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During AGN outbursts, jet material spreading sideways compresses the hot gas around it and cools into cold clumps on a roughly 30-million-year timescale.

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2026-08-03 09:12 UTC pith:G3ZL5JAK

load-bearing objection Concrete, testable mechanism for in-situ cold clump formation at jet edges; the tracer/control core holds up, but the quantitative Mach threshold rests on unresolved cooling and should be treated as provisional. the 4 major comments →

arxiv 2601.14391 v2 pith:G3ZL5JAK submitted 2026-01-20 astro-ph.GA

Cold gas formation triggered by active galactic nuclei jet feedback in galaxy cluster cores

Stefano Sotira , Martin A. Bourne , Debora Sijacki , Franco Vazza , Fabrizio Brighenti This is my paper
classification astro-ph.GA
keywords AGN feedbackcool-core galaxy clustersmultiphase intracluster mediumcold gas formationjet physicsturbulencesimulations
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper argues that active galactic nucleus (AGN) jets do more than heat the centers of galaxy clusters: they also trigger the formation of cold gas. Using high-resolution hydrodynamic simulations of a Perseus-like cluster with self-regulated jet feedback, the authors find that jet material expanding laterally into the surrounding intracluster medium compresses the hot gas, shortening its cooling time. That compressed gas, specifically in regions of high compression and low vorticity, condenses in situ into cold clumps within about 30 million years. The paper identifies a statistical marker for this process: condensation is strongly favored when the local turbulent Mach number of the hot gas is around 0.3.

Core claim

The central claim is that jet-driven lateral expansion, not just jet-induced uplift or turbulence, provides a positive feedback channel for cold gas formation. During individual AGN outbursts, hot jet material injected sideways into the turbulent mixing layer expands and compresses the surrounding hot intracluster medium, reducing local cooling times and producing new cold clumps in roughly 30 Myr. Using passive tracers, the authors show that hot gas (T ≥ 10^7 K) located in high-compression, low-vorticity zones condenses in situ, with the condensation fraction increasing when the local velocity divergence is more negative and the compressive ratio is high. Statistically, this condensation oc

What carries the argument

The key machinery is the interaction between the AGN jet's lateral expansion and the thermodynamics of the surrounding ICM. The paper isolates zones of high compression (strongly negative velocity divergence) and low vorticity (high compressive ratio r_cs, defined as the squared divergence over the sum of squared divergence and curl). These zones form at the tangled interface between the laterally expanding jet and the hot ICM, where turbulence is injected but vorticity remains low. This spatial selection is shown to be a good predictor of which hot gas parcels will cool to form cold clumps over the following ~30 Myr.

Load-bearing premise

The simulations do not fully resolve the cooling length and internal structure of the cold clumps they claim to form, so the result that hot gas condenses in situ on ~30 Myr timescales could be an artifact of unresolved numerical cooling rather than a real physical process.

What would settle it

Run the same simulation at higher resolution (or with a subgrid model that fully resolves the cooling length) and measure the fraction of tracer mass that condenses. If the condensation fraction drops sharply when the cooling length is resolved, the claimed mechanism is a numerical artifact. Alternatively, observe a cool-core cluster with X-ray spectroscopy: if no hot gas with local turbulent Mach number ≈0.3 is found coincident with cold clumps, the theory is contradicted.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • If correct, cold gas filaments in cool-core cluster centers can form directly from the hot ICM in situ, without requiring uplift of pre-existing cold gas from the center.
  • The local turbulent Mach number of ~0.3 in the hot gas becomes a diagnostic: regions of the ICM with this level of turbulence are prime sites for condensation, which can be searched for with X-ray instruments like XRISM.
  • The mechanism operates concurrently with AGN heating, meaning a single jet outburst both suppresses global cooling and locally promotes cold gas formation, reconciling positive and negative feedback.
  • The ~30 Myr condensation timescale sets a predictively useful timeline connecting AGN activity to the appearance of new cold clumps, linking observed AGN duty cycles to multiphase gas evolution.
  • The hydrodynamical jet deflection by cold clouds in the simulation provides a natural, non-precessing way to distribute jet energy and create the lateral expansion zones that drive condensation.

Where Pith is reading between the lines

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

  • This mechanism may generalize beyond Perseus-like cool cores to any environment where a jet or outflow expands into a hot, dilute medium, such as elliptical galaxies or groups, suggesting a common pathway for cold gas formation.
  • The authors do not model magnetic fields; if magnetic pressure or tension suppresses compression in the high-compression, low-vorticity zones, the condensation efficiency could be lower than simulated, so the ≈0.3 Mach threshold might be environment-dependent.
  • A testable extension would be to compare the predicted spatial correlation between hot-gas regions with Mach number ≈0.3 and observed cold Hα or CO filaments in the same clusters, since the simulation predicts they should coincide within tens of millions of years.
  • The measured cold-gas fractions and kinematics in the simulation could be folded into synthetic observations (emission-line maps, X-ray spectra) to tell observers what signatures of this mechanism look like.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The manuscript presents idealized AREPO simulations of a Perseus-like galaxy cluster with self-regulated AGN kinetic/thermal jet feedback, multi-phase cooling down to 10 K via Grackle, a simple star-formation prescription, and a sink-particle SMBH accretion model. The central claim is that during individual AGN outbursts the laterally expanding jet cocoon injects turbulence into the ICM, creating localized regions of strong compression and low vorticity in which hot gas (T ≥ 10^7 K) condenses in situ into cold clumps on a ~30 Myr timescale. The authors support this with tracer re-runs in which gas selected by strongly negative velocity divergence and high compressive ratio subsequently cools, while expanding-gas controls cool much less. They also present a statistical analysis of seven 5-kpc regions suggesting that condensation is preferentially promoted when the hot-gas turbulent Mach number σ_hot/c_s,hot is approximately 0.3. Global ICM density, temperature, and entropy profiles, as well as phase masses and warm-gas kinematics, are compared favorably with Perseus and cool-core cluster observations.

Significance. If the mechanism is confirmed, the paper provides a concrete and potentially observable in-situ formation channel for cold clumps in cluster cores, connecting AGN jet lateral expansion, compressive turbulence, and thermal instability. The paper's strengths include direct tracer tracking from hot to cold, the use of control tracer runs with expanding gas, a range of divergence and compressive-ratio thresholds, and a multi-phase simulation that reproduces several observed cool-core properties over a 4 Gyr self-regulated cycle. The proposed σ_hot/c_s ≈ 0.3 threshold and the predicted multiphase kinematics are testable with XRISM and deep radio observations. However, the quantitative claims currently rest on a single idealized realization, an unresolved cooling microphysical scale, and an unpublished accretion model, so the central numerical result is not yet established at the level claimed in the abstract.

major comments (4)
  1. [§2.2, §4.2.1, Table A.1] The paper explicitly acknowledges in §2.2 that 'the cooling length and internal structure of cold clouds are not fully resolved' and that the exact phase partitioning 'should be interpreted as an indicative diagnostic of the model behaviour, as opposed to a strict quantitative prediction.' This caveat is not carried through to the abstract and §4.2.1, which present a ~30 Myr condensation timescale, condensation fractions up to ~60%, and the σ_hot/c_s ≈ 0.3 threshold as robust results. The tracer validation covers only one time interval (t = 1.38–1.5 Gyr) and no convergence study is provided; the re-runs vary selection criteria, not resolution. In a moving-mesh simulation, unresolved thermal instability can be artificially suppressed by numerical diffusion or artificially enhanced by over-cooling. I request a resolution study, or at least a diagnostic comparing cell size to the local cool
  2. [§4.2.1, Eq. (9), Table A.1] The tracer selection is based on strongly negative velocity divergence and high compressive ratio. Since at fixed temperature t_cool ∝ ρ^{-1}, gas selected for strong compression has a shorter cooling time by construction. The monotonic increase in condensation fraction with more negative divergence in Fig. 7 is thus partly built into the selection. The control runs with ⟨∇·v⟩ > 0 are not a clean control for isolating the role of compression: expansion simultaneously lowers density and raises cooling time, so a low condensation fraction is expected even in the absence of any special physical mechanism. To substantiate the in-situ condensation claim, the authors should add a control in which tracers are placed in dense but non-compressive gas, or otherwise correct for the initial overdensity/cooling-time distribution of the selected cells. As it stands, the tracer experiment shows that co
  3. [§2.1, §2.4, §4.3] The conclusions are drawn from a single idealized realization. The jet is launched along a fixed z-axis with no precession (§2.4), and the self-regulated SMBH accretion and feedback cycle uses a sink-particle model from an unpublished companion paper (Ortame et al., in prep). The statistical analysis in §4.3 uses seven fixed 5-kpc regions over a 1.2 Gyr interval, so the 'characteristic' Mach number of ~0.3 is derived from one jet geometry and one realization, with no variance estimate. Furthermore, magnetic fields and thermal conduction are neglected; both are known to affect thermal instability and cold-cloud survival in cluster cores. I request at least one additional realization with a different jet orientation or perturbation seed, a sensitivity test of the sink-model parameters, or explicit discussion of why the single realization is representative. Without this, the words 'characte
  4. [§4.2, §4.3, Fig. 10] The direct causal connection between jet-induced compression and condensation is established for only one 6-kpc region and one 30-Myr interval (Section 4.2, Fig. 6–9). The statistical distribution in Fig. 10 counts cells that satisfy the divergence/compressive-ratio cuts at each time, but it does not verify that those cells actually condense; the only condensation measurements are the tracer re-runs at one epoch. The approximate overlap between the red and blue distributions in Fig. 10 is suggestive, but without repeating the tracer experiment at later epochs (e.g., 2 Gyr and 3 Gyr), the statement in §4.3 that this 'once more proves the positive feedback loop' is stronger than the evidence. Adding multi-epoch tracer re-runs, or explicitly acknowledging that the statistical relation is based on a proxy rather than direct condensation tracking, is necessary to support the generality of the
minor comments (6)
  1. [Abstract] The phrase 'injected laterally to the jet axis' should be 'injected laterally with respect to the jet axis' or 'perpendicular to the jet axis'.
  2. [Table A.1] The table lists runs Y and Z, but the text only describes runs A, B, C and the controls. Please define or remove the Y/Z notation.
  3. [Fig. 8 caption] The caption says 'centred at the cluster centre, and ±2, and 4 kpc away from the centre'; this is unclear. Specify the slice positions explicitly (e.g., y = 0, ±2, ±4 kpc).
  4. [Section 2.2] The temperature threshold for 'cold gas' is T < 5×10^4 K, which includes warm ionized gas. The terminology 'warm and cold phases' is used somewhat interchangeably; please define the phase nomenclature consistently (e.g., warm ionized, neutral, molecular, and total T < 5×10^4 K gas).
  5. [References] The name 'Voit' appears as 'V oit' at several points (e.g., Sections 1 and 5); this appears to be a LaTeX spacing artifact and should be corrected throughout.
  6. [Section 3.3] The sentence 'Around the epoch of t = 2 Gyr, however, in the central 20 kpc, the core is almost one order of magnitude denser' would benefit from specifying the region and reference value; the following discussion is clear, but a numerical comparison would help.

Circularity Check

0 steps flagged

No significant circularity: the cold-gas formation mechanism is a simulation outcome with discriminating control runs, not a reduction to its inputs.

full rationale

The central derivation is: jet cocoon lateral expansion -> compression of hot ICM (overdensity tails in Fig. 6) -> shortened cooling times below ~30 Myr -> cold clumps form in situ; tracers selected by velocity divergence and compressive ratio, not by temperature or by the predicted cold phase, then cool to <5x10^4 K within 30 Myr, while expansion-control runs show only ~10-14% condensation versus up to ~58% for strongly compressed gas (Fig. 7, Table A.1). Thus the condensation fraction is a measured outcome, not an output forced by the selection criterion. The ~0.3 Mach-number condition is a posteriori statistical peak of the mass of compression-selected gas, not a fitted parameter tuned to reproduce cold gas; the paper explicitly labels it necessary but not sufficient. Self-citations (Bourne & Sijacki 2017, 2021; Sotira et al. 2025) supply methodology and context, not a load-bearing uniqueness theorem. The admitted limitation in Sec. 2.2 that the cooling length and internal structure of cold clouds are not fully resolved, and the phase partitioning should be treated as an indicative diagnostic rather than a strict quantitative prediction, is a numerical robustness caveat, not a circular step. The dependence on the unpublished sink-particle model (Ortame et al., in prep) is a support limitation, not circularity. The comparison of simulated radial profiles to the observed Perseus profiles used to set initial conditions is a consistency check rather than the paper's central claim. No load-bearing step is equivalent, by definition or by self-citation, to its own inputs.

Axiom & Free-Parameter Ledger

9 free parameters · 8 axioms · 0 invented entities

No new physical particles, forces, or conserved quantities are introduced. The tracer fluid and sink particle are numerical bookkeeping devices, not physical entities. The central claim rests on a set of subgrid modeling choices (feedback efficiencies, thresholds, tracer cuts) and on unresolved cooling physics being captured correctly.

free parameters (9)
  • Feedback efficiency ε = 0.1
    Sets jet energy ĖJ=ε Ṁacc c²; directly controls jet power, lateral expansion, and the compression that triggers cold-gas formation (§2.4).
  • Accretion efficiency η = 0.1
    Determines Ṁacc from drained cold gas; sets AGN burst amplitude and duty cycle (§2.4).
  • Accretion timescale τ_acc = 5 Myr
    Controls how fast cold gas is consumed and thus the timing of jet bursts (approximately the free-fall time of the accretion region) (§2.4).
  • Cold-gas accretion threshold T_cold = 10^4 K
    Only gas below 10^4 K within 500 pc fuels the SMBH; defines the feedback trigger (§2.4).
  • Density threshold n_th for thermal vs kinetic jet coupling = 10 cm^-3
    Dense clouds receive thermal+momentum injection, hot ICM receives kinetic injection; shapes jet deflection and lateral expansion (§2.4).
  • Star formation density threshold n_SF = 2×10^4 cm^-3
    Dense gas converted to star particles with no feedback; prevents numerical over-density but removes mass from the cold-gas reservoir (§2.3).
  • Tracer selection divergence and compressive-ratio cuts = ⟨∇·v⟩ < -10 to -500 km/s/kpc; r_cs > 0.8
    These analysis thresholds define the 'compression zones' whose condensation is measured; they directly shape the central in-situ-formation claim (§4.2.1, Table A.1).
  • Initial SMBH mass = 4×10^9 M⊙
    Chosen to match Perseus; sets the central gravitational potential and influences the jet energy scale (§2.1).
  • Jet injection cylinder parameters = r_jet = h_jet; target mass 10^4 M⊙ or ≥10 cells
    Numerical choices for the injection region; affect jet collimation and resolution (§2.4).
axioms (8)
  • domain assumption The simulated cluster after 2 Gyr adiabatic relaxation and 4 Gyr evolution is representative of real cool-core clusters, despite starting from idealized analytical profiles and lacking cosmological accretion.
    Initial conditions from Churazov et al. (2004) and an NFW halo; realism of the feedback cycle depends on this idealization (§2.1).
  • domain assumption Magnetic fields and thermal conduction are negligible for the cold-gas formation mechanism.
    Observations suggest magnetic fields stabilize filaments; conduction could suppress thermal instability at unresolved scales; both may change cold-gas formation efficiency (§1, §5).
  • domain assumption The unresolved cooling length and internal structure of cold clouds do not qualitatively change the in-situ condensation result.
    Paper explicitly states cooling length and internal cloud structure are unresolved (§2.2); the central claim of in-situ condensation relies on unresolved cooling being captured correctly.
  • domain assumption AREPO's moving-mesh hydrodynamics and GRACKLE's non-equilibrium cooling tables accurately treat the resolved ICM flow and radiative cooling.
    The whole analysis rests on these codes; no code-level verification is included (§2.1, §2.2).
  • domain assumption Passive tracers advected with the flow faithfully track the thermodynamic history of the gas that forms cold clumps.
    The tracer re-runs are the main evidence for in-situ condensation; tracer fidelity is assumed (§4.2.1).
  • ad hoc to paper The unpublished sink-particle accretion model (Ortame et al., in prep) correctly describes SMBH fueling from cold gas.
    The AGN feedback cycle, jet burst timing, and cold-gas triggering depend on this unpublished model (§2.4).
  • domain assumption Star particles with no feedback do not significantly affect the jet-driven cold-gas cycle.
    Stellar feedback is omitted; it could affect cold-gas dynamics but likely not the central mechanism (§2.3).
  • domain assumption The subgrid jet-cloud coupling (ram-pressure-only treatment for dense gas) is adequate.
    Cold dense clouds are assumed to feel only ram pressure, with thermal+momentum injection; this shapes jet redirection and lateral expansion (§2.4).

pith-pipeline@v1.3.0-alltime-deepseek · 19469 in / 15139 out tokens · 156564 ms · 2026-08-03T09:12:56.312023+00:00 · methodology

0 comments
read the original abstract

Extended warm and cold gas nebulae, with complex morphologies and kinematics, have been observed in the centres of cool-core galaxy clusters. Their origin within the hot intracluster medium (ICM) is still puzzling, and among many mechanisms, positive feedback from the central active galactic nucleus (AGN) has been proposed. In this work, we performed a suite of very high-resolution hydrodynamic simulations of a Perseus-like cool-core galaxy cluster subject to self-regulated AGN jet feedback, which leads to realistic ICM properties. By explicitly following warm ionized, neutral, and molecular gas phases, we studied the complex interplay between AGN activity and the multi-phase ICM. While AGN feedback globally heats the ICM, we find that during the individual AGN jet bursts, hot material is also injected laterally to the jet axis, within the turbulent mixing layer. This material, as it expands, compresses the surrounding hot ICM, reducing the local cooling time, and leads to the formation of cold clumps on a characteristic timescale of $\sim 30$ Myr. By employing tracers, we explicitly track cooling within the affected regions, finding that very hot gas identified in high-compression, low-vorticity zones condenses in situ to form cold clumps. A statistical analysis reveals that the condensation of cold gas is highly promoted once the local turbulent Mach number, $\sigma_{hot}/c_{s,hot}$, in the hot gas component ($T \geq 10^7$ K) takes values around ~0.3. The presented process is a further important step in understanding the physical mechanisms that lead to the formation of cold gas in the cluster core. Our measured values of the characteristic turbulent Mach number, together with detailed multi-phase gas kinematics predictions, provide important theoretical tools to interpret future X-ray spectroscopy and deep radio data, ultimately to constrain the origin of cool-core cluster nebulae.

Figures

Figures reproduced from arXiv: 2601.14391 by Debora Sijacki, Fabrizio Brighenti, Franco Vazza, Martin A. Bourne, Stefano Sotira.

Figure 1
Figure 1. Figure 1: Top panels: overview of the gas phases of interest during three particular moments of the simulation (columns from left to right). First row: X-ray emission shown with black-red colour tones (see the main text for the emission calculations) and jet tracer projections contours (black colour). X-ray cavities coincide with the locations where jet tracer material is prevalent; second row: Warm ionized gas (3×1… view at source ↗
Figure 2
Figure 2. Figure 2: From left to the right, X-ray emission-weighted profiles of electron number density, temperature and entropy of our simulations, colour coded with respect to time (purple-yellow colour bar), and compare with: the Perseus cluster derived by Churazov et al. (2004) (green diamonds); cool-core clusters from the ACCEPT sample by Cavagnolo et al. (2008) (grey lines), with a central temperature in a range of 1.5 … view at source ↗
Figure 3
Figure 3. Figure 3: Total mass of gas with temperature below 5 × 104 K (solid blue line), as well as mass of neutral hydrogen HI (dashed blue line), molecu￾lar hydrogen H2 (dotted blue line) and warm-ionized phase (dot-dashed blue line). Note that the total amount refers to both hydrogen and he￾lium, while the different phases refer only to hydrogen. For complete￾ness, the total stellar mass (orange dashed line) is also shown… view at source ↗
Figure 4
Figure 4. Figure 4: A series of time instances showing the spatial distribution of the jet fraction in the meridional plane (red-yellow colour-coding). The column density of the gas with temperatures below 5 × 104 K is overplotted as well (blue-green colour-coding). The black circle indicates a 6 kpc radius region, centred at (x, y,z) = (−3, −12, −5) kpc, in which the properties of [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Simplified sketch of the cold clumps formation model at the “edges” of the main jet. Only a small vertical section of one jet is shown for simplicity. The red arrow indicates the main velocity of the jet (∼ 1000 km/s), while the edges are expanding in a perpendicular di￾rection at a lower speed (∼ 300 km/s). The thin black line demarks the separation of the jet material from the ICM. The green curly region… view at source ↗
Figure 6
Figure 6. Figure 6: Left: Distribution of the hot gas mass, i.e., with T > 107 K, binned as a function of overdensity, δρ/ρ¯ = (ρi − ρ¯)/ρ¯, where ρi and ρ¯ are the density of the i-th cell and the average density, respectively. Right: corresponding mass-weighted cooling time in each bin of overdensity. The distributions are computed in a spherical region having a radius of 6 kpc and centred at (x, y,z) = (−3, −12, 5) kpc (hi… view at source ↗
Figure 7
Figure 7. Figure 7: Mass fraction of tracers that goes in the cold phase for every tracer re-run (dot symbols) performed with different divergence cuts from −10 km/s/kpc to −500 km/s/kpc (x-axis) and with different com￾pressive ratio cuts, rcs > 0.8 (blue line) and rcs > 0 (red line). The mass of tracers that condense in the cold gas increases when the divergence and compressive ratio cuts are higher, reaching up to ∼ 60%. pr… view at source ↗
Figure 8
Figure 8. Figure 8: Different slices in the y − z plane of the jet contours (grey) and tracer mass map at the time of the injection using the following criteria: ⟨∇ · v⟩ < −100 km/s/kpc and rcs > 0.8 (run A), centred at the cluster centre, and ±2, and 4 kpc away from the centre, from left to the right, respectively. The tracers are placed where the divergence is strongly negative (i.e., zones of compression) and where the com… view at source ↗
Figure 9
Figure 9. Figure 9: Temperature PDF of the tracers at time of the injection (dotted lines) and after 30 Myr (solid lines), for runs A (green), C (blue), and control (red), corresponding to ⟨∇ · v⟩ lower than −100, −500 km/s/kpc and rcs > 0.8 for runs A and C, and ⟨∇ · v⟩ > 0 without the rcs cut for the control run. Our conditions on velocity divergence and compressive ratio successfully identify regions where cold clumps form… view at source ↗
Figure 10
Figure 10. Figure 10: Hot gas mass, i.e. with temperatures higher than T > 107 K, that has ⟨∇·v⟩ < −500 km/s/kpc and rcs > 0.8 (red line) and tcool/tff < 3 (blue line) computed for a sample of 5 kpc radius regions in the cluster core for t = 1.3−2.5 Gyr, computed against the total velocity dispersion of each region normalized to its mass-weighted average sound speed. the moment of the injection (dotted lines) and the time of t… view at source ↗

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Forward citations

Cited by 3 Pith papers

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

  1. XMAGNET -- Stir before serving: a Lagrangian perspective on mixing-driven condensation in the intracluster medium

    astro-ph.GA 2026-05 unverdicted novelty 6.0

    Lagrangian tracers show mixing with low-entropy seeds drives most condensation in cluster cores; magnetic fields cause earlier divergence, higher vorticity, lower Mach numbers, and slower cold-cloud motion via tension.

  2. XMAGNET -- Stir before serving: a Lagrangian perspective on mixing-driven condensation in the intracluster medium

    astro-ph.GA 2026-05 conditional novelty 6.0

    In cool-core cluster simulations, mixing with pre-existing cold gas, not direct radiative cooling, dominates condensation; magnetic fields stretch the pre-condensation history to ~150 Myr and brake infalling clouds th...

  3. BlackHoleWeather -- Chaotic cold accretion across the meso-scale: Variability and kinematics

    astro-ph.GA 2026-05 unverdicted novelty 5.0

    Meso-scale turbulence regulates CCA spatial transport and kinematics in galaxy group simulations but leaves innermost SMBH accretion rates similar across stormy and rainy regimes.

Reference graph

Works this paper leans on

70 extracted references · 5 linked inside Pith · cited by 2 Pith papers

  1. [1]

    K., Prochaska, J

    Armillotta, L., Fraternali, F., Werk, J. K., Prochaska, J. X., & Marinacci, F. 2017, MNRAS, 470, 114

    2017

  2. [2]

    J., & Scott, P

    Asplund, M., Grevesse, N., Sauval, A. J., & Scott, P. 2009, ARA&A, 47, 481

    2009

  3. [3]

    Balbus, S. A. & Soker, N. 1989, ApJ, 341, 611

    1989

  4. [4]

    S., Dubois, Y ., Guillard, P., et al

    Beckmann, R. S., Dubois, Y ., Guillard, P., et al. 2019, A&A, 631, A60

    2019

  5. [5]

    Black, J. H. 1981, MNRAS, 197, 553

    1981

  6. [6]

    A., Nayakshin, S., & Hobbs, A

    Bourne, M. A., Nayakshin, S., & Hobbs, A. 2014, MNRAS, 441, 3055

    2014

  7. [7]

    Bourne, M. A. & Sijacki, D. 2017, MNRAS, 472, 4707

    2017

  8. [8]

    Bourne, M. A. & Sijacki, D. 2021, MNRAS, 506, 488

    2021

  9. [9]

    A., Sijacki, D., & Puchwein, E

    Bourne, M. A., Sijacki, D., & Puchwein, E. 2019, MNRAS, 490, 343

    2019

  10. [10]

    Bourne, M. A. & Yang, H.-Y . K. 2023, Galaxies, 11, 73

    2023

  11. [11]

    A., Zubovas, K., & Nayakshin, S

    Bourne, M. A., Zubovas, K., & Nayakshin, S. 2015, MNRAS, 453, 1829

    2015

  12. [12]

    G., & Temi, P

    Brighenti, F., Mathews, W. G., & Temi, P. 2015, ApJ, 802, 118

    2015

  13. [13]

    W., Donahue, M., V oit, G

    Cavagnolo, K. W., Donahue, M., V oit, G. M., & Sun, M. 2008, The Astrophysical Journal, 683, L107

    2008

  14. [14]

    2004, MN- RAS, 347, 29

    Churazov, E., Forman, W., Jones, C., Sunyaev, R., & Böhringer, H. 2004, MN- RAS, 347, 29

    2004

  15. [15]

    & Sijacki, D

    Curtis, M. & Sijacki, D. 2015, MNRAS, 454, 3445

    2015

  16. [16]

    Das, H. K. & Gronke, M. 2024, MNRAS, 527, 991

    2024

  17. [17]

    E., O’Connell, R

    Donahue, M., de Messières, G. E., O’Connell, R. W., et al. 2011, ApJ, 732, 40

    2011

  18. [18]

    2011, MNRAS, 417, 1853

    Dubois, Y ., Devriendt, J., Teyssier, R., & Slyz, A. 2011, MNRAS, 417, 1853

    2011

  19. [19]

    2023, MN- RAS, 518, 4622

    Ehlert, K., Weinberger, R., Pfrommer, C., Pakmor, R., & Springel, V . 2023, MN- RAS, 518, 4622

    2023

  20. [20]

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

    2012

  21. [21]

    C., Ferland, G

    Fabian, A. C., Ferland, G. J., Sanders, J. S., et al. 2022, MNRAS, 515, 3336

    2022

  22. [22]

    C., Johnstone, R

    Fabian, A. C., Johnstone, R. M., Sanders, J. S., et al. 2008, Nature, 454, 968

    2008

  23. [23]

    C., Walker, S

    Fabian, A. C., Walker, S. A., Russell, H. R., et al. 2016, MNRAS, 461, 922

    2016

  24. [24]

    2015, ApJ, 813, 117

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

    2015

  25. [25]

    2019, ApJ, 879, 103

    Fogarty, K., Postman, M., Li, Y ., et al. 2019, ApJ, 879, 103

    2019

  26. [26]

    W., & O’Shea, B

    Fournier, M., Grete, P., Brüggen, M., Glines, F. W., & O’Shea, B. W. 2024, arXiv e-prints, arXiv:2406.05044

    Pith/arXiv arXiv 2024

  27. [27]

    2025, arXiv e-prints, arXiv:2507.00126

    Fujita, Y ., Fukushima, K., Sato, K., Fukazawa, Y ., & Kondo, M. 2025, arXiv e-prints, arXiv:2507.00126

    Pith/arXiv arXiv 2025

  28. [28]

    2023, Frontiers in Astronomy and Space Sciences, 10, 1138613

    Ganguly, S., Li, Y ., Olivares, V ., et al. 2023, Frontiers in Astronomy and Space Sciences, 10, 1138613

    2023

  29. [29]

    2013, Astronomische Nachrichten, 334, 394

    Gaspari, M., Brighenti, F., & Ruszkowski, M. 2013, Astronomische Nachrichten, 334, 394

    2013

  30. [30]

    2015, A&A, 579, A62

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

    2015

  31. [31]

    L., et al

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

    2018

  32. [32]

    2012, ApJ, 746, 94

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

    2012

  33. [33]

    B., et al

    Gendron-Marsolais, M., Hlavacek-Larrondo, J., Martin, T. B., et al. 2018, MN- RAS, 479, L28

    2018

  34. [34]

    2025, arXiv e-prints, arXiv:2510.03552

    Ghosh, R., Gronke, M., Sharma, P., & Dutta, A. 2025, arXiv e-prints, arXiv:2510.03552

    Pith/arXiv arXiv 2025

  35. [35]

    L., McNamara, B

    Gingras, M.-J., Coil, A. L., McNamara, B. R., et al. 2024, arXiv e-prints, arXiv:2404.02212

    Pith/arXiv arXiv 2024

  36. [36]

    Gronke, M. & Oh, S. P. 2018, MNRAS, 480, L111

    2018

  37. [37]

    2006, MNRAS, 373, L65 Hitomi Collaboration, Aharonian, F., Akamatsu, H., et al

    Heinz, S., Brüggen, M., Young, A., & Levesque, E. 2006, MNRAS, 373, L65 Hitomi Collaboration, Aharonian, F., Akamatsu, H., et al. 2016, Nature, 535, 117

    2006

  38. [38]

    2022, in Handbook of X-ray and Gamma-ray Astrophysics, ed

    Hlavacek-Larrondo, J., Li, Y ., & Churazov, E. 2022, in Handbook of X-ray and Gamma-ray Astrophysics, ed. C. Bambi & A. Sangangelo, 5

    2022

  39. [39]

    C., & Merklein, J

    Iapichino, L., Schmidt, W., Niemeyer, J. C., & Merklein, J. 2011, MNRAS, 414, 2297

    2011

  40. [40]

    S., Sijacki, D., & Dubois, Y

    Jennings, F., Beckmann, R. S., Sijacki, D., & Dubois, Y . 2023, MNRAS, 518, 5215

    2023

  41. [41]

    R., Bryan, G

    Joung, M. R., Bryan, G. L., & Putman, M. E. 2012, ApJ, 745, 148

    2012

  42. [42]

    I., McKee, C

    Klein, R. I., McKee, C. F., & Colella, P. 1994, ApJ, 420, 213

    1994

  43. [43]

    R., Coil, A

    Li, M., McNamara, B. R., Coil, A. L., et al. 2025, ApJ, 984, 22

    2025

  44. [44]

    L., Ruszkowski, M., et al

    Li, Y ., Bryan, G. L., Ruszkowski, M., et al. 2015, ApJ, 811, 73

    2015

  45. [45]

    1999, Astronomische Nachrichten, 320, 193

    Loewenstein, M. 1999, Astronomische Nachrichten, 320, 193

    1999

  46. [46]

    1987, ApJ, 319, 632

    Malagoli, A., Rosner, R., & Bodo, G. 1987, ApJ, 319, 632

    1987

  47. [47]

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

    2012

  48. [48]

    McNamara, B. R. & Nulsen, P. E. J. 2007, ARA&A, 45, 117

    2007

  49. [49]

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

    2012

  50. [50]

    R., Russell, H

    McNamara, B. R., Russell, H. R., Nulsen, P. E. J., et al. 2016, ApJ, 830, 79

    2016

  51. [51]

    2019, A&A, 631, A22

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

    2019

  52. [52]

    L., et al

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

    2022

  53. [53]

    2016, MNRAS, 455, 1134

    Pakmor, R., Springel, V ., Bauer, A., et al. 2016, MNRAS, 455, 1134

    2016

  54. [54]

    B., Hollenbach, D., Beckwith, S., et al

    Pollack, J. B., Hollenbach, D., Beckwith, S., et al. 1994, ApJ, 421, 615

    1994

  55. [55]

    2015, ApJ, 811, 108

    Prasad, D., Sharma, P., & Babul, A. 2015, ApJ, 811, 108

    2015

  56. [56]

    A., McNamara, B

    Pulido, F. A., McNamara, B. R., Edge, A. C., et al. 2018, ApJ, 853, 177

    2018

  57. [57]

    Qiu, Y ., Bogdanovi´c, T., Li, Y ., McDonald, M., & McNamara, B. R. 2020, Nature Astronomy, 4, 900

    2020

  58. [58]

    2005, MNRAS, 359, 1041

    Rebusco, P., Churazov, E., Böhringer, H., & Forman, W. 2005, MNRAS, 359, 1041

    2005

  59. [59]

    2025, ApJ, 989, 156

    Reefe, M., McDonald, M., Chatzikos, M., et al. 2025, ApJ, 989, 156

    2025

  60. [60]

    2008, A&A, 477, L33

    Revaz, Y ., Combes, F., & Salomé, P. 2008, A&A, 477, L33

    2008

  61. [61]

    P., Katz, H

    Rey, M. P., Katz, H. B., Cameron, A. J., Devriendt, J., & Slyz, A. 2024, MNRAS, 528, 5412

    2024

  62. [62]

    L., Hlavacek-Larrondo, J., Gendron-Marsolais, M.-L., et al

    Rhea, C. L., Hlavacek-Larrondo, J., Gendron-Marsolais, M.-L., et al. 2025, AJ, 169, 203 Salomé, P., Combes, F., Revaz, Y ., et al. 2011, A&A, 531, A85

    2025

  63. [63]

    2007, MNRAS, 380, 877

    Sijacki, D., Springel, V ., Di Matteo, T., & Hernquist, L. 2007, MNRAS, 380, 877

    2007

  64. [64]

    D., Bryan, G

    Smith, B. D., Bryan, G. L., Glover, S. C. O., et al. 2016, Grackle: Chemistry and radiative cooling library for astrophysical simulations, Astrophysics Source Code Library, record ascl:1612.020

    2016

  65. [65]

    2025, A&A, 697, A138

    Sotira, S., Vazza, F., & Brighenti, F. 2025, A&A, 697, A138

    2025

  66. [66]

    2010, MNRAS, 401, 791

    Springel, V . 2010, MNRAS, 401, 791

    2010

  67. [67]

    & Hernquist, L

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

    2003

  68. [68]

    Vernaleo, J. C. & Reynolds, C. S. 2006, ApJ, 645, 83 V oit, G. M. & Donahue, M. 2015, ApJ, 799, L1 V oit, G. M., Meece, G., Li, Y ., et al. 2017, ApJ, 845, 80

    2006

  69. [69]

    P., & Yang, H

    Wang, C., Ruszkowski, M., Pfrommer, C., Oh, S. P., & Yang, H. Y . K. 2021, MNRAS, 504, 898 XRISM Collaboration, Audard, M., Awaki, H., et al. 2025a, arXiv e-prints, arXiv:2505.06533 XRISM Collaboration, Audard, M., Awaki, H., et al. 2025b, ApJ, 982, L5

    Pith/arXiv arXiv 2021

  70. [70]

    Yang, H. Y . K. & Reynolds, C. S. 2016, ApJ, 829, 90 Article number, page 12 of 13 S. Sotira et al.: Jet-triggered cold gas formation Appendix A: Table of the re-runs with tracers runYrunZrunArunBrunCrunDrunErunFrunGcontrol control2 rcs >0.8>0.8>0.8>0.8>0.8- - - - - - ⟨∇·v⟩ ≤−10≤−50≤−100≤−200≤−500≤−50≤−100≤−200≤−500>0≥+100 Min [109 M⊙] 51 15.5 8 5 2 28.4 ...

    2016