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A novel Bayesian approach for decomposing the radio emission of quasars: II. Link between quasar radio emission and black hole mass

T0 review · 3 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper claims that quasar radio emission links to black hole mass only for the top 20% most massive black holes, where AGN jets are 2 to 3 times more likely to be bright at fixed redshift and luminosity.

desk verdict A credible unification of the RL/RQ–BH mass debate, but the headline 0.4 dex boost rests on a fixed power-law slope and fitted parameters without error bars; referee it, and ask for a free-gamma test. read the letter →

arxiv 2501.07629 v2 pith:2ZA7A47M submitted 2025-01-13 astro-ph.GA

B.-H. Yue , K. J. Duncan , P. N. Best , M. I. Arnaudova , L. K. Morabito , J. W. Petley , H. J. A. Röttgering , S. Shenoy
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D. J. B. Smith
This is my paper · ORCID
classification astro-ph.GA
keywords quasarradioemissionblackholemassAGNjetsloudnessstarformationBayesiantwo-componentmodelLOFARSDSSquasars
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 tries to settle a long-running dispute: does the mass of a quasar's supermassive black hole control how bright it is in the radio? The authors apply a two-component Bayesian model that separates each quasar's radio light into star formation in the host galaxy and AGN jet activity, fitting the observed 150 MHz flux density distribution of 222,782 SDSS quasars observed by LOFAR. They find that black hole mass makes no difference to host-galaxy star formation at fixed redshift and bolometric luminosity, and no difference to AGN jet activity for most black hole masses. The exception is the top 20% most massive black holes, whose quasars are 2 to 3 times more likely to be radio-bright, and this excess shows up only in the roughly 5% most radio-loud quasars. If right, the result explains why previous studies disagreed: radio-loud and radio-quiet definitions that mix in star-formation-dominated sources hide the mass dependence, and a physically motivated SF/AGN classification recovers it.

What carries the argument

The carrying mechanism is the two-component Bayesian model of the radio flux density distribution introduced in the companion paper (Y24): the star-formation component is a log-Gaussian centered on the 150 MHz luminosity corresponding to a mean SFR, with scatter, and the AGN component is a power-law luminosity function with fixed slope gamma = 1.5 and normalization phi, expressed as the radio-loud fraction f. Fitting this model inside M_i-z grids and then within black-hole-mass quintiles separates mass-dependent changes in star formation from mass-dependent changes in AGN jet production. The classification thresholds L_eq, where the SF and AGN probability densities cross, and L_pl, where the AGN power law provides 95% of the total PDF, turn the fitted components into a physically motivated radio-quasar taxonomy.

What would settle it

Refit the LoTSS-SDSS radio flux density distributions with gamma as a free parameter in each black-hole-mass quintile; if the best-fit gamma shifts systematically with mass, for instance by more than about 0.1 dex between the lowest and highest quintiles, or if a broken power law fits better at the faint end, the fixed-slope assumption fails and the f boost is not a jet-likelihood effect.

Watch

Extended reading notes

Core claim

The paper's central claim is that supermassive black hole mass is not a general driver of quasar radio emission; instead, a specific subpopulation, quasars hosting the top 20% of black hole masses at given redshift and bolometric luminosity, shows an enhanced AGN contribution, with the fitted jet normalization f increased by about 0.4 dex, meaning they are 2 to 3 times more likely to be radio-bright at fixed optical luminosity. The same excess appears when quasars are classified by the physical origin of their radio emission: only AGN-dominated sources, roughly the top 5% in radio loudness, host systematically more massive black holes, while SF-dominated quasars show no mass dependence. The paper further claims that traditional radio-loud definitions contaminate the radio-loud sample with SF-dominated and intermediate sources, diluting or erasing the mass signal, and that a classification based on model-derived thresholds L_eq and L_pl reconciles previously contradictory results.

Load-bearing premise

The load-bearing premise is that the AGN radio luminosity function is a single power law with slope gamma = 1.5 at all black hole masses and luminosities probed here; if the slope bends at low luminosities or changes with black hole mass, the fitted normalization f would absorb that change and the claimed 2 to 3 times boost would not cleanly measure jet likelihood.

Editorial extensions

If this is right

  • Quasar host-galaxy star formation is independent of black hole mass at fixed redshift and bolometric luminosity, so radio emission from star formation cannot serve as a black-hole-mass indicator.
  • AGN jet activity is also mass-independent across most of the black hole mass range, with the mass signal confined to the most massive 20% of black holes.
  • The radio excess in the most massive quasars affects only the roughly 5% most radio-loud quasars at a given redshift and luminosity, namely the AGN-dominated tail.
  • Traditional radio-loud and radio-quiet definitions, whether based on flux ratios or luminosity ratios, mix in SF-dominated and intermediate sources, which explains why some studies find no black-hole-mass dependence.
  • The SF-dominated versus AGN-dominated classification reproduces earlier positive results and erases the mass difference if the top 20% of black hole masses are removed, unifying previously divergent findings.

Reading between the lines

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

  • If the fixed-slope power law for the AGN component is correct, the 0.4 dex boost in f implies that jet launching efficiency itself increases with black hole mass at the high-mass end, a testable prediction for very-long-baseline observations of jet cores in this population.
  • The classification scheme could be applied to radio-selected quasar samples at other frequencies or redshifts to check whether the top-20% mass effect is universal or specific to the LoTSS-selected population.
  • The lower CIV distance and Eddington ratios of the massive AGN-dominated quasars hint at a distinct accretion state rather than an outflow-driven artifact, so X-ray or polarimetric follow-up of this quadrant could discriminate among accretion-mode scenarios.
  • Because gamma is fixed, letting the slope vary with black hole mass would provide a direct stress test: if gamma steepens at high mass, part of the claimed boost would migrate from normalization to spectral shape.
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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 / 4 minor

Summary. This paper applies the two-component Bayesian model of Yue et al. (2024) to 222,782 SDSS DR16Q quasars with LOFAR LoTSS DR2 150 MHz measurements, binning the sample in M_i-z grids and, within each grid, by BH-mass quintile. The model separates a log-normal star-forming host component from a power-law AGN jet component. The main result is that the AGN normalization f is roughly constant across BH mass except for the top 20% quintile, where log(f/f0) increases by about 0.4 dex, implying a factor 2-3 higher probability of hosting a strong jet at fixed M_i and z. The paper then defines SF-dominated (L<Leq) and AGN-dominated (L>Lpl) classes from the model and uses BH-mass CDF comparisons to argue that traditional RL/RQ definitions mix populations, and it uses stacked Mg II spectra and CIV-distance distributions to argue that the result is not driven by outflow-induced BH-mass overestimates.

Significance. If the central claim is correct, the paper offers a quantitative resolution of the long-standing disagreement over BH mass and radio loudness: the dependence is confined to the most massive 20% of BHs and the most radio-bright 5% of quasars. The paper has several concrete strengths: a large, well-defined sample; an explicit physical decomposition rather than a threshold in radio loudness; careful exclusion of problematic CIV-based masses at z>2; a tabulated classification (Leq, Lpl, Table A1) that other studies can apply directly; and a dedicated stacked-spectrum test (Section 5) that addresses a plausible outflow-related bias. The main quantitative claim, however, is currently a readout of a model with a fixed power-law slope and is presented without posterior uncertainties, so its robustness is not yet established.

major comments (3)
  1. [Section 3.2, Eq. (4)] The AGN component is modeled as P_AGN(L)dL proportional to phi L^{-gamma} dL with gamma fixed to 1.5, and f is defined as the integral above 10^26 W/Hz. Since the fit is performed on the full flux density distribution, a change in gamma with BH mass, or a bend in the AGN luminosity function below the bright end, can be partially absorbed into the normalization phi and hence into f. The paper's only defense is 'inspection of individual fits' in Section 3.2, which is not a quantitative test. I request a free-gamma fit, or at least a systematic sensitivity test with gamma varied across quintiles, together with the joint posterior constraints on gamma and f, so that the 0.4-dex enhancement can be separated from a spectral-shape effect.
  2. [Figure 5; Section 3.2] The headline result, namely the 0.4 dex increase in log(f/f0) for the top BH-mass quintile and the corresponding factor 2-3 higher jet probability, is shown in Figure 5 without error bars, credible intervals, or a significance level. Without these, the reader cannot assess whether the top-quintile offset is significant relative to the fit uncertainties and the scatter across the M_i-z grid cells. Please report per-quintile posterior intervals and a combined significance statement, for example a posterior probability or a matched-pair test across the grid cells.
  3. [Section 4, Figures 7-9] The KS tests and CDF comparisons in Section 4 are not independent tests of the central claim, because the classification boundaries Leq and Lpl are derived from the same fixed-gamma best fits used to infer f. They demonstrate internal consistency, but they cannot break the degeneracy between f and gamma identified above. This limitation should be stated explicitly, or the classification should be validated with a procedure that does not rely on the fitted AGN power-law tail, for example a non-parametric definition of the radio-bright tail.
minor comments (4)
  1. [Section 4] The sentence defining Lpl ends with 'so that 95% of .', which is incomplete; please complete the definition.
  2. [Figure B1 caption] The caption states 'where the RQ quasars have Rflux < 10 and the RL quasars have Rflux < 10'; the second condition should be Rflux > 10 for RL quasars.
  3. [Figure 10] The quantities labeled '2 = 0.33' and '2 = 0.57' are not defined; please define them in the caption.
  4. [Section 3.2] The phrase 'inspection of individual fits' is vague; please provide a quantitative goodness-of-fit statistic for the fixed-gamma model in place of, or in addition to, the visual statement.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported radio-bright excess is the fitted parameter f itself, and the supporting CDF comparisons use independent BH-mass and radio-loudness data.

full rationale

The paper's central claim is an inference from a fitted parameter f of a two-component Bayesian model, not an out-of-sample prediction. In Section 3.2 the authors explicitly report the 0.4 dex increase in log(f) as the fitted value, so the 2-3x statement is a direct reading of the fit, which is standard statistical inference rather than a circular reduction. The fixed slope gamma=1.5 is imported from Y24, but the paper states that it comes from extrapolating the bright-end luminosity function, i.e. an external calibration that does not include the BH-mass-dependence result; if gamma were misspecified the f-enhancement could be biased, but that is a model-robustness concern, not a circularity. The Section 4 BH-mass CDF comparisons use catalogue virial BH masses and observed radio luminosities, and the KS tests compare actual distributions; the classifications Leq/Lpl depend on the model, but the BH masses do not enter the model fit, so the comparison is not forced by construction. The Section 5 stacking and CIV-distance analyses use independent SDSS spectra and catalog quantities. No step in the derivation chain equates the input to the output by definition.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The central claim rests on the two-component model and the fixed power-law slope; the BH-mass ordering assumption is conservative. No new physical entities are introduced. The model parameters f, Psi, sigma_mu are fitted, and gamma is fixed by hand from the authors' prior work.

free parameters (4)
  • f (AGN power-law normalization) = varies per grid and quintile; top quintile log f offset ~ +0.4 dex
    Central fitted parameter; the claimed radio boost is an increase in f. Fitted to the radio flux density distribution in each M_i-z grid and BH-mass quintile (Section 3.2).
  • Psi (SF mean radio luminosity / SFR) = varies; small offsets within ~0.2 dex in Figure 5
    Mean star formation component; fitted per cell and quintile; shows no significant mass dependence.
  • sigma_mu (SF log-Gaussian scatter) = 0.2-0.3 dex, <5% variation with BH mass
    Dispersion of the SF component; fitted but not discussed in depth.
  • gamma (AGN power-law slope) = fixed at 1.5
    Set from the bright-end extrapolation in Y24; not re-fitted in this paper. The result could change if gamma varies with mass.
assumptions (6)
  • domain assumption Every quasar's radio emission is the sum of a log-Gaussian SF component and a single power-law AGN component.
    Section 3.1, Eqs. (3)-(4); foundation of the decomposition; if false, the separation into SF and AGN is invalid.
  • domain assumption The AGN power-law slope gamma is fixed at 1.5 for all grids and mass bins.
    Section 3.2; needed to identify the normalization f; not tested by fitting gamma.
  • domain assumption Virial single-epoch BH masses from Wu & Shen 2022 are accurate enough to order quasars into quintiles; scatter is 0.3-0.5 dex, wider than bin width 0.25 dex.
    Footnote 2, Section 3.2; cross-contamination would weaken the trend, so this is conservative, but still load-bearing.
  • domain assumption The SFR-radio luminosity relation of Smith et al. 2021 is mass-independent and applies to quasar hosts.
    Section 3.1; used to convert Psi to SFR; not central to the AGN boost claim but underlies the SFR non-dependence result.
  • domain assumption Quasars within a given M_i-z grid cell share similar physical properties, so a single two-component model applies.
    Section 3.1; required for stacking-like population fits.
  • standard math Standard Bayesian and statistical methods (Bayes theorem, KS tests) are applicable.
    Used throughout; uncontroversial.

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Cite this review

Pith. "Pith review of A novel Bayesian approach for decomposing the radio emission of quasars: II. Link between quasar radio emission and black hole mass." pith.science (2026). https://pith.science/paper/2ZA7A47M

@misc{pith2026250107629,
  author       = {Pith},
  title        = {Pith review of: A novel Bayesian approach for decomposing the radio emission of quasars: II. Link between quasar radio emission and black hole mass},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2ZA7A47M}},
  note         = {Machine review of arXiv:2501.07629}
}
abstract

Whether the mass of supermassive black hole ($M_\mathrm{BH}$) is directly linked to the quasar radio luminosity remains a long-debated issue, and understanding the role of $M_\mathrm{BH}$ in the evolution of quasars is pivotal to unveiling the mechanism of AGN feedback. In this work, based on a two-component Bayesian model, we examine how $M_\mathrm{BH}$ affects the radio emission from quasars, separating the contributions from host galaxy star formation (SF) and AGN activity. By modelling the radio flux density distribution of Sloan Digital Sky Survey (SDSS) quasars from the LOFAR Two-metre Sky Survey Data Release 2, we find no correlation between $M_\mathrm{BH}$ and SF rate (SFR) at any mass for quasars at a given redshift and bolometric luminosity. The same holds for AGN activity across most $M_\mathrm{BH}$ values; however, quasars with the top 20\% most massive SMBHs are 2 to 3 times more likely to host strong radio jets than those with lower-mass SMBHs at similar redshift and luminosity. We suggest defining radio quasar populations by their AGN and SF contributions instead of radio loudness; our new definition unifies previously divergent observational results on the role of $M_\mathrm{BH}$ in quasar radio emissions. We further demonstrate that this radio enhancement in quasars with the 20\% most massive SMBHs affects only the $\sim5\%$ most radio bright quasars at a given redshift and bolometric luminosity. We discuss possible physical origins of this radio excess in the most massive and radio-bright quasar population, which remains an interest for future study.

Figures

Figures reproduced from arXiv: 2501.07629 by the authors.

Figure 1
Figure 1. BH mass-redshift distribution of the sample used in this work. Yellow dots mark the average measured BH mass within each redshift bin, while the error bars show the standard deviation of BH masses within the corresponding redshift bin. Average measured BH masses lie between 𝑀BH = 108 ∼ 109𝑀⊙, and maintain a good range throughout the redshift bins. The shaded area (𝑧 > 2) marks the parameter space where only C ivBH m… view at source ↗
Figure 3
Figure 3. Coverage of parameter space in M𝑖 − 𝑧 plane. The red dashed grids show the total LoTSS DR2-SDSS sample set included in Y24, while the solid orange grids show the samples used in this work: each grid hosts at least 5,000 quasars with estimated BH masses from either H 𝛽 or Mg ii measurements. The dashed orange grid cells each host more than 5,000 quasar samples but have only C iv BH mass estimations. These grid cells … view at source ↗
Figure 4
Figure 4. Radio flux density distribution and the corresponding model best-fit in one of the representative M𝑖 − 𝑧 bins explored in this study. The left and middle panels show the distribution and fitted relation for the entire quasar population within the grid, in log and linear scales respectively. The orange dotted line and green dashed line represent the SF component from the host galaxy activity (log Gaussian) and the je… view at source ↗
Figures from the paper (8 more)
Figure 5
Figure 5. Figure 5: The evolution of SF and AGN contributions to the quasar radio flux density distribution with BH mass, as measured from the model best-fits using quasar samples split by BH mass percentiles. Within each M𝑖 − 𝑧 grid cell, the BH masses are divided into 5 quintiles (0% - …
Figure 6
Figure 6. Figure 6: The distribution of AGN-dominated (light orange) and SF-dominated (dark green) quasars under our new model-driven classification in the 𝑅lum − 𝑀BH space, where 𝑅lum is the radio loudness defined as 𝑅lum = log10 (𝐿150MHz/𝐿𝑖 ). With characteristic luminosity defined as 𝐿…
Figure 7
Figure 7. Figure 7: Cumulative mass distributions of AGN- (light orange) and SF-dominated (dark green) quasars defined in this work within each M𝑖 − 𝑧 grid. The grid cells are arranged by their average i band luminosity and redshift - grid cells with fainter luminosity are placed towards …
Figure 8
Figure 8. Figure 8: Difference in the BH mass CDF between RL/RQ (SF/AGN domi￾nated) quasars under different definitions. We carefully examined the origin of quasar radio emission at different 𝑀BH using our two-component model, and reproduce the difference in the cumulative BH mass distrib…
Figure 9
Figure 9. Figure 9: The black hole mass cumulative distribution function after removing the top 20% in the black hole mass distribution, separated into RL (AGN￾dominated) and RQ (SF-dominated) quasars based on various definitions of radio loudness. In contrast to [PITH_FULL_IMAGE:figures…
Figure 10
Figure 10. Figure 10: We show one representative M𝑖 −𝑧 bin (−24 < M𝑖 < −23, 1.2 < 𝑧 < 1.6) in which we stack the SDSS spectra of quasars that are matched in M𝑖 − 𝑧 space and have catalogue BH masses of 9.1 < log(𝑀BH/𝑀⊙ ) < 9.3 (within the highest 20% BH mass bin), while being dominated by …
Figure 11
Figure 11. Figure 11: The cumulative distribution function of quasar Civ distances in quadrants 1 to 4, calculated from the line of best-fit in Richards et al. (2021). Note that this figure does not include quasars with logCiv EW > 2.5 as they fall out of the domain of definition in Richar…
Figure 12
Figure 12. Figure 12: The cumulative distribution of Eddington ratio in quadrants 1 to 4 quasar populations, plotted in blue solid line, orange dashed line, green dotted line and pink dashed-dotted line, respectively. The vertical lines mark the mean value of Eddington ratio in each popula…

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Works this paper leans on

93 extracted references · 6 canonical work pages

  1. [1]

    I., et al., 2024, @doi [MNRAS] 10.1093/mnras/stae233 , 528, 4547

    Arnaudova M. I., et al., 2024, @doi [MNRAS] 10.1093/mnras/stae233 , 528, 4547

    doi:10.1093/mnras/stae233 2024

  2. [2]

    C., 2012, @doi [ApJ] 10.1088/0004-637X/759/1/30 , 759, 30

    Baloković M., Smolčić V., Ivezić Z., Zamorani G., Schinnerer E., Kelly B. C., 2012, @doi [ApJ] 10.1088/0004-637X/759/1/30 , 759, 30

    doi:10.1088/0004-637x/759/1/30 2012

  3. [3]

    D., Arnaud K

    Barthel P. D., Arnaud K. A., 1996, @doi [MNRAS] 10.1093/mnras/283.2.L45 , 283, L45

    doi:10.1093/mnras/283.2.l45 1996

  4. [4]

    H., White R

    Becker R. H., White R. L., Helfand D. J., 1995, @doi [ApJ] 10.1086/176166 , 450, 559

    doi:10.1086/176166 1995

  5. [5]

    D., Znajek R

    Blandford R. D., Znajek R. L., 1977, @doi [MNRAS] 10.1093/mnras/179.3.433 , 179, 433

    doi:10.1093/mnras/179.3.433 1977

  6. [6]

    M., Rawlings S., 2001, @doi [ApJ] 10.1086/337970 , 562, L5

    Blundell K. M., Rawlings S., 2001, @doi [ApJ] 10.1086/337970 , 562, L5

    doi:10.1086/337970 2001

  7. [7]

    Chaves-Montero J., et al., 2022, @doi [Astronomy & Astrophysics] 10.1051/0004-6361/202142567 , 660, A95

    work page doi:10.1051/0004-6361/202142567 2022

  8. [9]

    M., Norman C., 2015, @doi [ApJ] 10.1088/0004-637X/806/2/147 , 806, 147

    Chiaberge M., Gilli R., Lotz J. M., Norman C., 2015, @doi [ApJ] 10.1088/0004-637X/806/2/147 , 806, 147

    doi:10.1088/0004-637x/806/2/147 2015

Show all 93 references
  1. [10]

    Cirasuolo M., Magliocchetti M., Celotti A., Danese L., 2003, @doi [MNRAS] 10.1046/j.1365-8711.2003.06485.x , 341, 993

    2003

  2. [11]

    C., Banerji M., Richards G

    Coatman L., Hewett P. C., Banerji M., Richards G. T., Hennawi J. F., Prochaska J. X., 2017, @doi [MNRAS] 10.1093/mnras/stw2797 , 465, 2120

    2017 doi

  3. [12]

    J., Cotton W

    Condon J. J., Cotton W. D., Greisen E. W., Yin Q. F., Perley R. A., Taylor G. B., Broderick J. J., 1998, @doi [AJ] 10.1086/300337 , 115, 1693

    1998 doi

  4. [13]

    S., et al., 2016, @doi [AJ] 10.3847/0004-6256/151/2/44 , 151, 44

    Dawson K. S., et al., 2016, @doi [AJ] 10.3847/0004-6256/151/2/44 , 151, 44

    2016 doi

  5. [14]

    Dey A., et al., 2019, @doi [AJ] 10.3847/1538-3881/ab089d , 157, 168

    2019 doi

  6. [15]

    L., et al., 2025, @doi [MNRAS] 10.1093/mnras/stae2645 , 536, 1166

    Escott E. L., et al., 2025, @doi [MNRAS] 10.1093/mnras/stae2645 , 536, 1166

    2025 doi

  7. [16]

    Euclid Collaboration et al., 2024, @doi [arXiv.2405.13491] 10.48550/arXiv.2405.13491

    2024 doi

  8. [17]

    C., 2012, @doi [ARA&A] 10.1146/annurev-astro-081811-125521 , 50, 455

    Fabian A. C., 2012, @doi [ARA&A] 10.1146/annurev-astro-081811-125521 , 50, 455

    2012 doi

  9. [18]

    Giustini M., Proga D., 2019, @doi [A&A] 10.1051/0004-6361/201833810 , 630, A94

    2019 doi

  10. [19]

    J., et al., 2021, @doi [A&A] 10.1051/0004-6361/202141722 , 656, A137

    Gloudemans A. J., et al., 2021, @doi [A&A] 10.1051/0004-6361/202141722 , 656, A137

    2021 doi

  11. [20]

    Gürkan G., et al., 2019, @doi [A&A] 10.1051/0004-6361/201833892 , 622, A11

    2019 doi

  12. [21]

    J., et al., 2023, @doi [A&A] 10.1051/0004-6361/202347333 , 678, A151

    Hardcastle M. J., et al., 2023, @doi [A&A] 10.1051/0004-6361/202347333 , 678, A151

    2023 doi

  13. [22]

    A., et al., 2014, @doi [MNRAS] 10.1093/mnras/stu1725 , 445, 280

    Hatch N. A., et al., 2014, @doi [MNRAS] 10.1093/mnras/stu1725 , 445, 280

    2014 doi

  14. [23]

    F., Krolik J

    Hawley J. F., Krolik J. H., 2006, @doi [ApJ] 10.1086/500385 , 641, 103

    2006 doi

  15. [24]

    M., Best P

    Heckman T. M., Best P. N., 2014, @doi [ARA&A] 10.1146/annurev-astro-081913-035722 , 52, 589

    2014 doi

  16. [25]

    Hinshaw G., et al., 2013, @doi [ApJS] 10.1088/0067-0049/208/2/19 , 208, 19

    2013 doi

  17. [26]

    C., Ulvestad J

    Ho L. C., Ulvestad J. S., 2001, @doi [ApJS] 10.1086/319185 , 133, 77

    2001 doi

  18. [27]

    Ichikawa K., Inayoshi K., 2017, @doi [ApJL] 10.3847/2041-8213/aa6e4b , 840, L9

    2017 doi

  19. [28]

    T., Sikora M., Madejski G

    Inoue Y., Doi A., Tanaka Y. T., Sikora M., Madejski G. M., 2017, @doi [ApJ] 10.3847/1538-4357/aa6b57 , 840, 46

    2017 doi

  20. [29]

    Ivezić Z., et al., 2002, @doi [AJ] 10.1086/344069 , 124, 2364

    2002 doi

  21. [30]

    J., et al., 2017, @doi [arXiv:1709.01901] 10.48550/arXiv.1709.01901

    Jarvis M. J., et al., 2017, @doi [arXiv:1709.01901] 10.48550/arXiv.1709.01901

    2017 doi arXiv

  22. [31]

    Jin S., et al., 2024, @doi [MNRAS] 10.1093/mnras/stad557 , 530, 2688

    2024 doi

  23. [32]

    S., Netzer H., Maoz D., Jannuzi B

    Kaspi S., Smith P. S., Netzer H., Maoz D., Jannuzi B. T., Giveon U., 2000, @doi [ApJ] 10.1086/308704 , 533, 631

    2000 doi

  24. [33]

    I., Sramek R., Schmidt M., Shaffer D

    Kellermann K. I., Sramek R., Schmidt M., Shaffer D. B., Green R., 1989, @doi [AJ] 10.1086/115207 , 98, 1195

    1989 doi

  25. [34]

    Kondapally R., et al., 2021, @doi [A&A] 10.1051/0004-6361/202038813 , 648, A3

    2021 doi

  26. [35]

    Kondapally R., et al., 2022, @doi [MNRAS] 10.1093/mnras/stac1128 , 513, 3742

    2022 doi

  27. [36]

    C., 2013, @doi [ARA&A] 10.1146/annurev-astro-082708-101811 , 51, 511

    Kormendy J., Ho L. C., 2013, @doi [ARA&A] 10.1146/annurev-astro-082708-101811 , 51, 511

    2013 doi

  28. [37]

    W., et al., 2020, @doi [ApJS] 10.3847/1538-4365/aba623 , 250, 8

    Lyke B. W., et al., 2020, @doi [ApJS] 10.3847/1538-4365/aba623 , 250, 8

    2020 doi

  29. [38]

    Macfarlane C., et al., 2021, @doi [MNRAS] 10.1093/mnras/stab1998 , 506, 5888

    2021 doi

  30. [39]

    Magliocchetti M., 2022, @doi [A&ARv] 10.1007/s00159-022-00142-1 , 30, 6

    2022 doi

  31. [42]

    Martínez-Sansigre A., Rawlings S., 2011b, @doi [MNRAS] 10.1111/j.1745-3933.2011.01148.x , 418, L84

    2011

  32. [43]

    C., Tchekhovskoy A., Blandford R

    McKinney J. C., Tchekhovskoy A., Blandford R. D., 2012, @doi [MNRAS] 10.1111/j.1365-2966.2012.21074.x , 423, 3083

    2012

  33. [44]

    J., Jarvis M

    McLure R. J., Jarvis M. J., 2002, @doi [MNRAS] 10.1046/j.1365-8711.2002.05871.x , 337, 109

    2002

  34. [45]

    J., Jarvis M

    McLure R. J., Jarvis M. J., 2004, @doi [MNRAS] 10.1111/j.1365-2966.2004.08305.x , 353, L45

    2004

  35. [46]

    Mehdipour M., Costantini E., 2019, @doi [A&A] 10.1051/0004-6361/201935205 , 625, A25

    2019 doi

  36. [47]

    Merloni A., Heinz S., 2008, @doi [MNRAS] 10.1111/j.1365-2966.2008.13472.x , 388, 1011

    2008

  37. [48]

    Merloni A., Heinz S., di Matteo T., 2003, @doi [MNRAS] 10.1046/j.1365-2966.2003.07017.x , 345, 1057

    2003

  38. [49]

    Mitchell J. A. J., Done C., Ward M. J., Kynoch D., Hagen S., Lusso E., Landt H., 2023, @doi [MNRAS] 10.1093/mnras/stad1830 , 524, 1796

    2023 doi

  39. [50]

    ascl:1502.007

    Mohan N., Rafferty D., 2015, Astrophysics Source Code Library, p. ascl:1502.007

    2015

  40. [51]

    K., et al., 2019, @doi [A&A] 10.1051/0004-6361/201833821 , 622, A15

    Morabito L. K., et al., 2019, @doi [A&A] 10.1051/0004-6361/201833821 , 622, A15

    2019 doi

  41. [52]

    D., et al., 2015, @doi [ApJS] 10.1088/0067-0049/221/2/27 , 221, 27

    Myers A. D., et al., 2015, @doi [ApJS] 10.1088/0067-0049/221/2/27 , 221, 27

    2015 doi

  42. [53]

    Narayan R., Yi I., 1994, @doi [ApJ] 10.1086/187381 , 428, L13

    1994 doi

  43. [54]

    Narayan R., Yi I., 1995, @doi [ApJ] 10.1086/175599 , 444, 231

    1995 doi

  44. [55]

    V., Abramowicz M

    Narayan R., Igumenshchev I. V., Abramowicz M. A., 2003, @doi [PASJ] 10.1093/pasj/55.6.L69 , 55, L69

    2003 doi

  45. [56]

    M., 2014, @doi [Space Science Reviews] 10.1007/s11214-013-9987-4 , 183, 253

    Peterson B. M., 2014, @doi [Space Science Reviews] 10.1007/s11214-013-9987-4 , 183, 253

    2014 doi

  46. [57]

    W., et al., 2022, @doi [MNRAS] 10.1093/mnras/stac2067 , 515, 5159

    Petley J. W., et al., 2022, @doi [MNRAS] 10.1093/mnras/stac2067 , 515, 5159

    2022 doi

  47. [58]

    W., et al., 2024, @doi [MNRAS] 10.1093/mnras/stae626 , 529, 1995

    Petley J. W., et al., 2024, @doi [MNRAS] 10.1093/mnras/stae626 , 529, 1995

    2024 doi

  48. [59]

    M., Markoff S., Kelly B

    Plotkin R. M., Markoff S., Kelly B. C., Körding E., Anderson S. F., 2012, @doi [MNRAS] 10.1111/j.1365-2966.2011.19689.x , 419, 267

    2012

  49. [60]

    Pâris I., et al., 2018, @doi [A&A] 10.1051/0004-6361/201732445 , 613, A51

    2018 doi

  50. [61]

    L., Hewett P

    Rankine A. L., Hewett P. C., Banerji M., Richards G. T., 2020, @doi [MNRAS] 10.1093/mnras/staa130 , 492, 4553

    2020 doi

  51. [62]

    Retana-Montenegro E., Röttgering H. J. A., 2017, @doi [A&A] 10.1051/0004-6361/201526433 , 600, A97

    2017 doi

  52. [63]

    S., Garofalo D., Begelman M

    Reynolds C. S., Garofalo D., Begelman M. C., 2006, @doi [ApJ] 10.1086/507691 , 651, 1023

    2006 doi

  53. [64]

    T., et al., 2006, @doi [AJ] 10.1086/503559 , 131, 2766

    Richards G. T., et al., 2006, @doi [AJ] 10.1086/503559 , 131, 2766

    2006 doi

  54. [65]

    T., et al., 2011, @doi [AJ] 10.1088/0004-6256/141/5/167 , 141, 167

    Richards G. T., et al., 2011, @doi [AJ] 10.1088/0004-6256/141/5/167 , 141, 167

    2011 doi

  55. [66]

    T., McCaffrey T

    Richards G. T., McCaffrey T. V., Kimball A., Rankine A. L., Matthews J. H., Hewett P. C., Rivera A. B., 2021, @doi [AJ] 10.3847/1538-3881/ac283b , 162, 270

    2021 doi

  56. [67]

    G., Best P

    Roseboom I. G., Best P. N., 2014, @doi [MNRAS] 10.1093/mnras/stt2452 , 439, 1286

    2014 doi

  57. [68]

    P., et al., 2009, @doi [ApJ] 10.1088/0004-637X/697/2/1634 , 697, 1634

    Ross N. P., et al., 2009, @doi [ApJ] 10.1088/0004-637X/697/2/1634 , 697, 1634

    2009 doi

  58. [69]

    P., et al., 2012, @doi [ApJS] 10.1088/0067-0049/199/1/3 , 199, 3

    Ross N. P., et al., 2012, @doi [ApJS] 10.1088/0067-0049/199/1/3 , 199, 3

    2012 doi

  59. [70]

    P., et al., 2010, @doi [AJ] 10.1088/0004-6256/139/6/2360 , 139, 2360

    Schneider D. P., et al., 2010, @doi [AJ] 10.1088/0004-6256/139/6/2360 , 139, 2360

    2010 doi

  60. [71]

    Schulze A., Done C., Lu Y., Zhang F., Inoue Y., 2017, @doi [ApJ] 10.3847/1538-4357/aa9181 , 849, 4

    2017 doi

  61. [72]

    Seymour N., et al., 2007, @doi [ApJS] 10.1086/517887 , 171, 353

    2007 doi

  62. [73]

    Shen Y., 2013, @doi [Bulletin of the Astronomical Society of India] 10.48550/arXiv.1302.2643 , 41, 61

    2013 doi arXiv

  63. [74]

    Shen Y., et al., 2009, @doi [ApJ] 10.1088/0004-637X/697/2/1656 , 697, 1656

    2009 doi

  64. [75]

    Shen Y., et al., 2011, @doi [ApJS] 10.1088/0067-0049/194/2/45 , 194, 45

    2011 doi

  65. [76]

    Shen Y., et al., 2019, @doi [ApJS] 10.3847/1538-4365/ab074f , 241, 34

    2019 doi

  66. [77]

    Shen Y., et al., 2024, @doi [ApJS] 10.3847/1538-4365/ad3936 , 272, 26

    2024 doi

  67. [78]

    W., et al., 2017, @doi [A&A] 10.1051/0004-6361/201629313 , 598, A104

    Shimwell T. W., et al., 2017, @doi [A&A] 10.1051/0004-6361/201629313 , 598, A104

    2017 doi

  68. [79]

    W., et al., 2019, @doi [A&A] 10.1051/0004-6361/201833559 , 622, A1

    Shimwell T. W., et al., 2019, @doi [A&A] 10.1051/0004-6361/201833559 , 622, A1

    2019 doi

  69. [80]

    W., et al., 2022, @doi [A&A] 10.1051/0004-6361/202142484 , 659, A1

    Shimwell T. W., et al., 2022, @doi [A&A] 10.1051/0004-6361/202142484 , 659, A1

    2022 doi

  70. [81]

    C., 2013, @doi [ApJ] 10.1088/2041-8205/764/2/l24 , 764, L24

    Sikora M., Begelman M. C., 2013, @doi [ApJ] 10.1088/2041-8205/764/2/l24 , 764, L24

    2013 doi

  71. [82]

    Smith D. J. B., et al., 2016, @doi [arXiv:1611.02706] 10.48550/arXiv.1611.02706 , pp 271--280

    2016 doi arXiv

  72. [83]

    Smith D. J. B., et al., 2021, @doi [A&A] 10.1051/0004-6361/202039343 , 648, A6

    2021 doi

  73. [84]

    J., et al., 2023, @doi [MNRAS] 10.1093/mnras/stad1448 , 523, 646

    Temple M. J., et al., 2023, @doi [MNRAS] 10.1093/mnras/stad1448 , 523, 646

    2023 doi

  74. [85]

    S., Ho L

    Ulvestad J. S., Ho L. C., 2001, @doi [ApJ] 10.1086/322307 , 558, 561

    2001 doi

  75. [86]

    S., 2009, @doi [ApJ] 10.1088/0004-637X/699/1/800 , 699, 800

    Vestergaard M., Osmer P. S., 2009, @doi [ApJ] 10.1088/0004-637X/699/1/800 , 699, 800

    2009 doi

  76. [87]

    M., 2006, @doi [ApJ] 10.1086/500572 , 641, 689

    Vestergaard M., Peterson B. M., 2006, @doi [ApJ] 10.1086/500572 , 641, 689

    2006 doi

  77. [88]

    L., Helfand D

    White R. L., Helfand D. J., Becker R. H., Glikman E., de Vries W., 2007, @doi [ApJ] 10.1086/507700 , 654, 99

    2007 doi

  78. [89]

    H., et al., 2022, @doi [MNRAS] 10.1093/mnras/stac2140 , 516, 245

    Whittam I. H., et al., 2022, @doi [MNRAS] 10.1093/mnras/stac2140 , 516, 245

    2022 doi

  79. [90]

    L., et al., 2019, @doi [A&A] 10.1051/0004-6361/201833564 , 622, A2

    Williams W. L., et al., 2019, @doi [A&A] 10.1051/0004-6361/201833564 , 622, A2

    2019 doi

  80. [91]

    S., Colbert E

    Wilson A. S., Colbert E. J. M., 1995, @doi [ApJ] 10.1086/175054 , 438, 62

    1995 doi

  81. [92]

    L., et al., 2010, @doi [AJ] 10.1088/0004-6256/140/6/1868 , 140, 1868

    Wright E. L., et al., 2010, @doi [AJ] 10.1088/0004-6256/140/6/1868 , 140, 1868

    2010 doi

  82. [93]

    Wu Q., Shen Y., 2022, @doi [ApJS] 10.3847/1538-4365/ac9ead , 263, 42

    2022 doi

  83. [94]

    G., et al., 2000, @doi [AJ] 10.1086/301513 , 120, 1579

    York D. G., et al., 2000, @doi [AJ] 10.1086/301513 , 120, 1579

    2000 doi

  84. [95]

    H., et al., 2024, @doi [MNRAS] 10.1093/mnras/stae725 , 529, 3939

    Yue B. H., et al., 2024, @doi [MNRAS] 10.1093/mnras/stae725 , 529, 3939

    2024 doi

  85. [96]

    P., et al., 2013, @doi [A&A] 10.1051/0004-6361/201220873 , 556, A2

    van Haarlem M. P., et al., 2013, @doi [A&A] 10.1051/0004-6361/201220873 , 556, A2

    2013 doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.