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REVIEW 3 major objections 6 minor 108 references

Large-scale dual AGN in large-scale cosmological hydrodynamical simulations

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Nine cosmological simulations place the dual-AGN peak at cosmic noon and find too few such systems in the local universe.

desk verdict A solid, transparent multi-simulation census of dual AGN, but the z=0 deficit claim needs a matched selection comparison before it becomes a secure result. read the letter →

arxiv 2411.15297 v1 pith:GMKJW2Q5 submitted 2024-11-22 astro-ph.CO astro-ph.GA

Clara Puerto-Sánchez , Melanie Habouzit , Marta Volonteri , Yueying Ni , Adi Foord , Daniel Anglés-Alcázar , Nianyi Chen , Paloma Guetzoyan
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Romeel Davé Tiziana Di Matteo Yohan Dubois Michael Koss Yetli Rosas-Guevara
This is my paper · ORCID
classification astro-ph.COastro-ph.GA
keywords dualactivegalacticnucleimassiveblackholescosmologicalhydrodynamicsimulationsAGNluminosityfunctiongalaxymergersX-raysurveysgravitationalwaveprecursorscosmicnoon
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 how many dual active galactic nuclei—two accreting massive black holes in separate galaxies, each shining above $10^{43}$ erg/s and separated by no more than 30 proper kiloparsecs—exist across cosmic time. Pulling together nine large cosmological hydrodynamic simulations with different black-hole seeding and feedback recipes, it argues that these systems are rare, making up 0 to 6 percent of all AGN, and that their number density peaks at z ≈ 1–3, just before the cosmic peaks of star formation and AGN activity. At a fixed redshift the simulated DAGN density spans two orders of magnitude, but every simulation agrees on the timing of the peak. It would matter because DAGN are the electromagnetic counterparts that could be caught before massive black holes coalesce, so a reliable census tells future X-ray and gravitational-wave observatories where and when to look; the same simulations produce too few DAGN at z = 0 compared with local surveys, suggesting either overly fast merging or missing physics.

What carries the argument

The carrying definition is a three-part selection rule: a pair of massive black holes each with bolometric luminosity $L_{\rm bol}\ge 10^{43}\,\mathrm{erg\,s^{-1}}$, three-dimensional separation $d\le 30\,\mathrm{pkpc}$, and members residing in distinct galaxies. The distinct-galaxy clause is what lets the nine simulations—which treat black-hole dynamics very differently, from repositioning black holes to galactic centres to modelling dynamical friction—be compared on equal footing. Bolometric luminosities are computed from accretion rates via $L_{\rm bol}=0.1\,\dot{M}_{\rm MBH} c^2$, with a fixed bolometric correction to the 2–10 keV band for the X-ray detectability forecasts.

What would settle it

A volume-complete, arcsecond-resolution X-ray survey at z ≈ 2–3 that counts AGN pairs with $L_{\rm bol}\ge 10^{43}\,\mathrm{erg\,s^{-1}}$ and separation $\le 30\,\mathrm{pkpc}$ could settle it: the simulations predict about $10^{-5}$ to $10^{-3}\,\mathrm{cMpc^{-3}}$, so a survey that should contain dozens but finds none would falsify the peak-density prediction, and a detection rate an order of magnitude higher would falsify the current upper limits.

Watch

Extended reading notes

Core claim

On the simulations' own terms, the paper establishes three things. First, DAGN with Lbol ≥ $10^{43}$ erg/s in distinct galaxies and d ≤ 30 pkpc have number densities from about $10^{-8}$ (or zero) to $10^{-3}$ $cMpc^{-3}$ over z = 0–7, with fractions of AGN in pairs of 0–6 percent. Second, all nine simulations place the peak of that population at z = 1–3, slightly earlier than the peak of AGN activity, and the typical DAGN separation grows with time while the median black-hole mass grows with time. Third, the simulated z = 0 DAGN fractions fall below the values reported by local AGN-pair surveys, while the z ≈ 3 predictions are consistent with current upper limits; the paper interprets this as simulations either producing too few pairs or merging them too quickly. The paper also translates the simulated populations into predicted detections for upcoming X-ray telescopes and argues that a large fraction of the DAGN it finds in EAGLE would fall in the LISA gravitational-wave detection region.

Load-bearing premise

The load-bearing premise is that the simulated AGN populations are reliable enough for pair counting even though their luminosity functions are not calibrated and are known to overproduce faint AGN in low-mass galaxies at high redshift; if that overproduction dominates the DAGN counts, the predicted densities, the z = 1–3 peak, and the local deficit would be artefacts of the subgrid physics rather than real astrophysics.

Editorial extensions

If this is right

  • The predicted DAGN peak at z ≈ 1–3 means deep surveys should target cosmic noon: the AXIS Deep field is expected to recover about 10^-5 to 10^-3 cMpc^-3 of these systems at z = 2–3.
  • If the simulated z = 0 shortfall is correct, local AGN-pair surveys are detecting systems that current feedback and merging recipes cannot reproduce, pointing to missing merger delays or overly prompt numerical merging.
  • Restricting to bright, massive-MBH DAGN, as many observational samples do, changes both the fraction and its redshift evolution, so survey selection must be matched before comparing theory with data.
  • At least 75 percent of the EAGLE DAGN identified by the paper fall in the LISA signal-to-noise region, meaning a dedicated electromagnetic survey of such systems could flag gravitational-wave progenitor candidates.

Reading between the lines

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

  • Beyond the paper: if the z = 0 deficit is physical rather than a luminosity-function artefact, it implies the subgrid models merge massive black holes too promptly; a direct test would be to rerun one simulation with redshift-dependent dynamical-friction delays and see whether the local DAGN fraction rises to the observed value.
  • Beyond the paper: the two-order-of-magnitude spread in predicted densities makes DAGN a sharper diagnostic of subgrid AGN physics than the overall AGN luminosity function, so matching a single upcoming DAGN survey could break degeneracies between seeding mass, accretion efficiency, and feedback.
  • Beyond the paper: because the simulations only treat pairs in distinct galaxies, the consensus peak at z = 1–3 may underestimate the true DAGN abundance once same-galaxy pairs at separations below about 5 pkpc, which current surveys cannot resolve, are included; high-resolution zoom-in simulations could test this.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. This paper uses nine published large-scale cosmological hydrodynamical simulations (Illustris, TNG50/100/300, Horizon-AGN, EAGLE, SIMBA, BlueTides, and Astrid) to compute the number density, fraction, host-galaxy and MBH properties, and X-ray detectability of dual AGN, defined as two AGN with Lbol >= 1e43 erg/s in distinct galaxies with three-dimensional separation <= 30 pkpc. The main quantitative results are a DAGN number density spanning roughly 1e-8 to 1e-3 cMpc^-3 over z = 0-7, a peak in DAGN number density and fraction at z = 1-3, a claimed deficit of simulated DAGN at z = 0 relative to the constraints of Liu et al. (2011) and Koss et al. (2012), consistency with the Sandoval et al. (2023) upper limit at z ~ 3, and forecasts that future X-ray observatories, especially AXIS, should detect from a few to more than a hundred DAGN depending on survey and simulation.

Significance. The paper is a useful census that applies a uniform DAGN definition across nine simulations and systematically explores the sensitivity of the results to selection cuts in stellar mass, MBH mass, luminosity, and separation. Its qualitative conclusions, such as a DAGN peak at z = 1-3 and fractions of order a few percent, are likely robust to several modeling choices, and the AXIS/Athena detection forecasts with cosmic-variance estimates are a valuable forward-looking element. The authors are transparent about the uncalibrated nature of the simulated AGN luminosity functions and about the dependence of the results on definitions. However, the headline comparison at z = 0 is currently weakened by a mismatch between the simulated selection (3D separation, Lbol >= 1e43 erg/s) and the observed selections (projected separation, X-ray or emission-line thresholds), and the absolute number densities remain conditioned on the AGN luminosity-function normalizations of the host simulations. These issues need to be addressed before the quantitative claims, especially the claimed z = 0 deficit, can be taken at face value.

major comments (3)
  1. [Sec. 3.2.1 and Fig. 5 (also Abstract and Sec. 6)] The claim that all simulations produce too few DAGN at z = 0 is not yet securely established, because the simulated and observed samples are selected differently. The simulation selection uses a 3D separation d <= 30 pkpc and Lbol >= 1e43 erg/s, whereas Liu et al. (2011) use projected separations of 3.5-30 kpc with optical emission-line AGN selection and Koss et al. (2012) use L2-10keV > 1e42 erg/s at z < 0.05. Using Eq. (3), Lbol = 1e43 erg/s corresponds to Lx ~ 7.8e41 erg/s, so the simulated sample contains AGN fainter than the Koss threshold; conversely, a projected-separation cut can include pairs whose true 3D separation exceeds 30 pkpc. These two effects act in opposite directions, so the net bias on the simulated DAGN fraction is unknown. The analogous statement in the Fig. 6 caption that using 3D rather than projected separation 'does not impact the results here' is not supported by a calculation for the z = 0 constraints. Please recompute the DAGN fractions with projected separations and with the luminosity thresholds of the comparison samples, accounting for the redshift window of each survey, and report both the matched and unmatched values.
  2. [Sec. 3.1, Sec. 2.2, and Table A1] The absolute DAGN number densities are outputs of AGN populations whose luminosity functions are not calibrated and, as the paper notes in Sec. 2.2, overproduce faint AGN at z >= 4 relative to observational constraints (Habouzit et al. 2022). The 1e-8 to 1e-3 cMpc^-3 range quoted in the Abstract therefore mixes DAGN physics with AGN normalization differences between the simulations. The DAGN fraction partly cancels this normalization, but the denominator (total number of AGN) is itself affected by the overproduction of low-luminosity AGN in low-mass galaxies, so the fraction is not fully immune. Please add a robustness test, for example by reweighting or restricting the simulated AGN to the luminosity and host-stellar-mass range where each simulation's AGN luminosity function is in better agreement with observations, and state which of the paper's quantitative conclusions survive. At minimum, the high-redshift (z >= 4) DAGN densities should be explicitly labeled as conditional on the uncalibrated AGN luminosity function.
  3. [Sec. 3.2.1 and Fig. 5] The three fraction definitions shown in the rows of Fig. 5 are not all matched to the definitions used by the plotted observational constraints, yet the same observational points are repeated in every panel. Liu et al.'s 1.85% includes a correction for SDSS spectroscopic incompleteness and a projected-separation window of 3.5-30 kpc, while the simulation curves use 3D separations and no incompleteness correction; Koss et al.'s fraction is based on an X-ray-selected parent sample. Repeating the observational values in all panels makes the comparison look more exact than it is. Please place each observational constraint only in the panel(s) whose definition it matches, or state explicitly in the caption and text which rows are not directly comparable.
minor comments (6)
  1. [Abstract and Sec. 3.1.1] The minimum DAGN number density is quoted as 1e-8 cMpc^-3 in the Abstract and Conclusions but as 1e-7 cMpc^-3 in Sec. 3.1.1; unify these values after deciding whether the zero-DAGN cases are included in the quoted range.
  2. [Fig. B1] The label 'Koos et al. 2012' should read 'Koss et al. 2012'.
  3. [Title page and running text] There are several LaTeX and formatting artifacts, including 'MN L ATEX style file' on the title page and 'T able 1' and 'T able A1' in the text; these should be cleaned before final submission.
  4. [Sec. 3.1.1] In the paragraph discussing EAGLE, 'EAGLE MHBs' should be 'EAGLE MBHs'.
  5. [Sec. 3.2.1 and Fig. 5] The observed points from Liu et al. and Koss et al. are plotted without error bars, while the text notes that the observational error bars can be large and that the values are sensitive to definitions; please either add error bars or clearly state that the plotted values are central estimates.
  6. [Sec. 5.1] The detectability forecast discusses DAGN with separations larger than 30 pkpc for Athena, but the fiducial DAGN definition in the rest of the paper is d <= 30 pkpc; please clarify that this part intentionally extends the separation range.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the DAGN predictions are outputs of independent, previously published simulations, compared against external observational constraints; self-citations are contextual and not load-bearing.

full rationale

The paper's derivation chain is self-contained with respect to circularity. DAGN are defined in Section 2.3 as AGN with Lbol >= 1e43 erg/s and 3D separation <= 30 pkpc in distinct galaxies, applied to nine published simulation outputs. The luminosity computation (Eqs. 2-3) uses a fixed radiative efficiency and an external bolometric correction from Shen et al. (2020); no DAGN observational dataset is used to set any parameter. The choice epsilon_r = 0.1 is a physics assumption matched to AGN luminosity functions, not to the target DAGN statistics, so the DAGN number densities and fractions remain genuine outputs rather than fits. The comparisons in Section 3.2.1 and Figures 5-6 confront these outputs with external constraints (Liu et al. 2011; Koss et al. 2012; Sandoval et al. 2023; Silverman et al. 2020), making the claimed z = 0 deficit a falsifiable prediction. The self-citations (Habouzit et al. 2021, 2022; Volonteri et al. 2022; Rosas-Guevara et al. 2019; Chen et al. 2023) are used for methodology transfer, AGN luminosity-function context, and consistency checks, not as a uniqueness theorem or as the sole support for the central predictions; removing them would not collapse the argument. The projected-versus-3D separation mismatch noted in the Fig. 6 caption and relevant to the z = 0 comparison is a selection-effect concern for the observational comparison, but it is not a circular reduction of the predictions to the inputs.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

No new entities are introduced. The analysis relies on existing simulation outputs and empirical scaling relations, with the free parameters listed above being the main hand-chosen inputs.

free parameters (4)
  • Radiative efficiency epsilon_r = 0.1
    Used in Eq. 2 to convert MBH accretion rate to bolometric luminosity; chosen to match observed AGN luminosity functions; directly sets the Lbol >= 1e43 erg/s AGN selection.
  • Shen et al. bolometric correction parameters alpha1, alpha2, beta1, beta2 = 12.60, 4.073, 0.278, -0.026
    Eq. 3 converts Lbol to hard X-ray luminosity for detectability forecasts; fitted to observed AGN SEDs in Shen et al. 2020.
  • XRB scaling relation parameters = 10^29.15, 10^39.73, slopes 1 and 1.3
    Eq. 4 estimates galaxy-wide X-ray binary emission for the contamination cut; fitted to local Universe data and extrapolated to high z.
  • Selection thresholds: Lbol >= 1e43 erg/s, d <= 30 pkpc, M* >= 1e9 Msun = as stated
    Defines DAGN to match observability; results depend strongly on these cuts (Fig. 7, Fig. B1), so they are free choices.
assumptions (3)
  • domain assumption AGN populations in the nine simulations are reliable enough for DAGN statistics despite known luminosity function discrepancies
    Central to interpreting the spread and redshift evolution as physical; the paper itself notes overproduction of faint AGN at z ~ 4 in Section 2.2.
  • domain assumption Simulated 3D separations map to projected separations without large bias
    Used to compare against observed DAGN and to apply angular resolution cuts; projection can boost counts by a factor ~2 (Section 5.1).
  • domain assumption Central/satellite classifications from different group catalogs are comparable
    Used in Table 2 and Section 4.3; the groups use different halo finders.

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Cite this review

Pith. "Pith review of Large-scale dual AGN in large-scale cosmological hydrodynamical simulations." pith.science (2026). https://pith.science/paper/GMKJW2Q5

@misc{pith2026241115297,
  author       = {Pith},
  title        = {Pith review of: Large-scale dual AGN in large-scale cosmological hydrodynamical simulations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GMKJW2Q5}},
  note         = {Machine review of arXiv:2411.15297}
}
read the original abstract

Detecting dual active galactic nuclei (DAGN) in observations and understanding theoretically which massive black holes (MBHs) compose them and in which galactic and large-scale environment they reside are becoming increasingly important questions as we enter the multi-messenger era of MBH astronomy. This paper presents the abundance and properties of DAGN produced in nine large-scale cosmological hydrodynamical simulations. We focus on DAGN powered by AGN with Lbol > 1e43 erg/s and belonging to distinct galaxies, i.e. pairs that can be characterised with current and near-future electromagnetic observations. We find that the number density of DAGN separated by a few to 30 proper kpc varies from 1e-8 (or none) to 1e-3 comoving Mpc^3 in the redshift range z=0-7. At a given redshift, the densities of the DAGN numbers vary by up to two orders of magnitude from one simulation to another. However, for all simulations, the DAGN peak is in the range z=1-3, right before the peak of cosmic star formation or cosmic AGN activity. The corresponding fractions of DAGN (with respect to the total number of AGN) range from 0 to 6 percent. We find that simulations could produce too few DAGN at z=0 (or merge pairs too quickly) compared to current observational constraints while being consistent with preliminary constraints at high redshift (z = 3). Next-generation observatories (e.g., AXIS) will be of paramount importance to detect DAGN across cosmic times. We predict the detectability of DAGN with future X-ray telescopes and discuss DAGN as progenitors for future LISA gravitational wave detections.

Figures

Figures reproduced from arXiv: 2411.15297 by the authors.

Figure 1
Figure 1. Map of Illustris dark matter density combined with stellar composite images with a depth of 10 Mpc; a light scatter plot shows the full population of galaxies. The location of the DAGN identified at z = 0 in Illustris and TNG100 is shown with pink and yellow symbols, respectively. The two simulations share the same initial conditions and, therefore, have similar large-scale structures. The differences in the subgrid… view at source ↗
Figure 2
Figure 2. Top panels: Number density of DAGN as a function of redshift. DAGN are defined as AGN with Lbol ⩾ 1043 erg/s separated by ⩽ 30 pkpc. Shaded regions indicate Poisson errors. We apply different mass cuts: only galaxies with M⋆ ⩾ 109 , 109.5 , 1010 M⊙ are considered on the left, central, right panels, respectively. Bottom panels: Fraction of DAGN, defined as the number of DAGN divided by the total number of AGN with th… view at source ↗
Figure 3
Figure 3. Number density of AGN (dashed lines) and DAGN (solid lines, identical to [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (15 more)
Figure 4
Figure 4. Figure 4: Same as [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Comparison with the observational constraints from Liu et al. (2011); Koss et al. (2012) and upper limit from Sandoval et al. (2023); we report the constraints in all panels no matter the M⋆ cuts used for the simulations. Top row: Same definition of DAGN fraction as in…
Figure 6
Figure 6. Figure 6: Comparison with the observational constraint from the Subaru/HSC program of Silverman et al. (2020, Subaru/HSC program, shaded region represents the 1σ confidence interval). The fraction represents the number of AGN involved in dual or multiple-AGN systems divided by t…
Figure 7
Figure 7. Figure 7: Same as [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: Top panel: Fractions of DAGN produced in cosmological simulations for MBHs with MMBH ⩾ 107 M⊙ in galaxies of M⋆ ⩾ 109 M⊙. Bottom panels: DAGN fractions for the full redshift range (left panel), for d = 5 − 30 kpc (middle panel, almost identical to the left panel), and …
Figure 9
Figure 9. Figure 9: Normalized distribution of the 3-dimensional separations of DAGN (coloured histograms) and DAGN with MMBH ⩾ 107 M⊙ (grey filled distributions) for redshift z = 4, 2, 0. Poisson error bars have been included on top of the bar plots. Each distribution was normalised by t…
Figure 10
Figure 10. Figure 10: Normalized distribution of DAGN (coloured histograms) and AGN (grey distributions) as a function of their MBH mass for z = 4, 2, 0. Only galaxies with M⋆ ⩾ 109 M⊙ are considered, and AGN with Lbol ⩾ 1043 erg/s. Poisson error bars are included on top of the bar plots. …
Figure 11
Figure 11. Figure 11: Normalized distribution of DAGN (coloured histograms) and AGN (grey distributions) as a function of their host galaxy stellar mass for z = 4, 2, 0. Same M⋆, Lbol selection as previous figures. Poisson error bars are included on top of the bar plots. Each distribution …
Figure 12
Figure 12. Figure 12: Normalized distributions of DAGN (coloured histograms) and AGN (grey filled distributions) as a function of MMBH/M⋆ ratios at z = 0. Same M⋆, Lbol selection as previous figures. Arrows indicate the median value of each distribution. Poisson error bars are included on …
Figure 13
Figure 13. Figure 13: Distribution of DAGN (coloured histograms) and AGN (grey filled distributions) bolometric luminosities for z = 4, 2, 0, normalized to the total number of objects in the histograms. Same M⋆, Lbol selection as previous figures. Arrows indicate the median of the distribu…
Figure 14
Figure 14. Figure 14: MMBH − M⋆ diagram at z = 2. Left panels: MBHs in DAGN are joined with coloured lines. Central panels: All DAGN are shown as dots labelled as primary (most massive, blue) or secondary (less massive, red) MBHs with contour levels overlaid on top. Right panels: As in the…
Figure 15
Figure 15. Figure 15: DAGN (and candidates) identified in observations up to z ∼ 3 (different luminosity thresholds have been used for these studies); adapted from Chen et al. (2022c). Future X-ray telescopes will resolve separations of a hundred kpc down to a few kpc. Theoretical separati…
Figure 16
Figure 16. Figure 16: Upper panels: Number density of DAGN as a function of redshift for next-generation X-ray telescopes (for the Wide and Deep surveys of Athena, and the Medium and Deep surveys of AXIS). Only the telescopes’ flux sensitivity is taken into account (Lx ⩾ Lthres, see a) of …
Figure 17
Figure 17. Figure 17: Illustration of the effect of the cosmic variance in detecting DAGN. Several random realisations of the AXIS Deep survey (0.13 deg2 ) are shown with orange squares. DAGN detections in these fields are shown with red points for the different simulations at z = 2 (z = 3…
Figure 18
Figure 18. Figure 18: Growth history of 3 EAGLE MBHs identified as two DAGN (salmon, orange, and white lines). The seeding mass used in EAGLE is shown as a vertical white dotted line at MMBH ∼ 105 M⊙. The mergers of these two DAGN are shown with black diamonds with edge colours (z = 2 and …

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Works this paper leans on

108 extracted references · 70 canonical work pages

  1. [1]

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

    ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 'mid.sentence := #2 '...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in " " * FUNCTION format....

  3. [3]

    Aird J. et al. , 2010, , 401, 2531

    2010

  4. [4]

    Amaro-Seoane P. et al. , 2023, Living Reviews in Relativity, 26, 2

    2023

  5. [5]

    F., 2017 a , , 464, 2840

    Angl \'e s-Alc \'a zar D., Dav \'e R., Faucher-Gigu \`e re C.-A., \"O zel F., Hopkins P. F., 2017 a , , 464, 2840

    2017

  6. [6]

    F., Feldmann R., Torrey P., Wetzel A., Kere s D., 2017 b , , 472, L109

    Angl \'e s-Alc \'a zar D., Faucher-Gigu \`e re C.-A., Quataert E., Hopkins P. F., Feldmann R., Torrey P., Wetzel A., Kere s D., 2017 b , , 472, L109

    2017

  7. [7]

    Angl \'e s-Alc \'a zar D., \"O zel F., Dav \'e R., 2013, , 770, 5

    2013

  8. [8]

    A., Oppenheimer B

    Angl \'e s-Alc \'a zar D., \"O zel F., Dav \'e R., Katz N., Kollmeier J. A., Oppenheimer B. D., 2015, , 800, 127

    2015

Show all 108 references
  1. [9]

    Bah \'e Y. M. et al. , 2022, , 516, 167

    2022

  2. [10]

    Baron D., M \'e nard B., 2019, , 487, 3404

    2019

  3. [11]

    S., Sandberg Lacy C

    Barrows R. S., Sandberg Lacy C. H., Kennefick J., Comerford J. M., Kennefick D., Berrier J. C., 2013, , 769, 95

    2013

  4. [12]

    C., Blandford R

    Begelman M. C., Blandford R. D., Rees M. J., 1980, , 287, 307

    1980

  5. [13]

    K., Di Matteo T., Myers A

    Bhowmick A. K., Di Matteo T., Myers A. D., 2020, , 492, 5620

    2020

  6. [14]

    Bird S., Ni Y., Di Matteo T., Croft R., Feng Y., Chen N., 2022, , 512, 3703

    2022

  7. [15]

    Buchner J. et al. , 2015, , 802, 89

    2015

  8. [16]

    R., Dotti M., Volonteri M., Mayer L., Bellovary J

    Capelo P. R., Dotti M., Volonteri M., Mayer L., Bellovary J. M., Shen S., 2017, , 469, 4437

    2017

  9. [17]

    R., Volonteri M., Dotti M., Bellovary J

    Capelo P. R., Volonteri M., Dotti M., Bellovary J. M., Mayer L., Governato F., 2015, , 447, 2123

    2015

  10. [18]

    Cappelluti N. et al. , 2023, arXiv e-prints, arXiv:2311.07669

    2023 arXiv

  11. [19]

    Chen N. et al. , 2023, , 522, 1895

    2023

  12. [20]

    Chen N. et al. , 2023, Monthly Notices of the Royal Astronomical Society, stad834

    2023

  13. [21]

    Chen N. et al. , 2022 a , , 514, 2220

    2022

  14. [22]

    Chen N., Ni Y., Tremmel M., Di Matteo T., Bird S., DeGraf C., Feng Y., 2022 b , , 510, 531

    2022

  15. [23]

    L., Yang Q., Li J

    Chen Y.-C., Hwang H.-C., Shen Y., Liu X., Zakamska N. L., Yang Q., Li J. I., 2022 c , , 925, 162

    2022

  16. [24]

    Ciurlo A. et al. , 2023, , 671, L4

    2023

  17. [25]

    Civano F. et al. , 2016, VizieR Online Data Catalog, J/ApJ/819/62

    2016

  18. [26]

    M., Pooley D., Barrows R

    Comerford J. M., Pooley D., Barrows R. S., Greene J. E., Zakamska N. L., Madejski G. M., Cooper M. C., 2015, , 806, 219

    2015

  19. [27]

    M., Pooley D., Gerke B

    Comerford J. M., Pooley D., Gerke B. F., Madejski G. M., 2011, , 737, L19

    2011

  20. [28]

    Crain R. A. et al. , 2015, , 450, 1937

    2015

  21. [29]

    H., Appleby S., 2019, , 486, 2827

    Dav \'e R., Angl \'e s-Alc \'a zar D., Narayanan D., Li Q., Rafieferantsoa M. H., Appleby S., 2019, , 486, 2827

    2019

  22. [30]

    L., Graham A

    Davis B. L., Graham A. W., Cameron E., 2018, , 869, 113

    2018

  23. [31]

    M., Shiralilou B., Piana O., Choudhury T

    Dayal P., Rossi E. M., Shiralilou B., Piana O., Choudhury T. R., Volonteri M., 2019, , 486, 2336

    2019

  24. [32]

    De Rosa A. et al. , 2019, , 86, 101525

    2019

  25. [33]

    De Rosa A. et al. , 2023, , 519, 5149

    2023

  26. [34]

    Di Matteo T., Angles-Alcazar D., Shankar F., 2023, arXiv e-prints, arXiv:2304.11541

    2023 arXiv

  27. [35]

    Dubois Y., Peirani S., Pichon C., Devriendt J., Gavazzi R., Welker C., Volonteri M., 2016, , 463, 3948

    2016

  28. [36]

    A., Bird S., Battaglia N., Wilkins S., 2016, , 455, 2778

    Feng Y., Di-Matteo T., Croft R. A., Bird S., Battaglia N., Wilkins S., 2016, , 455, 2778

    2016

  29. [37]

    Foord A. et al. , 2023, arXiv e-prints, arXiv:2311.07664

    2023 arXiv

  30. [38]

    M., Hodges-Kluck E., Barrows R

    Foord A., G \"u ltekin K., Nevin R., Comerford J. M., Hodges-Kluck E., Barrows R. S., Goulding A. D., Greene J. E., 2020, , 892, 29

    2020

  31. [39]

    C., Koss M

    Foord A., G \"u ltekin K., Runnoe J. C., Koss M. J., 2021, , 907, 71

    2021

  32. [40]

    \'E ., An T., Frey S., Komossa S., Paragi Z., Hong X

    Gab \'a nyi K. \'E ., An T., Frey S., Komossa S., Paragi Z., Hong X. Y., Shen Z. Q., 2016, , 826, 106

    2016

  33. [41]

    Genel S. et al. , 2014, , 445, 175

    2014

  34. [42]

    Georgakakis A. et al. , 2015, , 453, 1946

    2015

  35. [43]

    Gilli R. et al. , 2022, , 666, A17

    2022

  36. [44]

    M., Habouzit M., Ba \ n ados E., Afonso P

    Greiner J., Bolmer J., Yates R. M., Habouzit M., Ba \ n ados E., Afonso P. M. J., Schady P., 2021, , 654, A79

    2021

  37. [45]

    Habouzit M. et al. , 2020, arXiv e-prints, arXiv:2006.10094

    2020 arXiv

  38. [46]

    Habouzit M. et al. , 2021, , 503, 1940

    2021

  39. [47]

    Habouzit M. et al. , 2022, , 509, 3015

    2022

  40. [48]

    Habouzit M., Volonteri M., Dubois Y., 2017, Monthly Notices of the Royal Astronomical Society, 468, 3935

    2017

  41. [49]

    Haidar H. et al. , 2022, , 514, 4912

    2022

  42. [50]

    Haiman Z. et al. , 2023, arXiv e-prints, arXiv:2306.14990

    2023 arXiv

  43. [51]

    Hirschmann M., Dolag K., Saro A., Bachmann L., Borgani S., Burkert A., 2014, , 442, 2304

    2014

  44. [52]

    F., Richards G

    Hopkins P. F., Richards G. T., Hernquist L., 2007, ApJ, 654, 731

    2007

  45. [53]

    Ishikawa Y. et al. , 2024, arXiv e-prints, arXiv:2403.08098

    2024 arXiv

  46. [54]

    Izquierdo-Villalba D., Sesana A., Bonoli S., Colpi M., 2022, , 509, 3488

    2022

  47. [55]

    L., Kelley L

    Katz M. L., Kelley L. Z., Dosopoulou F., Berry S., Blecha L., Larson S. L., 2020, , 491, 2301

    2020

  48. [56]

    Kaviraj S. et al. , 2017, , 467, 4739

    2017

  49. [57]

    Z., Blecha L., Hernquist L., Sesana A., Taylor S

    Kelley L. Z., Blecha L., Hernquist L., Sesana A., Taylor S. R., 2017, , 471, 4508

    2017

  50. [58]

    Kocevski D. D. et al. , 2018, VizieR Online Data Catalog, J/ApJS/236/48

    2018

  51. [59]

    Koss M., Mushotzky R., Treister E., Veilleux S., Vasudevan R., Trippe M., 2012, , 746, L22

    2012

  52. [60]

    Koss M. J. et al. , 2023, , 942, L24

    2023

  53. [61]

    Lehmer B. D. et al. , 2019, , 243, 3

    2019

  54. [62]

    Li J., Zhuang M.-Y., Shen Y., Volonteri M., Chen N., Di Matteo T., 2024, arXiv e-prints, arXiv:2405.14980

    2024 arXiv

  55. [63]

    Li Y. et al. , 2020, , 895, 102

    2020

  56. [64]

    A., Hao L., 2011, , 737, 101

    Liu X., Shen Y., Strauss M. A., Hao L., 2011, , 737, 101

    2011

  57. [65]

    Luo B. et al. , 2017, , 228, 2

    2017

  58. [66]

    Mannucci F. et al. , 2022, Nature Astronomy, 6, 1185

    2022

  59. [67]

    Mannucci F. et al. , 2023, arXiv e-prints, arXiv:2305.07396

    2023 arXiv

  60. [68]

    G., Harrison C

    McAlpine S., Bower R. G., Harrison C. M., Crain R. A., Schaller M., Schaye J., Theuns T., 2017, , 468, 3395

    2017

  61. [69]

    G., Rosario D

    McAlpine S., Bower R. G., Rosario D. J., Crain R. A., Schaye J., Theuns T., 2018, , 481, 3118

    2018

  62. [70]

    Merloni A., 2004, , 353, 1035

    2004

  63. [71]

    M., Nevin R., Barrows R

    M \"u ller-S \'a nchez F., Comerford J. M., Nevin R., Barrows R. S., Cooper M. C., Greene J. E., 2015, , 813, 103

    2015

  64. [72]

    Nandra K. et al. , 2013, arXiv e-prints, arXiv:1306.2307

    2013 arXiv

  65. [73]

    Nandra K. et al. , 2015, VizieR Online Data Catalog, J/ApJS/220/10

    2015

  66. [74]

    Ni Y. et al. , 2022, , 513, 670

    2022

  67. [75]

    Perna M. et al. , 2023 a , arXiv e-prints, arXiv:2310.03067

    2023 arXiv

  68. [76]

    Perna M. et al. , 2023 b , , 679, A89

    2023

  69. [77]

    Pfister H., Volonteri M., Dubois Y., Dotti M., Colpi M., 2019, , 486, 101

    2019

  70. [78]

    Pillepich A. et al. , 2018 a , , 473, 4077

    2018

  71. [79]

    Pillepich A. et al. , 2018 b , , 473, 4077

    2018

  72. [80]

    H., Naab T., Lah \'e n N., Sawala T., 2017, , 840, 53

    Rantala A., Pihajoki P., Johansson P. H., Naab T., Lah \'e n N., Sawala T., 2017, , 840, 53

    2017

  73. [81]

    E., Volonteri M., 2015, , 813, 82

    Reines A. E., Volonteri M., 2015, , 813, 82

    2015

  74. [82]

    Reynolds C. S. et al. , 2023, arXiv e-prints, arXiv:2311.00780

    2023 arXiv

  75. [83]

    Ricarte A., Natarajan P., 2018, , 481, 3278

    2018

  76. [84]

    G., Schaye J., McAlpine S., Dalla Vecchia C., Frenk C

    Rosas-Guevara Y., Bower R. G., Schaye J., McAlpine S., Dalla Vecchia C., Frenk C. S., Schaller M., Theuns T., 2016, , 462, 190

    2016

  77. [85]

    M., Bower R

    Rosas-Guevara Y. M., Bower R. G., McAlpine S., Bonoli S., Tissera P. B., 2019, , 483, 2712

    2019

  78. [86]

    Rosas-Guevara Y. M. et al. , 2015, , 454, 1038

    2015

  79. [87]

    G., Theuns T., McAlpine S., Schaller M., Crain R

    Salcido J., Bower R. G., Theuns T., McAlpine S., Schaller M., Crain R. A., Schaye J., Regan J., 2016, , 463, 870

    2016

  80. [88]

    Sandoval B. et al. , 2023, arXiv e-prints, arXiv:2312.02311

    2023 arXiv

  81. [89]

    Schaye J. et al. , 2015, , 446, 521

    2015

  82. [90]

    Schirra A. P. et al. , 2021, , 508, 4816

    2021

  83. [91]

    Scialpi M. et al. , 2024, , 690, A57

    2024

  84. [92]

    Sesana A., Volonteri M., Haardt F., 2007, , 377, 1711

    2007

  85. [93]

    F., Faucher-Gigu \`e re C.-A., Alexander D

    Shen X., Hopkins P. F., Faucher-Gigu \`e re C.-A., Alexander D. M., Richards G. T., Ross N. P., Hickox R. C., 2020, , 495, 3252

    2020

  86. [94]

    Shen Y. et al. , 2023 a , arXiv e-prints, arXiv:2306.15527

    2023 arXiv

  87. [95]

    Shen Y. et al. , 2023 b , , 943, 38

    2023

  88. [96]

    Silverman J. D. et al. , 2020, , 899, 154

    2020

  89. [97]

    S., Dav \'e R., 2015, , 53, 51

    Somerville R. S., Dav \'e R., 2015, , 53, 51

    2015

  90. [98]

    Thomas N., Dav \'e R., Angl \'e s-Alc \'a zar D., Jarvis M., 2019, , 487, 5764

    2019

  91. [99]

    Thomas N., Dav \'e R., Angl \'e s-Alc \'a zar D., Jarvis M., 2019, Monthly Notices of the Royal Astronomical Society, 487, 5764

    2019

  92. [100]

    J., Angles-Alcazar D., 2020, arXiv e-prints, arXiv:2010.11225

    Thomas N., Dave R., Jarvis M. J., Angles-Alcazar D., 2020, arXiv e-prints, arXiv:2010.11225

    2020 arXiv

  93. [101]

    R., 2015, , 451, 1868

    Tremmel M., Governato F., Volonteri M., Quinn T. R., 2015, , 451, 1868

    2015

  94. [102]

    Vogelsberger M. et al. , 2014, , 444, 1518

    2014

  95. [103]

    Volonteri M., Dubois Y., Pichon C., Devriendt J., 2016, , 460, 2979

    2016

  96. [104]

    Volonteri M., Pfister H., Beckmann R., Dotti M., Dubois Y., Massonneau W., Musoke G., Tremmel M., 2022, , 514, 640

    2022

  97. [105]

    Volonteri M. et al. , 2020, , 498, 2219

    2020

  98. [106]

    Weinberger R. et al. , 2018, , 479, 4056

    2018

  99. [107]

    Yue M., Fan X., Yang J., Wang F., 2021, , 921, L27

    2021

  100. [108]

    Yue M., Fan X., Yang J., Wang F., 2023, , 165, 191

    2023

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.