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arxiv: 2605.27503 · v1 · pith:7XUTSFQVnew · submitted 2026-05-26 · 🌌 astro-ph.GA · astro-ph.HE

BlackHoleWeather -- Jet-regulated chaotic cold accretion across the meso scale: Morphology and thermodynamics

Vieri Cammelli , Massimo Gaspari , Olmo Piana , Filippo Barbani , Giovanni Stel , Davide M. Brustio , Valeria Olivares , Francesco Salvestrini
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Ashkbiz Danehkar Michael Reefe Pasquale Temi Filippo M. Maccagni Francesco Tombesi Martin Fournier
This is my paper

Pith reviewed 2026-06-29 16:55 UTC · model grok-4.3

classification 🌌 astro-ph.GA astro-ph.HE
keywords AGN feedbackchaotic cold accretiongalaxy groupsjet regulationturbulencemultiphase condensationblack hole fuelingmeso-scale
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The pith

Ambient turbulence controls jet-regulated chaotic cold accretion, yielding extended stormy phases with inefficient fueling at high levels and coherent rainy cycles at low levels.

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

The paper runs two hydrodynamical simulations of a radiatively cooling galaxy-group atmosphere with self-regulated kinetic AGN jets, each started in a different turbulence regime. Stronger turbulence delays the onset of condensation but spreads it into filamentary, mixed structures across a porous cocoon, producing bursty central fueling that later becomes inefficient in a cloud-dominated state. Weaker turbulence allows earlier condensation that stays coherent and centrally confined, supporting a longer-lived cold reservoir and steadier fueling. In both cases the jet suppresses condensation in its channel while permitting it at the ambient interface, and accretion rates exceed the Bondi value once multiphase gas appears. This frames chaotic cold accretion as a jet-regulated weather process in which turbulence sets the morphology and thermodynamics that link halo gas to black-hole feeding.

Core claim

In the stronger-turbulence simulation condensation begins later, becomes extended and filamentary with a broader hot-warm-cold temperature bridge and porous cocoon, and evolves into a cloud-dominated state featuring inefficient central accretion; in the weaker-turbulence run condensation starts earlier, remains coherent and centrally confined inside a regular cocoon, and sustains a longer-lived inner cold reservoir with steady fueling. Condensation is suppressed inside the jet channel yet survives in the surrounding atmosphere and along the jet-ambient interface. Once condensation begins, supermassive-black-hole fueling becomes super-Bondi in both runs.

What carries the argument

Jet-regulated chaotic cold accretion (CCA) cycle, in which the kinetic jet heats, compresses, entrains and mixes the gas while ambient turbulence acts as the control parameter that organizes condensation into distinct weather states (stormy, rainy, cloudy).

If this is right

  • Higher ambient turbulence produces later but more spatially extended, filamentary condensation with a broader temperature distribution and burst-dominated fueling.
  • Lower ambient turbulence produces earlier, coherent, centrally confined condensation that maintains a longer-lived inner cold reservoir and steadier fueling.
  • Condensation is always suppressed inside the jet channel but persists at the jet-ambient interface and in the surrounding atmosphere.
  • Supermassive black hole fueling exceeds the classical Bondi rate once multiphase condensation appears.
  • In the high-turbulence regime the system evolves toward a cloud-dominated state with inefficient central accretion.

Where Pith is reading between the lines

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

  • Differences in observed filamentary structures and cold-gas distributions across galaxy groups may trace variations in ambient turbulence levels.
  • The meso-scale layer identified here provides a concrete bridge between large-scale halo thermodynamics and the small-scale accretion that grows supermassive black holes.
  • Adding magnetic fields or cosmic-ray physics to similar simulations could test whether they modify the turbulence-controlled transition between stormy and rainy states.

Load-bearing premise

The chosen jet model, turbulence driving scheme, and numerical resolution are sufficient to determine the dominant processes that set condensation morphology and accretion rates.

What would settle it

Observations of multiphase gas morphology and central accretion variability in galaxy groups that differ in measured turbulence strength, checked against the predicted differences in filament extent, temperature bridges, and fueling burstiness.

Figures

Figures reproduced from arXiv: 2605.27503 by Ashkbiz Danehkar, Davide M. Brustio, Filippo Barbani, Filippo M. Maccagni, Francesco Salvestrini, Francesco Tombesi, Giovanni Stel, Martin Fournier, Massimo Gaspari, Michael Reefe, Olmo Piana, Pasquale Temi, Valeria Olivares, Vieri Cammelli.

Figure 1
Figure 1. Figure 1: SMR grid visualization showing the sink particle (black cells) and the jet injection zone (orange cells) at the innermost refinement lev￾els. At each iteration, the gas density, the temperature, and the velocity are reset to ρsink = 10−30 g cm−3 , Tsink = 1, and vsink = 0 km s−1 , re￾spectively. The radius of the sink particle rsink is expressed in cell length units and has been set to 4 cells. Figure adap… view at source ↗
Figure 2
Figure 2. Figure 2: Volume rendering of the jet injected at the sink level observed from a location outside the simulation box. Blue color represents the turbulent gas, mainly introduced by the ad-hoc turbulence driving. Reddish gas is instead jet entrained material, while the white depicts the jet core which is expelled at the injection region sitting on the sink. consequences of a kinetically-dominated injected jet, i.e., s… view at source ↗
Figure 3
Figure 3. Figure 3: Zoom–cascade of projections of the simulated gas density along the plane perpendicular to the jet injection axis, z, in the high-turb run. Each panel shows a projection through a finite slab whose thickness equals the panel width, centered on the same point; widths decrease along each column, while the time evolution is observed in different rows from top (t/train = 1) to bottom (t/train = 7). In the first… view at source ↗
Figure 4
Figure 4. Figure 4: Same as [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Similar to [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Same as [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Time evolution of mass-weighted radial profiles of the region outside/inside the jet-cone in the upper/lower figures, respectively. From left to right, several quantities are shown; gas density, temperature, pressure and velocity magnitude with respect to the high-turb (top rows) and low-turb (bottom rows) runs. Colors denote thermal phases broadly tied to observational bands (radio, optical, UV, X-rays), … view at source ↗
Figure 8
Figure 8. Figure 8: Number–density logarithmic probability distribution functions (PDFs) in the radial shell 0 < r < 0.10 kpc (micro-scale) are shown at four times (t/train = 1, 3, 5, 7) with respect to the high-turb (top row) and low-turb (bottom row) case runs. Colors denote gas phases (Molecular, Cold, Warm, Hot - Soft X, Hot - Hard X; see legend), i.e. gas selected by broad temperature intervals as defined in the text. In… view at source ↗
Figure 9
Figure 9. Figure 9: Same as [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Same as [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Phase masses as a function of time in the low-turb and high-turb case, as indicated in the legend, in different radial shells, from micro (top panel) to outer macro (bottom panel) according to the definitions in the text. The time is normalized to the corresponding train in order to emphasize the weather cycles, starting from the activation of radiative cooling and jet feedback. For reference, train ≃ 9 M… view at source ↗
Figure 12
Figure 12. Figure 12: Black-hole accretion rate as a function of time in the low-turb and high-turb runs, as indicated in the legend. The time is normal￾ized to the corresponding train (∼ 9 and ∼ 16 Myr for the low-turb and high-turb runs, respectively), starting from the activation of radiative cooling and jet feedback, in order to highlight the weather-cycle evolu￾tion. Despite the presence of the jet, M˙ • reaches relativel… view at source ↗
Figure 13
Figure 13. Figure 13: Density–temperature phase diagrams at four different epochs (t/train = 1, 3, 5, 7) for the high-turb (first row) and low-turb (second row) reference runs. Axes show density and temperature; colors encode the gas mass per bin (M⊙). At t/train = 1 the distributions are dominated by hot, rarefied gas displaced by the jet, with a prominent ridge following an approximately isobaric track (T ∝ n −1 , denoted by… view at source ↗
Figure 14
Figure 14. Figure 14: Density–temperature phase diagrams at four different radial bins at t/train = 3 (top figure) and t/train = 7 (bottom figure) from micro (r < 0.1 kpc), meso (0.1 < r < 1.0 kpc), to inner (1 < r < 10 kpc) and outer (10 < r < 50 kpc) macro scales. Axes show density and temperature; colors encode the gas mass per bin (M⊙). densities by t/train = 7. In fact, the condensed material increas￾ingly resides in a be… view at source ↗
Figure 15
Figure 15. Figure 15: Schematic sketch of the proposed jet-regulated BlackHoleWeather cycle. The four panels illustrate the phenomenological states sunny, stormy, cloudy, and rainy. sunny: hot-dominated clearing stage, weak multiphase occupation, feeding near the Bondi-like baseline. stormy: ex￾tended filamentary precipitation, broad hot–warm–cold phase-space bridge, burstier feeding. cloudy: substantial cold gas survives as a… view at source ↗
read the original abstract

How mechanical AGN feedback couples to multiphase condensation across scales remains a problem in galaxy groups and clusters. It is unclear how jets reshape the chaotic cold accretion (CCA) cycle and regulate black-hole fueling. BlackHoleWeather aims to build a unified description of the AGN baryon cycle across horizon, galactic, and group scales. Here we focus on how weather states shape the morphology and thermodynamics of jet-regulated CCA. We perform two hydrodynamical simulations of a turbulent, radiatively cooling galaxy-group atmosphere with self-regulated AGN feedback. The runs are initialized in two turbulence regimes and evolved with a kinetic mass-loaded jet. The jet prevents cooling via heating, but anisotropically reorganizes condensation through compression, entrainment, and turbulent mixing. In the stronger-turbulence case, condensation starts later but becomes extended, filamentary, and mixed, with a broader hot-warm-cold bridge, a porous cocoon, and burst-dominated fueling. This run evolves toward a cloud-dominated state with inefficient central accretion. In the weaker-turbulence case, condensation starts earlier and remains coherent and centrally confined, yielding a regular cocoon, a longer-lived inner cold reservoir with sustained fueling. In both runs, condensation is suppressed inside the jet channel and survives in the surrounding atmosphere and along the jet-ambient interface. Once condensation begins, SMBH fueling becomes super-Bondi. These results extend CCA from a pure cooling + turbulence problem to a jet-regulated weather process. Ambient turbulence acts as a control parameter, producing an extended stormy phase, a centrally retained rainy cycle, and, in the high-turbulence case, a later cloudy state with inefficient central fueling. The meso scale emerges as the layer linking halo thermodynamics to SMBH feeding within the broader BlackHoleWeather framework.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The manuscript presents two hydrodynamical simulations of a turbulent, radiatively cooling galaxy-group atmosphere with self-regulated kinetic mass-loaded AGN jet feedback, initialized in two different turbulence regimes. It claims that ambient turbulence level acts as the decisive control parameter for jet-regulated chaotic cold accretion (CCA), producing an extended stormy phase with filamentary mixed condensation, a broader hot-warm-cold bridge, porous cocoon, and bursty inefficient fueling in the high-turbulence case, versus earlier, coherent, centrally confined condensation with sustained fueling in the low-turbulence case. Condensation is suppressed inside the jet channel but occurs along the jet-ambient interface and surrounding atmosphere, leading to super-Bondi SMBH fueling once initiated; the work frames this as extending CCA to a jet-regulated weather process within the BlackHoleWeather framework.

Significance. If the morphology and thermodynamic differences prove robust, the results would usefully extend CCA models by demonstrating how jets anisotropically reorganize multiphase condensation via compression, entrainment, and turbulent mixing, with turbulence level modulating the transition between stormy, rainy, and cloudy states. This could strengthen links between halo-scale thermodynamics and central fueling in galaxy groups. The self-regulated feedback and explicit comparison of turbulence strengths are positive elements, though the hydro-only setup and limited run count constrain the strength of the control-parameter claim.

major comments (3)
  1. [Simulation setup / methods] Simulation setup: only a single run is presented per turbulence regime (stronger vs. weaker driving amplitude), with no resolution study, convergence test, or variation in the jet mass-loading factor reported. This makes it difficult to establish that the reported differences in filamentary extent, cocoon porosity, and fueling burstiness are attributable to turbulence level rather than numerical or parameter choices.
  2. [Simulation setup / results] Physics content: the runs are purely hydrodynamical and omit magnetic fields and cosmic rays. Processes controlled by these (suppression or channeling of mixing at the jet-ambient interface, anisotropic conduction, cosmic-ray pressure support) could modify the hot-warm-cold bridge width, condensation morphology, and central accretion efficiency, directly affecting the claim that turbulence is the decisive control parameter.
  3. [Methods / abstract] Validation and numerics: the abstract and available text provide no details on grid resolution, numerical methods, or direct comparison to observed group/cluster properties. Without these, the central morphological and thermodynamic claims rest on unverified assumptions about the dominant processes captured by the chosen jet model and turbulence driving.
minor comments (1)
  1. [Abstract / results] The distinction between 'stormy', 'rainy', and 'cloudy' states is introduced in the abstract but would benefit from explicit quantitative definitions (e.g., thresholds on condensation extent, velocity dispersion, or accretion rate variability) in the results section for reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed report. We address each major comment point by point below, indicating the revisions that will be incorporated.

read point-by-point responses

  1. Referee: [Simulation setup / methods] Simulation setup: only a single run is presented per turbulence regime (stronger vs. weaker driving amplitude), with no resolution study, convergence test, or variation in the jet mass-loading factor reported. This makes it difficult to establish that the reported differences in filamentary extent, cocoon porosity, and fueling burstiness are attributable to turbulence level rather than numerical or parameter choices.

    Authors: We agree that a single run per regime without dedicated resolution or parameter-variation tests limits the strength of the attribution to turbulence level alone. In the revised manuscript we will add a dedicated 'Limitations and future extensions' subsection that explicitly discusses the current setup constraints, the rationale for the chosen jet mass-loading, and plans for follow-up convergence and parameter studies. We will also include any available internal checks on numerical sensitivity from the existing data. revision: partial

  2. Referee: [Simulation setup / results] Physics content: the runs are purely hydrodynamical and omit magnetic fields and cosmic rays. Processes controlled by these (suppression or channeling of mixing at the jet-ambient interface, anisotropic conduction, cosmic-ray pressure support) could modify the hot-warm-cold bridge width, condensation morphology, and central accretion efficiency, directly affecting the claim that turbulence is the decisive control parameter.

    Authors: The work is presented as a hydrodynamical study, as stated in the abstract and methods. We concur that magnetic fields and cosmic rays could alter mixing, the hot-warm-cold bridge, and accretion efficiency. In the revision we will add a paragraph in the discussion that acknowledges these omissions, cites relevant literature on their expected effects, and qualifies the language from 'decisive control parameter' to 'key control parameter within the hydrodynamical regime'. revision: yes

  3. Referee: [Methods / abstract] Validation and numerics: the abstract and available text provide no details on grid resolution, numerical methods, or direct comparison to observed group/cluster properties. Without these, the central morphological and thermodynamic claims rest on unverified assumptions about the dominant processes captured by the chosen jet model and turbulence driving.

    Authors: We will expand the abstract to include a brief statement on the numerical resolution and hydrodynamical methods. The full methods section already specifies the grid, code, and turbulence driving; we will ensure these details are cross-referenced clearly. In addition, we will insert a short comparison of simulated condensation timescales, jet powers, and accretion rates to observed galaxy-group properties in the results or discussion section. revision: yes

Circularity Check

0 steps flagged

No circularity: outcomes emerge from hydrodynamical evolution

full rationale

The manuscript reports results from two hydrodynamical simulations initialized with different turbulence driving strengths and evolved under a fixed kinetic mass-loaded jet model with self-regulated feedback. The central claims (ambient turbulence as control parameter producing stormy/rainy/cloudy states, extended filamentary condensation in high-turbulence run, centrally retained cycle in low-turbulence run, super-Bondi fueling once condensation begins) are direct numerical outputs, not analytic derivations, fitted parameters renamed as predictions, or self-citation chains. No equations reduce by construction to inputs; no uniqueness theorems or ansatzes are imported from prior author work to force the morphology or thermodynamics. The paper is a numerical experiment whose results are independent of any self-definitional loop.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The work rests on standard hydrodynamical assumptions and simulation parameters chosen to represent two turbulence regimes; no new entities are introduced.

free parameters (2)
  • turbulence driving amplitude
    Two discrete values chosen to represent stronger and weaker regimes; values not numerically specified in abstract.
  • jet mass-loading factor
    Kinetic jet is described as mass-loaded; exact factor not given in abstract.
axioms (1)
  • domain assumption Ideal hydrodynamics plus radiative cooling and a subgrid kinetic jet model capture the dominant meso-scale processes.
    Invoked by the choice to run pure hydro simulations without magnetic fields or cosmic rays.

pith-pipeline@v0.9.1-grok · 5911 in / 1225 out tokens · 34245 ms · 2026-06-29T16:55:47.411264+00:00 · methodology

discussion (0)

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Forward citations

Cited by 1 Pith paper

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

  1. Active galactic nucleus driven jet feedback in cosmologically forming cool-core galaxy clusters I: The effect of hierarchical assembly on intra-cluster medium properties

    astro-ph.GA 2026-06 unverdicted novelty 4.0

    Cosmological simulations with AGN jet feedback reproduce observed cool-core cluster ICM properties more accurately than isolated simulations or IllustrisTNG due to merger-driven effects on gas velocity and warm gas abundance.

Reference graph

Works this paper leans on

97 extracted references · 3 canonical work pages · cited by 1 Pith paper

  1. [1]

    Abramowicz, M. A. & Fragile, P. C. 2013, Living Reviews in Relativity, 16, 1

    2013

  2. [2]

    A., Bureau, M., et al

    Alatalo, K., Davis, T. A., Bureau, M., et al. 2013, MNRAS, 432, 1796

    2013

  3. [3]

    2017, Quinoa, Version 0.2, U.S

    Bakosi, J., Junghans, C., Bird, R., et al. 2017, Quinoa, Version 0.2, U.S. Depart- ment of Energy (DOE) Software

    2017

  4. [4]

    2023, MNRAS, 524, 4091

    Barbani, F., Pascale, R., Marinacci, F., et al. 2023, MNRAS, 524, 4091

    2023

  5. [5]

    2025, A&A, 697, A121

    Barbani, F., Pascale, R., Marinacci, F., et al. 2025, A&A, 697, A121

    2025

  6. [6]

    2019, ARA&A, 57, 467 Brüggen, M., Scannapieco, E., & Grete, P

    Blandford, R., Meier, D., & Readhead, A. 2019, ARA&A, 57, 467 Brüggen, M., Scannapieco, E., & Grete, P. 2023, ApJ, 951, 113

    2019

  7. [7]

    Butcher, J. C. 2008, Numerical Methods for Ordinary Differential Equations, 2nd edn. (John Wiley & Sons)

    2008

  8. [8]

    2026, A&A, Submitted

    Cammelli, V ., Gaspari, M., Barbani, F., & et al. 2026, A&A, Submitted

    2026

  9. [9]

    C., et al

    Cammelli, V ., Monaco, P., Tan, J. C., et al. 2025, MNRAS, 536, 851

    2025

  10. [10]

    2025, arXiv e-prints, arXiv:2504.20538

    Castignani, G., Combes, F., Salomé, P., Edge, A., & Jablonka, P. 2025, arXiv e-prints, arXiv:2504.20538

    work page arXiv 2025

  11. [11]

    Chevalier, R. A. & Clegg, A. W. 1985, Nature, 317, 44

    1985

  12. [12]

    S., Su, K.-Y ., Narayan, R., & Natarajan, P

    Cho, H., Prather, B. S., Su, K.-Y ., Narayan, R., & Natarajan, P. 2024, The Astro- physical Journal, 977, 200

    2024

  13. [13]

    F., & Kale, L

    Choi, J., Richards, D. F., & Kale, L. V . 2022, arXiv e-prints, arXiv:2202.11819

    work page arXiv 2022

  14. [14]

    2014, A&A, 562, A21

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

    2014

  15. [15]

    & Ostriker, J

    Ciotti, L. & Ostriker, J. P. 2007, ApJ, 665, 1038

    2007

  16. [16]

    P., O’Sullivan, E., Jones, C., et al

    David, L. P., O’Sullivan, E., Jones, C., et al. 2011, ApJ, 728, 162

    2011

  17. [17]

    2015, ApJ, 805, 177

    Donahue, M., Connor, T., Fogarty, K., et al. 2015, ApJ, 805, 177

    2015

  18. [18]

    Eckert, D., Gaspari, M., Gastaldello, F., Le Brun, A. M. C., & O’Sullivan, E. 2021, Universe, 7, 142

    2021

  19. [19]

    2014, Journal of Parallel and Dis- tributed Computing, 74

    Edwards, H., Trott, C., & Sunderland, D. 2014, Journal of Parallel and Dis- tributed Computing, 74

    2014

  20. [20]

    2022, Monthly Notices of the Royal Astronomical Society, 518, 4622–4645

    Ehlert, K., Weinberger, R., Pfrommer, C., Pakmor, R., & Springel, V . 2022, Monthly Notices of the Royal Astronomical Society, 518, 4622–4645

    2022

  21. [21]

    2012, Annual Review of Astronomy and Astrophysics, 50, 455 Faucher-Giguère, C.-A

    Fabian, A. 2012, Annual Review of Astronomy and Astrophysics, 50, 455 Faucher-Giguère, C.-A. & Quataert, E. 2012, MNRAS, 425, 605

    2012

  22. [22]

    Field, G. B. 1965, ApJ, 142, 531

    1965

  23. [23]

    2017, A&A, 601, A143

    Fiore, F., Feruglio, C., Shankar, F., et al. 2017, A&A, 601, A143

    2017

  24. [24]

    2015, ApJ, 813, 117

    Fogarty, K., Postman, M., Connor, T., Donahue, M., & Moustakas, J. 2015, ApJ, 813, 117

    2015

  25. [25]

    W., & O’Shea, B

    Fournier, M., Grete, P., Brüggen, M., Glines, F. W., & O’Shea, B. W. 2024, A&A, 691, A239

    2024

  26. [26]

    2025, A&A, 698, A121

    Fournier, M., Grete, P., Brüggen, M., et al. 2025, A&A, 698, A121

    2025

  27. [27]

    2012, Monthly Notices of the Royal Astronomical Society, 425, 438

    Gaibler, V ., Khochfar, S., Krause, M., & Silk, J. 2012, Monthly Notices of the Royal Astronomical Society, 425, 438

    2012

  28. [28]

    2015, A&A, 579, A62

    Gaspari, M., Brighenti, F., & Temi, P. 2015, A&A, 579, A62

    2015

  29. [29]

    & Churazov, E

    Gaspari, M. & Churazov, E. 2013, A&A, 559, A78

    2013

  30. [30]

    2019, ApJ, 884, 169

    Gaspari, M., Eckert, D., Ettori, S., et al. 2019, ApJ, 884, 169

    2019

  31. [31]

    L., et al

    Gaspari, M., McDonald, M., Hamer, S. L., et al. 2018, ApJ, 854, 167

    2018

  32. [32]

    Gaspari, M., Ruszkowski, M., & Oh, S. P. 2013, MNRAS, 432, 3401

    2013

  33. [33]

    2012, ApJ, 746, 94

    Gaspari, M., Ruszkowski, M., & Sharma, P. 2012, ApJ, 746, 94

    2012

  34. [34]

    & S˛ adowski, A

    Gaspari, M. & S˛ adowski, A. 2017, ApJ, 837, 149

    2017

  35. [35]

    2017, MNRAS, 466, 677

    Gaspari, M., Temi, P., & Brighenti, F. 2017, MNRAS, 466, 677

    2017

  36. [36]

    2020, Nature Astronomy, 4, 10

    Gaspari, M., Tombesi, F., & Cappi, M. 2020, Nature Astronomy, 4, 10

    2020

  37. [37]

    Gitti, M., Brighenti, F., & McNamara, B. R. 2012, Advances in Astronomy, 2012, 950641

    2012

  38. [38]

    Godunov, S. K. 1959, Math. Sbornik, 47, 271

    1959

  39. [39]

    C., Miller, J

    Grete, P., Dolence, J. C., Miller, J. M., et al. 2023, The International Journal of High Performance Computing Applications, 37, 465

    2023

  40. [40]

    W., Glines, F

    Grete, P., O’Shea, B. W., Glines, F. W., et al. 2025, ApJ, 988, 155

    2025

  41. [41]

    M., Kim, C.-G., & Quataert, E

    Guo, M., Stone, J. M., Kim, C.-G., & Quataert, E. 2023, The Astrophysical Jour- nal, 946, 26

    2023

  42. [42]

    Harrison, C. M. 2017, Nature Astronomy, 1, 0165

    2017

  43. [43]

    S., Nandra, K., Clerc, N., & Gaspari, M

    Hofmann, F., Sanders, J. S., Nandra, K., Clerc, N., & Gaspari, M. 2016, A&A, 585, A130

    2016

  44. [44]

    2017, in Practice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact, 1–8

    Holmen, J., Humphrey, A., Sunderland, D., & Berzins, M. 2017, in Practice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact, 1–8

    2017

  45. [45]

    & Pounds, K

    King, A. & Pounds, K. 2015, ARA&A, 53, 115

    2015

  46. [46]

    Li, J., Emonts, B. H. C., Cai, Z., et al. 2021, ApJ, 922, L29

    2021

  47. [47]

    & Bryan, G

    Li, Y . & Bryan, G. L. 2014, The Astrophysical Journal, 789, 54

    2014

  48. [48]

    M., Serra, P., Gaspari, M., et al

    Maccagni, F. M., Serra, P., Gaspari, M., et al. 2021, A&A, 656, A45

    2021

  49. [49]

    2021, The Messenger, 182, 45

    Mainieri, V ., Circosta, C., Kakkad, D., et al. 2021, The Messenger, 182, 45

    2021

  50. [50]

    R., Fabian, A

    Maiolino, R., Russell, H. R., Fabian, A. C., et al. 2017, Nature, 544, 202

    2017

  51. [51]

    McCourt, M., Sharma, P., Quataert, E., & Parrish, I. J. 2012, MNRAS, 419, 3319

    2012

  52. [52]

    R., & Tremblay, G

    McDonald, M., Gaspari, M., McNamara, B. R., & Tremblay, G. R. 2018, ApJ, 858, 45

    2018

  53. [53]

    McNamara, B. R. & Nulsen, P. E. J. 2012, New Journal of Physics, 14, 055023

    2012

  54. [54]

    2019, ApJ, 877, 74

    Menci, N., Fiore, F., Feruglio, C., et al. 2019, ApJ, 877, 74

    2019

  55. [55]

    2008, ApJ, 686, 219

    Menci, N., Fiore, F., Puccetti, S., & Cavaliere, A. 2008, ApJ, 686, 219

    2008

  56. [56]

    2017, Frontiers in Astronomy and Space Sciences, 4, 42

    Morganti, R. 2017, Frontiers in Astronomy and Space Sciences, 4, 42

    2017

  57. [57]

    2025, Nature Astronomy, 9, 449

    Olivares, V ., Picquenot, A., Su, Y ., et al. 2025, Nature Astronomy, 9, 449

    2025

  58. [58]

    2019, A&A, 631, A22

    Olivares, V ., Salome, P., Combes, F., et al. 2019, A&A, 631, A22

    2019

  59. [59]

    L., et al

    Olivares, V ., Salomé, P., Hamer, S. L., et al. 2022, A&A, 666, A94

    2022

  60. [60]

    A., et al

    Omoruyi, O., Tremblay, G., Baum, S. A., et al. 2026, ApJ, 997, 114

    2026

  61. [61]

    Piana, O., Dayal, P., V olonteri, M., & Choudhury, T. R. 2021, MNRAS, 500, 2146

    2021

  62. [62]

    & Soker, N

    Pizzolato, F. & Soker, N. 2005, ApJ, 632, 821

    2005

  63. [63]

    W., et al

    Prasad, D., Grete, P., O’Shea, B. W., et al. 2026, MNRAS, 545, staf2155

    2026

  64. [64]

    2015, The Astrophysical Journal, 811, 108

    Prasad, D., Sharma, P., & Babul, A. 2015, The Astrophysical Journal, 811, 108

    2015

  65. [65]

    2025, arXiv e-prints, arXiv:2501.08527

    Reefe, M., McDonald, M., Chatzikos, M., et al. 2025, arXiv e-prints, arXiv:2501.08527

    work page arXiv 2025

  66. [66]

    E., Gaspari, M., Schellenberger, G., et al

    Romero, C. E., Gaspari, M., Schellenberger, G., et al. 2025, ApJ, 985, 248

    2025

  67. [67]

    R., McNamara, B

    Russell, H. R., McNamara, B. R., Fabian, A. C., et al. 2019, MNRAS, 490, 3025

    2019

  68. [68]

    A., Goldbaum, N

    Schive, H.-Y ., ZuHone, J. A., Goldbaum, N. J., et al. 2018, MNRAS, 481, 4815

    2018

  69. [69]

    Sharma, P., McCourt, M., Quataert, E., & Parrish, I. J. 2012, MNRAS, 420, 3174

    2012

  70. [70]

    & Rees, M

    Silk, J. & Rees, M. J. 1998, A&A, 331, L1 S˛ adowski, A. & Gaspari, M. 2017, MNRAS, 468, 1398

    1998

  71. [71]

    M., Tomida, K., White, C

    Stone, J. M., Tomida, K., White, C. J., & Felker, K. G. 2020, ApJS, 249, 4

    2020

  72. [72]

    M., Donahue, M., et al

    Sun, M., V oit, G. M., Donahue, M., et al. 2009, ApJ, 693, 1142

    2009

  73. [73]

    C., & Narayan, R

    Tchekhovskoy, A., McKinney, J. C., & Narayan, R. 2012, in Journal of Physics Conference Series, V ol. 372, Journal of Physics Conference Series (IOP), 012040

    2012

  74. [74]

    Tchekhovskoy, A., Narayan, R., & McKinney, J. C. 2011, MNRAS, 418, L79

    2011

  75. [75]

    2018, ApJ, 858, 17

    Temi, P., Amblard, A., Gitti, M., et al. 2018, ApJ, 858, 17

    2018

  76. [76]

    2022, ApJ, 928, 150

    Temi, P., Gaspari, M., Brighenti, F., et al. 2022, ApJ, 928, 150

    2022

  77. [77]

    2026, The Astrophysical Journal, Submitted

    Temi, P., Ubertosi, F., Brighenti, F., & et al. 2026, The Astrophysical Journal, Submitted

    2026

  78. [78]

    N., et al

    Tombesi, F., Cappi, M., Reeves, J. N., et al. 2013, MNRAS, 430, 1102

    2013

  79. [79]

    N., et al

    Tombesi, F., Cappi, M., Reeves, J. N., et al. 2011, ApJ, 742, 44

    2011

  80. [80]

    N., et al

    Tombesi, F., Cappi, M., Reeves, J. N., et al. 2010, A&A, 521, A57

    2010

Showing first 80 references.