𝑓gas = … view at source ↗
Figure 2
Figure 2. Distribution of SMBH properties for centrals (purple), satellites (blue), and jellyfish (yellow) galaxies in TNG50 and TNG100 at 𝑧 = 0 with 𝑀stellar ≥ 109.5 M⊙. Operational definitions of these samples and their relative size are given in [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. The AGN fraction across various cosmological environments. Here we quantify the AGN fraction in stellar mass bins, i.e. the fraction of galaxies with 𝐿AGN ≥ 1044 erg s−1 , of jellyfish (yellow), compared to inspected satellites (pink), all satellites (blue), centrals (purple), and the whole population of galaxies (black) in TNG50 and TNG100 at 𝑧 = 0 with 𝑀stellar ≥ 109.5 M⊙. In the main panel, each bin contains at l… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Ram-pressure feeding of SMBH activity, shown through the evolution of ram pressure, central gas densities (innermost 1 ckpc), and AGN luminosities in three individual jellyfish galaxies from TNG50, shown in the images above at 𝑧 = 0. We also note whether the galaxy is …
Figure 5
Figure 5. Figure 5: Ram-pressure feeding of SMBH activity,shown through the evolu￾tion of ram pressure, central gas densities, and AGN luminosities in hundreds of jellyfish galaxies from TNG50 and TNG100 at 𝑧 = 0. The cumulative i.e. “stacked” evolution represents a combination of three e…
Figure 6
Figure 6. Figure 6: Possible mechanisms of ram-pressure feeding. Here, we show the 2-D gas density projection of the example jellyfish galaxy in “Case 1” of [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Distribution of gas properties in and around jellyfish, gaseous satellites, and central galaxies. We show the distributions of properties of the internal and the surrounding gas of the jellyfish (yellow) and inspected satellites which are not jellyfish (pink), and cent…
Figure 8
Figure 8. Figure 8: Hint of ram pressure and AGN connection. Properties of the internal gas, within 1 ckpc of the SMBH, and the surrounding gas, in the local background environment, of jellyfish and non-jellyfish inspected satellites in TNG50 and TNG100 at 𝑧 = 0 with 𝑀stellar ≥ 109.5 M⊙. …
Figure 9
Figure 9. Figure 9: The special case of a satellite with ram-pressure stripped tails and an AGN-driven bubble. The image shows the 2-D gas pressure projection of a satellite galaxy from TNG50 at 𝑧 = 0, with Subhalo ID = 184935, Snapshot 99 and a stellar mass of 𝑀stellar = 1010.7 M⊙. We sh…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

118 extracted references · 7 canonical work pages

  1. [1]

    M., Smith R., Marasco A., 2023, @doi [ ] 10.3847/1538-4357/acbf4d , 948, 18

    Akerman N., Tonnesen S., Poggianti B. M., Smith R., Marasco A., 2023, @doi [ ] 10.3847/1538-4357/acbf4d , 948, 18

    doi:10.3847/1538-4357/acbf4d 2023

  2. [2]

    Amiri A., Tavasoli S., De Zotti G., 2019, @doi [ ] 10.3847/1538-4357/ab08e7 , https://ui.adsabs.harvard.edu/abs/2019ApJ...874..140A 874, 140

    doi:10.3847/1538-4357/ab08e7 2019

  3. [3]

    J., Martini P., Mulchaey J

    Arnold T. J., Martini P., Mulchaey J. S., Berti A., Jeltema T. E., 2009, @doi [ ] 10.1088/0004-637X/707/2/1691 , https://ui.adsabs.harvard.edu/abs/2009ApJ...707.1691A 707, 1691

    doi:10.1088/0004-637x/707/2/1691 2009

  4. [4]

    M., Kauffmann G., White S

    Ayromlou M., Nelson D., Yates R. M., Kauffmann G., White S. D. M., 2019, @doi [ ] 10.1093/mnras/stz1549 , 487, 4313–4331

    doi:10.1093/mnras/stz1549 2019

  5. [5]

    M., Nelson D., White S

    Ayromlou M., Kauffmann G., Yates R. M., Nelson D., White S. D. M., 2021, @doi [ ] 10.1093/mnras/stab1245 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.505..492A 505, 492

    doi:10.1093/mnras/stab1245 2021

  6. [6]

    Ayromlou M., Nelson D., Pillepich A., 2023, @doi [ ] 10.1093/mnras/stad2046 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.524.5391A 524, 5391

    doi:10.1093/mnras/stad2046 2023

  7. [7]

    A., Phillips M

    Baldwin J. A., Phillips M. M., Terlevich R., 1981, @doi [ ] 10.1086/130766 , https://ui.adsabs.harvard.edu/abs/1981PASP...93....5B 93, 5

    doi:10.1086/130766 1981

  8. [8]

    L., Navarro J

    Balogh M. L., Navarro J. F., Morris S. L., 2000, @doi [ ] 10.1086/309323 , https://ui.adsabs.harvard.edu/abs/2000ApJ...540..113B 540, 113

    doi:10.1086/309323 2000

Show all 118 references
  1. [10]

    Bluck A. F. L., Piotrowska J. M., Maiolino R., 2023, @doi [ ] 10.3847/1538-4357/acac7c , https://ui.adsabs.harvard.edu/abs/2023ApJ...944..108B 944, 108

    2023 doi

  2. [11]

    Boselli A., et al., 2016, @doi [ ] 10.1051/0004-6361/201527795 , 587, A68

    2016 doi

  3. [12]

    G., Benson A

    Bower R. G., Benson A. J., Malbon R., Helly J. C., Frenk C. S., Baugh C. M., Cole S., Lacey C. G., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10519.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.370..645B 370, 645

    2006

  4. [14]

    Ciotti L., D'Ercole A., Pellegrini S., Renzini A., 1991, @doi [ ] 10.1086/170289 , https://ui.adsabs.harvard.edu/abs/1991ApJ...376..380C 376, 380

    1991 doi

  5. [15]

    C., O’Neil K., Sakai S., 2006, @doi [ ] 10.1051/0004-6361:20064873 , 453, 847–861

    Cortese L., Gavazzi G., Boselli A., Franzetti P., Kennicutt R. C., O’Neil K., Sakai S., 2006, @doi [ ] 10.1051/0004-6361:20064873 , 453, 847–861

    2006 doi

  6. [16]

    A., et al., 2015, @doi [ ] 10.1093/mnras/stv725 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.450.1937C 450, 1937

    Crain R. A., et al., 2015, @doi [ ] 10.1093/mnras/stv725 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.450.1937C 450, 1937

    2015 doi

  7. [17]

    J., et al., 2006, @doi [ ] 10.1111/j.1365-2966.2005.09675.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.365...11C 365, 11

    Croton D. J., et al., 2006, @doi [ ] 10.1111/j.1365-2966.2005.09675.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.365...11C 365, 11

    2006

  8. [18]

    S., White S

    Davis M., Efstathiou G., Frenk C. S., White S. D. M., 1985, @doi [ ] 10.1086/163168 , https://ui.adsabs.harvard.edu/abs/1985ApJ...292..371D 292, 371

    1985 doi

  9. [19]

    H., Appleby S., 2019, @doi [ ] 10.1093/mnras/stz937 , 486, 2827–2849

    Davé R., Anglés-Alcázar D., Narayanan D., Li Q., Rafieferantsoa M. H., Appleby S., 2019, @doi [ ] 10.1093/mnras/stz937 , 486, 2827–2849

    2019 doi

  10. [20]

    World Scientific, pp 149--225

    Di Matteo T., Angles-Alcazar D., Shankar F., 2023, Massive Black Holes in Galactic Nuclei: Theory. World Scientific, pp 149--225

    2023

  11. [21]

    Donnari M., et al., 2019, @doi [ ] 10.1093/mnras/stz712 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.485.4817D 485, 4817

    2019 doi

  12. [22]

    Donnari M., et al., 2021a, @doi [ ] 10.1093/mnras/staa3006 , 500, 4004

    doi

  13. [23]

    Donnari M., Pillepich A., Nelson D., Marinacci F., Vogelsberger M., Hernquist L., 2021b, @doi [ ] 10.1093/mnras/stab1950 , 506, 4760

    doi

  14. [24]

    B., Shectman S

    Dressler A., Thompson I. B., Shectman S. A., 1985, @doi [ ] 10.1086/162813 , https://ui.adsabs.harvard.edu/abs/1985ApJ...288..481D 288, 481

    1985 doi

  15. [25]

    N., Edge A

    Ebeling H., Stephenson L. N., Edge A. C., 2014, @doi [ ] 10.1088/2041-8205/781/2/L40 , https://ui.adsabs.harvard.edu/abs/2014ApJ...781L..40E 781, L40

    2014 doi

  16. [26]

    Fabian A., 2012, @doi [ ] 10.1146/annurev-astro-081811-125521 , 50, 455–489

    2012 doi

  17. [27]

    Fumagalli M., Fossati M., Hau G. K. T., Gavazzi G., Bower R., Sun M., Boselli A., 2014, @doi [ ] 10.1093/mnras/stu2092 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.445.4335F 445, 4335

    2014 doi

  18. [28]

    T., Peter A

    Garling C. T., Peter A. H. G., Spekkens K., Sand D. J., Hargis J., Crnojevi \'c D., Carlin J. L., 2024, @doi [ ] 10.1093/mnras/stae014 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528..365G 528, 365

    2024 doi

  19. [29]

    M., Carrasco L., 2001, @doi [ ] 10.1086/338389 , https://ui.adsabs.harvard.edu/abs/2001ApJ...563L..23G 563, L23

    Gavazzi G., Boselli A., Mayer L., Iglesias-Paramo J., V \' lchez J. M., Carrasco L., 2001, @doi [ ] 10.1086/338389 , https://ui.adsabs.harvard.edu/abs/2001ApJ...563L..23G 563, L23

    2001 doi

  20. [30]

    L., Boselli A., Yoshida M., 2018, @doi [ ] 10.1051/0004-6361/201833427 , 618, A130

    Gavazzi G., Consolandi G., Gutierrez M. L., Boselli A., Yoshida M., 2018, @doi [ ] 10.1051/0004-6361/201833427 , 618, A130

    2018 doi

  21. [31]

    Genel S., et al., 2014, @doi [ ] 10.1093/mnras/stu1654 , 445, 175–200

    2014 doi

  22. [32]

    George K., et al., 2019, @doi [ ] 10.1093/mnras/stz1443 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.487.3102G 487, 3102

    2019 doi

  23. [33]

    George K., et al., 2024, @doi [ ] 10.1051/0004-6361/202450302 , https://ui.adsabs.harvard.edu/abs/2024A&A...690A.337G 690, A337

    2024 doi

  24. [34]

    Y., 2003, @doi [ ] 10.1086/344636 , https://ui.adsabs.harvard.edu/abs/2003ApJ...582..141G 582, 141

    Gnedin O. Y., 2003, @doi [ ] 10.1086/344636 , https://ui.adsabs.harvard.edu/abs/2003ApJ...582..141G 582, 141

    2003 doi

  25. [35]

    D., Rohr E., Zinger E., Pillepich A., 2023, @doi [ ] 10.1093/mnras/stad2551 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.525.3551G 525, 3551

    G \"o ller J., Joshi G. D., Rohr E., Zinger E., Pillepich A., 2023, @doi [ ] 10.1093/mnras/stad2551 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.525.3551G 525, 3551

    2023 doi

  26. [36]

    H., Bluck A

    Goubert P. H., Bluck A. F. L., Piotrowska J. M., Maiolino R., 2024, @doi [ ] 10.1093/mnras/stae269 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528.4891G 528, 4891

    2024 doi

  27. [37]

    E., Gott III J

    Gunn J. E., Gott III J. R., 1972, @doi [ ] 10.1086/151605 , https://ui.adsabs.harvard.edu/abs/1972ApJ...176....1G 176, 1

    1972 doi

  28. [38]

    Habouzit M., et al., 2019, @doi [ ] 10.1093/mnras/stz102 , 484, 4413–4443

    2019 doi

  29. [39]

    R., et al., 2020, @doi [Nature] 10.1038/s41586-020-2649-2 , 585, 357

    Harris C. R., et al., 2020, @doi [Nature] 10.1038/s41586-020-2649-2 , 585, 357

    2020 doi

  30. [40]

    D., 2007, @doi [Computing in Science & Engineering] 10.1109/MCSE.2007.55 , 9, 90

    Hunter J. D., 2007, @doi [Computing in Science & Engineering] 10.1109/MCSE.2007.55 , 9, 90

    2007 doi

  31. [41]

    J \'a chym P., Combes F., Cortese L., Sun M., Kenney J. D. P., 2014, @doi [ ] 10.1088/0004-637X/792/1/11 , https://ui.adsabs.harvard.edu/abs/2014ApJ...792...11J 792, 11

    2014 doi

  32. [42]

    D., Pillepich A., Nelson D., Marinacci F., Springel V., Rodriguez-Gomez V., Vogelsberger M., Hernquist L., 2020, @doi [ ] 10.1093/mnras/staa1668 , 496, 2673

    Joshi G. D., Pillepich A., Nelson D., Marinacci F., Springel V., Rodriguez-Gomez V., Vogelsberger M., Hernquist L., 2020, @doi [ ] 10.1093/mnras/staa1668 , 496, 2673

    2020 doi

  33. [43]

    D., Pillepich A., Nelson D., Zinger E., Marinacci F., Springel V., Vogelsberger M., Hernquist L., 2021, @doi [ ] 10.1093/mnras/stab2573 , 508, 1652

    Joshi G. D., Pillepich A., Nelson D., Zinger E., Marinacci F., Springel V., Vogelsberger M., Hernquist L., 2021, @doi [ ] 10.1093/mnras/stab2573 , 508, 1652

    2021 doi

  34. [44]

    Jáchym P., et al., 2017, @doi [ ] 10.3847/1538-4357/aa6af5 , 839, 114

    2017 doi

  35. [45]

    Kauffmann G., White S. D. M., Heckman T. M., M \'e nard B., Brinchmann J., Charlot S., Tremonti C., Brinkmann J., 2004, @doi [ ] 10.1111/j.1365-2966.2004.08117.x , https://ui.adsabs.harvard.edu/abs/2004MNRAS.353..713K 353, 713

    2004

  36. [46]

    Kenney J. D. P., van Gorkom J. H., Vollmer B., 2004, @doi [ ] 10.1086/420805 , 127, 3361–3374

    2004 doi

  37. [47]

    C., 2013, @doi [ ] 10.1146/annurev-astro-082708-101811 , https://ui.adsabs.harvard.edu/abs/2013ARA&A..51..511K 51, 511

    Kormendy J., Ho L. C., 2013, @doi [ ] 10.1146/annurev-astro-082708-101811 , https://ui.adsabs.harvard.edu/abs/2013ARA&A..51..511K 51, 511

    2013 doi

  38. [48]

    Koulouridis E., Gkini A., Drigga E., 2024, @doi [ ] 10.1051/0004-6361/202348212 , https://ui.adsabs.harvard.edu/abs/2024A&A...684A.111K 684, A111

    2024 doi

  39. [49]

    arXiv:2503.01969

    Lehle K., Nelson D., Pillepich A., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2503.01969 , https://ui.adsabs.harvard.edu/abs/2025arXiv250301969L p. arXiv:2503.01969

    2025 doi

  40. [50]

    Lopes P. A. A., Ribeiro A. L. B., Rembold S. B., 2017, @doi [ ] 10.1093/mnras/stx2046 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.472..409L 472, 409

    2017 doi

  41. [51]

    R., Nulsen P., 2006, @doi [ ] 10.1086/503350 , 644, 155–166

    Machacek M., Jones C., Forman W. R., Nulsen P., 2006, @doi [ ] 10.1086/503350 , 644, 155–166

    2006 doi

  42. [52]

    Man Z.-y., Peng Y.-j., Kong X., Guo K.-x., Zhang C.-p., Dou J., 2019, @doi [ ] 10.1093/mnras/stz1706 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488...89M 488, 89

    2019 doi

  43. [53]

    Marinacci F., et al., 2018, @doi [ ] 10.1093/mnras/sty2206 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.480.5113M 480, 5113

    2018 doi

  44. [54]

    S., Kelson D

    Martini P., Mulchaey J. S., Kelson D. D., 2007, @doi [ ] 10.1086/519158 , https://ui.adsabs.harvard.edu/abs/2007ApJ...664..761M 664, 761

    2007 doi

  45. [55]

    J., Ma C.-P., 2013, @doi [ ] 10.1088/0004-637X/764/2/184 , https://ui.adsabs.harvard.edu/abs/2013ApJ...764..184M 764, 184

    McConnell N. J., Ma C.-P., 2013, @doi [ ] 10.1088/0004-637X/764/2/184 , https://ui.adsabs.harvard.edu/abs/2013ApJ...764..184M 764, 184

    2013 doi

  46. [56]

    Merluzzi P., et al., 2024, @doi [ ] 10.1093/mnras/stae1867 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.533.1394M 533, 1394

    2024 doi

  47. [57]

    K., Perola G

    Miley G. K., Perola G. C., van der Kruit P. C., van der Laan H., 1972, @doi [ ] 10.1038/237269a0 , https://ui.adsabs.harvard.edu/abs/1972Natur.237..269M 237, 269

    1972 doi

  48. [58]

    J., Nichol R

    Miller C. J., Nichol R. C., G \'o mez P. L., Hopkins A. M., Bernardi M., 2003, @doi [ ] 10.1086/378383 , https://ui.adsabs.harvard.edu/abs/2003ApJ...597..142M 597, 142

    2003 doi

  49. [59]

    Moore B., Katz N., Lake G., Dressler A., Oemler A., 1996, @doi [ ] 10.1038/379613a0 , https://ui.adsabs.harvard.edu/abs/1996Natur.379..613M 379, 613

    1996 doi

  50. [60]

    P., et al., 2018, @doi [ ] 10.1093/mnras/sty618 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.1206N 477, 1206

    Naiman J. P., et al., 2018, @doi [ ] 10.1093/mnras/sty618 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.1206N 477, 1206

    2018 doi

  51. [62]

    Nelson D., et al., 2018a, @doi [ ] 10.1093/mnras/stx3040 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475..624N 475, 624

    doi

  52. [63]

    Nelson D., et al., 2019a, @doi [Computational Astrophysics and Cosmology] 10.1186/s40668-019-0028-x , https://ui.adsabs.harvard.edu/abs/2019ComAC...6....2N 6, 2

    doi

  53. [65]

    Nelson D., et al., 2019c, @doi [ ] 10.1093/mnras/stz2306 , 490, 3234

    doi

  54. [66]

    Pakmor R., Bauer A., Springel V., 2011, @doi [ ] 10.1111/j.1365-2966.2011.19591.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.418.1392P 418, 1392

    2011

  55. [67]

    Peluso G., et al., 2022, @doi [ ] 10.3847/1538-4357/ac4225 , 927, 130

    2022 doi

  56. [68]

    Peng Y., Maiolino R., Cochrane R., 2015, @doi [Nature] 10.1038/nature14439 , 521, 192–195

    2015 doi

  57. [69]

    Pillepich A., et al., 2018a, @doi [ ] 10.1093/mnras/stx2656 , 473, 4077

    doi

  58. [70]

    Pillepich A., et al., 2018b, @doi [ ] 10.1093/mnras/stx3112 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475..648P 475, 648

    doi

  59. [71]

    Pillepich A., et al., 2019, @doi [ ] 10.1093/mnras/stz2338 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.3196P 490, 3196

    2019 doi

  60. [72]

    M., Hernquist L., 2021, @doi [ ] 10.1093/mnras/stab2779 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.508.4667P 508, 4667

    Pillepich A., Nelson D., Truong N., Weinberger R., Martin-Navarro I., Springel V., Faber S. M., Hernquist L., 2021, @doi [ ] 10.1093/mnras/stab2779 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.508.4667P 508, 4667

    2021 doi

  61. [73]

    Pillepich A., et al., 2024, @doi [ ] 10.1093/mnras/stae2165 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.535.1721P 535, 1721

    2024 doi

  62. [74]

    M., et al., 2017a, @doi [Nature] 10.1038/nature23462 , 548, 304

    Poggianti B. M., et al., 2017a, @doi [Nature] 10.1038/nature23462 , 548, 304

    doi

  63. [75]

    M., et al., 2017b, @doi [ ] 10.3847/1538-4357/aa78ed , 844, 48

    Poggianti B. M., et al., 2017b, @doi [ ] 10.3847/1538-4357/aa78ed , 844, 48

    doi

  64. [76]

    Predehl P., et al., 2020, @doi [ ] 10.1038/s41586-020-2979-0 , https://ui.adsabs.harvard.edu/abs/2020Natur.588..227P 588, 227

    2020 doi

  65. [77]

    Prunier M., Hlavacek-Larrondo J., Pillepich A., Lehle K., Nelson D., 2025, @doi [ ] 10.1093/mnras/stae2743 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.536.3200P 536, 3200

    2025 doi

  66. [78]

    L., Moretti A., Bettoni D., Gullieuszik M., Vulcani B., Fritz J., 2019, @doi [ ] 10.1093/mnras/stz809 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.486..486R 486, 486

    Radovich M., Poggianti B., Jaff \'e Y. L., Moretti A., Bettoni D., Gullieuszik M., Vulcani B., Fritz J., 2019, @doi [ ] 10.1093/mnras/stz809 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.486..486R 486, 486

    2019 doi

  67. [79]

    C., P\' e rez-Villegas A., 2018, @doi [ ] 10.1093/mnras/sty393 , 476, 3781–3792

    Ramos-Mart\' nez M., G\' o mez G. C., P\' e rez-Villegas A., 2018, @doi [ ] 10.1093/mnras/sty393 , 476, 3781–3792

    2018 doi

  68. [80]

    Ricarte A., Tremmel M., Natarajan P., Quinn T., 2020, @doi [ Letters] 10.3847/2041-8213/ab9022 , 895, L8

    2020 doi

  69. [81]

    D., et al., 2021, @doi [ ] 10.1051/0004-6361/202140784 , 650, A111

    Roberts I. D., et al., 2021, @doi [ ] 10.1051/0004-6361/202140784 , 650, A111

    2021 doi

  70. [82]

    D., et al., 2022, @doi [ ] 10.3847/1538-4357/ac9e9f , https://ui.adsabs.harvard.edu/abs/2022ApJ...941...77R 941, 77

    Roberts I. D., et al., 2022, @doi [ ] 10.3847/1538-4357/ac9e9f , https://ui.adsabs.harvard.edu/abs/2022ApJ...941...77R 941, 77

    2022 doi

  71. [83]

    D., et al., 2023, @doi [ ] 10.1051/0004-6361/202346599 , https://ui.adsabs.harvard.edu/abs/2023A&A...675A..78R 675, A78

    Roberts I. D., et al., 2023, @doi [ ] 10.1051/0004-6361/202346599 , https://ui.adsabs.harvard.edu/abs/2023A&A...675A..78R 675, A78

    2023 doi

  72. [84]

    Rohr E., Pillepich A., Nelson D., Zinger E., Joshi G., Ayromlou M., 2023, @doi [ ] 10.1093/mnras/stad2101 , 524, 3502

    2023 doi

  73. [85]

    Rohr E., Pillepich A., Nelson D., Ayromlou M., Zinger E., 2024, @doi [ ] 10.1051/0004-6361/202348583 , https://ui.adsabs.harvard.edu/abs/2024A&A...686A..86R 686, A86

    2024 doi

  74. [86]

    V., Chies-Santos A

    Roman-Oliveira F. V., Chies-Santos A. L., Rodr \' guez del Pino B., Arag \'o n-Salamanca A., Gray M. E., Bamford S. P., 2019, @doi [ ] 10.1093/mnras/stz007 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.484..892R 484, 892

    2019 doi

  75. [87]

    N., Argudo-Fern \'a ndez M., 2013, @doi [ ] 10.1093/mnras/sts675 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.430..638S 430, 638

    Sabater J., Best P. N., Argudo-Fern \'a ndez M., 2013, @doi [ ] 10.1093/mnras/sts675 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.430..638S 430, 638

    2013 doi

  76. [88]

    Schaye J., et al., 2015, @doi [ ] 10.1093/mnras/stu2058 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.446..521S 446, 521

    2015 doi

  77. [90]

    F., Nelson D., Hernquist L., 2015, @doi [ ] 10.1093/mnras/stv1340 , 452, 575–596

    Sijacki D., Vogelsberger M., Genel S., Springel V., Torrey P., Snyder G. F., Nelson D., Hernquist L., 2015, @doi [ ] 10.1093/mnras/stv1340 , 452, 575–596

    2015 doi

  78. [91]

    R., Martini P., Zabludoff A

    Sivakoff G. R., Martini P., Zabludoff A. I., Kelson D. D., Mulchaey J. S., 2008, @doi [ ] 10.1086/589686 , https://ui.adsabs.harvard.edu/abs/2008ApJ...682..803S 682, 803

    2008 doi

  79. [93]

    Sparre M., Pfrommer C., Puchwein E., 2024, @doi [ ] 10.1093/mnras/stad3607 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.5829S 527, 5829

    2024 doi

  80. [94]

    Springel V., 2010, @doi [ ] 10.1111/j.1365-2966.2009.15715.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.401..791S 401, 791

    2010

  81. [95]

    Springel V., Hernquist L., 2003, @doi [ ] 10.1046/j.1365-8711.2003.06206.x , 339, 289–311

    2003

  82. [96]

    Springel V., White S. D. M., Tormen G., Kauffmann G., 2001, @doi [ ] 10.1046/j.1365-8711.2001.04912.x , 328, 726–750

    2001

  83. [97]

    Springel V., et al., 2018, @doi [ ] 10.1093/mnras/stx3304 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475..676S 475, 676

    2018 doi

  84. [98]

    Stevens A. R. H., et al., 2019, @doi [ ] 10.1093/mnras/sty3451 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.483.5334S 483, 5334

    2019 doi

  85. [99]

    Stevens A. R. H., et al., 2021, @doi [ ] 10.1093/mnras/staa3662 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.3158S 502, 3158

    2021 doi

  86. [100]

    Sun M., Jones C., Forman W., Nulsen P. E. J., Donahue M., Voit G. M., 2006, @doi [ ] 10.1086/500590 , https://ui.adsabs.harvard.edu/abs/2006ApJ...637L..81S 637, L81

    2006 doi

  87. [101]

    A., et al., 2020, @doi [ ] 10.1093/mnras/staa374 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.1888T 493, 1888

    Terrazas B. A., et al., 2020, @doi [ ] 10.1093/mnras/staa374 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.1888T 493, 1888

    2020 doi

  88. [102]

    arXiv:2411.05074

    Tiwari J., Sun M., Luo R., Fossati M., Chen C.-T., Tamhane P., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2411.05074 , https://ui.adsabs.harvard.edu/abs/2024arXiv241105074T p. arXiv:2411.05074

    2024 doi arXiv

  89. [103]

    L., 2009, @doi [ ] 10.1088/0004-637x/694/2/789 , 694, 789–804

    Tonnesen S., Bryan G. L., 2009, @doi [ ] 10.1088/0004-637x/694/2/789 , 694, 789–804

    2009 doi

  90. [104]

    L., 2012, @doi [ ] 10.1111/j.1365-2966.2012.20737.x , 422, 1609–1624

    Tonnesen S., Bryan G. L., 2012, @doi [ ] 10.1111/j.1365-2966.2012.20737.x , 422, 1609–1624

    2012

  91. [105]

    Torrey P., Vogelsberger M., Genel S., Sijacki D., Springel V., Hernquist L., 2014, @doi [ ] 10.1093/mnras/stt2295 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.438.1985T 438, 1985

    2014 doi

  92. [106]

    R., Pontzen A., Anderson L., Bellovary J., 2017, @doi [ ] 10.1093/mnras/stx1160 , 470, 1121–1139

    Tremmel M., Karcher M., Governato F., Volonteri M., Quinn T. R., Pontzen A., Anderson L., Bellovary J., 2017, @doi [ ] 10.1093/mnras/stx1160 , 470, 1121–1139

    2017 doi

  93. [107]

    Truong N., et al., 2020, @doi [ ] 10.1093/mnras/staa685 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.494..549T 494, 549

    2020 doi

  94. [108]

    Truong N., Pillepich A., Werner N., 2021, @doi [ ] 10.1093/mnras/staa3880 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.501.2210T 501, 2210

    2021 doi

  95. [109]

    Verdugo C., Combes F., Dasyra K., Salomé P., Braine J., 2015, @doi [ ] 10.1051/0004-6361/201526551 , 582, A6

    2015 doi

  96. [110]

    Virtanen P., et al., 2020, @doi [Nature Methods] 10.1038/s41592-019-0686-2 , https://rdcu.be/b08Wh 17, 261

    2020 doi

  97. [111]

    Vogelsberger M., Genel S., Sijacki D., Torrey P., Springel V., Hernquist L., 2013, @doi [ ] 10.1093/mnras/stt1789 , 436, 3031–3067

    2013 doi

  98. [112]

    Vogelsberger M., et al., 2014, @doi [ ] 10.1093/mnras/stu1536 , 444, 1518–1547

    2014 doi

  99. [113]

    Vogelsberger M., Marinacci F., Torrey P., Puchwein E., 2020, @doi [Nature Reviews Physics] 10.1038/s42254-019-0127-2 , https://ui.adsabs.harvard.edu/abs/2020NatRP...2...42V 2, 42

    2020 doi

  100. [114]

    Vulcani B., et al., 2018, @doi [ ] 10.3847/2041-8213/aae68b , https://ui.adsabs.harvard.edu/abs/2018ApJ...866L..25V 866, L25

    2018 doi

  101. [115]

    Vulcani B., et al., 2024, @doi [ ] 10.1051/0004-6361/202348135 , https://ui.adsabs.harvard.edu/abs/2024A&A...682A.117V 682, A117

    2024 doi

  102. [116]

    R., Harrison C

    Ward S. R., Harrison C. M., Costa T., Mainieri V., 2022, @doi [ ] 10.1093/mnras/stac1219 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.514.2936W 514, 2936

    2022 doi

  103. [117]

    Weinberger R., et al., 2017, @doi [ ] 10.1093/mnras/stw2944 , 465, 3291

    2017 doi

  104. [118]

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

    2018 doi

  105. [119]

    J., Lagos C

    Wright R. J., Lagos C. d. P., Power C., Stevens A. R. H., Cortese L., Poulton R. J. J., 2022, @doi [ ] 10.1093/mnras/stac2042 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.516.2891W 516, 2891

    2022 doi

  106. [120]

    Yun K., et al., 2019, @doi [ ] 10.1093/mnras/sty3156 , 483, 1042

    2019 doi

  107. [121]

    Zinger E., et al., 2020, @doi [ ] 10.1093/mnras/staa2607 , 499, 768

    2020 doi

  108. [122]

    D., Pillepich A., Rohr E., Nelson D., 2024, @doi [ ] 10.1093/mnras/stad3716 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.8257Z 527, 8257

    Zinger E., Joshi G. D., Pillepich A., Rohr E., Nelson D., 2024, @doi [ ] 10.1093/mnras/stad3716 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.8257Z 527, 8257

    2024 doi

  109. [123]

    von der Linden A., Wild V., Kauffmann G., White S. D. M., Weinmann S., 2010, @doi [ ] 10.1111/j.1365-2966.2010.16375.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.404.1231V 404, 1231

    2010

  110. [124]

    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 7, 2026 · model on record in the stance chip above.