Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

Self-consistent population synthesis of AGN from observational constraints in the X-rays

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read A self-consistent ray-traced AGN population reproduces the X-ray background and obscuration at once, and yields a 21±7% intrinsic Compton-thick fraction.

desk verdict The paper's real contribution is structural—absorption and reflection finally come from one matter distribution—but the headline 21±7% CT fraction is a model-dependent output, not yet a measured number. read the letter →

arxiv 2506.14885 v1 pith:7Y42QT4S submitted 2025-06-17 astro-ph.CO astro-ph.HE

D. Gerolymatou , S. Paltani , C. Ricci , M. Regamey This is my paper
classification astro-ph.COastro-ph.HE
keywords activegalacticnucleicosmicX-raybackgroundAGNpopulationsynthesisCompton-thickobscurationraytracingsimulation-basedinference
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 show that the X-ray emission, absorption, and reflection of active galactic nuclei (AGN) can be generated from one physical geometry rather than patched together by hand. The authors simulate AGN spectra with the ray-tracing code RefleX, populate a synthetic universe by sampling an intrinsic X-ray luminosity function, and use simulation-based inference to compare model populations against the cosmic X-ray background, the observed neutral-hydrogen column-density distribution, and the luminosity and redshift dependence of the absorbed fraction. They find that the simplest orientation-based unification model already fits the background, but that reproducing the absorption data requires a torus whose inner radius grows with luminosity, and that adding an accretion disk gives the best simultaneous match to all constraints. The population that fits everything implies that 21±7% of AGN are intrinsically Compton-thick, a fraction in line with local hard-X-ray surveys. If the claim holds, the long-standing freedom to trade reflection strength against the number of heavily obscured sources in background models is removed.

What carries the argument

The machinery is a grid of 2–100 keV spectra computed with the ray-tracing code RefleX for a torus as a function of equatorial column density and inner-to-outer radius ratio, with a lamp-post double source above and below an accretion disk in the final model. The load-bearing identities are Eq. (4), which turns the observation angle $\theta_{\rm obs}$ into a line-of-sight $N_{\rm H}$ for a homogeneous torus, and Eq. (3), the luminosity-dependent inner radius that makes the covering factor fall with intrinsic luminosity; the slope of that relation, $\alpha$, is the parameter that carries the absorbed-fraction–luminosity trend. Simulation-based inference (SNPE) maps the four observables to posteriors over the mean and width of the lognormal equatorial-column distribution, the radius normalization, and $\alpha$.

What would settle it

A direct check is to measure line-of-sight $N_{\rm H}$ for local AGN whose torus inclination is known from infrared interferometry or polarization and test the single-valued mapping of Eq. (4). Failing that, a clumpy-torus population synthesis fitted to the same four constraints that reproduces them with a materially different intrinsic Compton-thick fraction, or a high-redshift hard-X-ray survey whose measured CT fraction disagrees with the constant 21±7% prediction, would falsify the central claim.

Watch

Extended reading notes

Core claim

The central claim is that a 'LD+AD' geometry—a cold, homogeneous dusty torus whose dust-sublimation radius scales with luminosity as $r_{\rm dust}\propto L^{\alpha}$ with fitted $\alpha=0.24\pm0.01$, joined to an optically thick accretion disk, with the line-of-sight column density set purely by inclination through Eq. (4)—simultaneously matches the cosmic X-ray background, the $N_{\rm H}$ distribution of local hard-X-ray-selected AGN, the absorbed fraction as a function of observed luminosity and redshift, and the observed trend of reflection with obscuration. Because emission, absorption, and reflection all come from the same matter distribution, the model has no free reflection parameter and no assumed intrinsic $N_{\rm H}$ distribution. The resulting intrinsic Compton-thick fraction is 21±7%, down from 50±2% when the disk is omitted, since unobscured and Compton-thin sources with the disk reflect enough to explain the 20–30 keV background peak without demanding many heavily obscured nuclei.

Load-bearing premise

The load-bearing premise is that the torus is a cold, homogeneous, non-clumpy structure, so that any line of sight through it always crosses optically thick material and the observed column is a single-valued function of viewing angle; if real tori are clumpy, the fitted luminosity slope and the derived 21±7% Compton-thick fraction could shift.

Editorial extensions

If this is right

  • The cosmic X-ray background alone is a weak constraint: even a population of identical orientation-only tori reproduces it, so fitting the background cannot by itself fix the AGN population.
  • Matching the absorption data forces the torus inner radius to grow roughly as $L^{0.24}$, flatter than the classical receding-torus prediction of $L^{0.5}$ and consistent with X-ray surveys.
  • The accretion disk matters for reflection: dropping it raises the required intrinsic Compton-thick fraction from 21±7% to 50±2% and raises the Compton-thick contribution to the 14–195 keV background from about 7% to 28%.
  • The same population reproduces the absorbed and unabsorbed X-ray luminosity functions, the Chandra, XMM-Newton, and NuSTAR number counts, and the covering-factor–luminosity trend, while overpredicting Swift/BAT 14–195 keV counts by 1–2σ.

Reading between the lines

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

  • The homogeneous-torus assumption is the untested hinge: with a clumpy torus, $N_{\rm H}$ would scatter at fixed orientation, which could change $\alpha$ and the inferred intrinsic CT fraction; a clumpy-torus rerun of the same population synthesis is the nearest decisive experiment.
  • The model's underpredicted high-redshift absorbed fraction might be cured without touching the torus by adding a galaxy-scale interstellar medium whose column density grows with redshift, as the paper hints.
  • The fitted $\alpha\approx0.24$ is close to the $L^{-0.25}$ scaling predicted for clumpy tori, suggesting the luminosity dependence could be driven by the number of clouds rather than dust sublimation alone—an alternative the homogeneous model cannot distinguish.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. This paper presents a population synthesis model of AGN in the X-rays, using the ray-tracing code RefleX to self-consistently link absorption and reflection through the circumnuclear matter distribution. Three geometric models are considered: a simple homogeneous torus, a luminosity-dependent (LD) torus, and an LD torus with an accretion disk (LD+AD). Model parameters are inferred with simulation-based inference (SBI), using the cosmic X-ray background (CXB), the Swift/BAT NH distribution, and the Hasinger (2008) absorbed fraction as functions of luminosity and redshift. The claim is that the LD+AD model simultaneously reproduces all constraints, yielding a luminosity-dependent covering factor, number counts, a reflection–obscuration correlation, and an intrinsic Compton-thick fraction of 21±7%.

Significance. If the result held, the paper would be a valuable step toward physically motivated population synthesis, replacing assumed NH distributions and reflection parameters with self-consistent ray-traced models. The explicit treatment of survey selection functions and the use of SBI are strengths, as is the comparison of nested models of increasing complexity. The qualitative conclusion that a luminosity-dependent torus plus accretion disk is required to match both the CXB and absorption data is plausible and likely to survive further scrutiny. However, the quantitative claims (the CT fraction, the slope α, and the statement of 'simultaneous reproduction') are currently conditional on a homogeneous torus geometry and on a fit that is formally rejected at the statistical level.

major comments (4)
  1. [Table 2, §4.4] The best combined model, LD+AD, yields χ²/dof=804/109≈7.4, and even the EC=300 keV variant gives 540/109≈5.0; both are formally rejected at high confidence. The abstract's statement that the synthetic population 'simultaneously reproduces' the constraints overstates the fit quality. The paper should either report the formal rejection and adopt a model-inadequacy term (e.g., an added systematic error floor) or revise the claim to a qualitative best-match statement, since the derived CT fraction and α are conditioned on this model.
  2. [§3.3, §5.3, Eq. (4)] All models assume a cold, homogeneous, continuous torus for which the line-of-sight column is a deterministic function of inclination (Eq. 4). This assumption is load-bearing: the reflected spectrum, and hence the amount of CT AGN needed to match the CXB peak, is fixed by this mapping. The paper acknowledges in §5.3 that a clumpy torus 'could alter this relationship' but does not test it or bound the effect. A sensitivity analysis (e.g., injecting scatter in NH at fixed inclination, or comparing with a clumpy-torus realisation) is needed before the 21±7% CT fraction and the fitted slope α=0.24 can be regarded as robust.
  3. [§4.4, §5.4] The LD+AD model underpredicts the absorbed AGN fraction at high redshift by about 25%, and the paper attributes this to the lack of redshift evolution in the torus parameters. This contradicts the abstract's claim that the absorption properties are reproduced 'including their redshift and luminosity evolution.' The text should be revised to state explicitly that the luminosity dependence is reproduced while the redshift dependence is not, and the H08 integrated fraction is matched only because it is averaged over redshift.
  4. [§3.2, §5.4] The 'derived' intrinsic CT fraction is an output of the fitted model, not an independent measurement: the parameters (μ, σ, R0, α) are inferred using, among other data, the R17 NH distribution that includes CT bins, and the XLF is normalised to the NH<10^24 population. The paper should clarify that 21±7% is a posterior prediction conditional on the assumed geometry and XLF, and that the quoted uncertainty does not include systematic errors from the clumpy-torus or evolution assumptions.
minor comments (5)
  1. [§4.4] The EC=300 keV check uses the same parameter posteriors as the EC=200 keV fit rather than a new fit; clarify that this is a post-hoc evaluation, not a re-optimisation.
  2. [Table 2] Table 2 lists reduced χ² values for all models, several of which exceed 5; adding p-values or a discussion of why the χ² metric is used despite the formal rejection would help readers calibrate the comparisons.
  3. [§5.4] The ±7% uncertainty on the CT fraction should be explicitly labelled as the 68% posterior spread only, excluding the systematic effects discussed in the major comments.
  4. [§3.4] The weight N_XLF/(N_sample − N_CT) depends on N_CT, which is itself a function of the inferred parameters; a sentence explaining how N_CT is computed during the inference would remove ambiguity.
  5. [Fig. 12] The model curves are compared to observed reflection–obscuration data without applying survey selection functions; the text should state that the comparison is qualitative only, as is already hinted.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the model parameters are fitted to observational constraints, and the derived quantities are checked against independent data; self-citations are present but not load-bearing.

full rationale

The paper is an inference/fitting study rather than a derivation from first principles, and its central results do not reduce to their inputs by construction. The SBI posteriors are obtained from four constraints (CXB, observed NH distribution, absorbed fraction versus luminosity, and absorbed fraction versus redshift). Derived quantities such as the intrinsic CT fraction, the split XLF, the covering-factor-luminosity relation, and the reflection-obscuration correlation are deterministic functions of the fitted parameters and the assumed torus geometry, but they are not identical to the constraints. In particular, the intrinsic CT fraction is obtained by integrating the fitted parent NH,eq distribution above 10^24 cm^-2 after normalizing the LF to the NH<10^24 XLF (Sec. 3.2), and it is then compared with external local measurements rather than claimed as an independent prediction. The observed NH distribution does include Compton-thick bins, so the CT fraction is inferred from that constraint, but inference from a constraint is not circularity. The covering-factor comparison with Ricci et al. (2022) in Fig. 9 uses a dataset not used in the fit, and the number counts and reflection-obscuration trends in Figs. 11-12 are also external, making those checks genuine. Several cited inputs are authored or coauthored by the present authors (RefleX: Paltani & Ricci 2017; the R17 NH data: Ricci et al. 2017a; McKaig et al. 2022), but these are published, reproducible data or codes and are not invoked as a uniqueness theorem to exclude alternatives. The acknowledged homogeneous, non-clumpy torus assumption in Sec. 5.3 is a model limitation that affects robustness, but it is not circular because it does not define the results in terms of themselves. No step reduces by construction to its own input; only the density of self-citations warrants a minimal nonzero score.

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

The central output of the paper, the intrinsic CT fraction, is a function of the four fitted parameters mu, sigma, R0, and alpha. The model is not a derivation from first principles; it is a parameterized simulation fitted to the observed CXB and absorption statistics. The luminosity dependence of the absorbed fraction is built in through Eq. (3) with a free slope, so its recovery is partly by construction. The lognormal form of the NH_eq distribution is assumed, not derived. The most important external checks (covering factor vs luminosity, number counts, reflection-obscuration) are not part of the fit, which is what keeps the circularity burden low. No new physical entities are introduced.

free parameters (4)
  • mu (mean of log NH_eq distribution) = 23.56 (LD+AD combined, Table 2)
    Mean of the lognormal equatorial column density distribution; fitted via SBI to the CXB and absorption constraints. It directly sets the location of the NH distribution and influences the CT fraction.
  • sigma (scatter of log NH_eq distribution) = 0.98 (LD+AD combined)
    Standard deviation of the lognormal; controls the breadth of the NH distribution and the fraction of AGN in the Compton-thick regime.
  • R0 (torus inner edge normalization) = 0.93 (LD+AD combined, in pc)
    Normalization in Eq. (3); sets the overall torus size scale relative to L0 = 10^44 erg/s. Determines the covering factor at the reference luminosity.
  • alpha (luminosity slope of inner edge) = 0.24 (LD+AD combined)
    Slope in Eq. (3); controls how strongly the covering factor decreases with luminosity. This fitted parameter is what produces the luminosity-dependent absorbed fraction.
assumptions (6)
  • domain assumption The circumnuclear matter is a cold, homogeneous dusty torus, with line-of-sight column density given by Eq. (4).
    Introduced in Sec. 3.3 and used for all models; a clumpy torus would change the NH(theta) mapping and the derived CT fraction. The paper acknowledges this in Sec. 5.3.
  • domain assumption The intrinsic equatorial column density NH_eq follows a lognormal distribution with free mean mu and scatter sigma.
    Sec. 3.4; this parameterized form is assumed rather than derived, and it is the main driver of the observed NH distribution and of the derived CT fraction.
  • domain assumption The U03 XLF represents the intrinsic AGN population for NH < 10^24 cm^-2, and the synthetic population is normalized to it in that regime.
    Sec. 3.2; if the U03 XLF is biased, the absolute scale of the population and the inferred CT fraction shift accordingly.
  • domain assumption The primary X-ray emission is a power law with Gamma = 1.9 and cutoff EC = 200 or 300 keV, emitted from the torus center or from a lamp-post double source.
    Sec. 3.3; adopted from typical AGN values. The choice of EC affects the CXB fit above about 50 keV, as discussed in Sec. 4.4.
  • domain assumption AGN obscuration arises only from the torus (and the optically thick accretion disk in LD+AD), with no host-galaxy ISM contribution.
    Sec. 2.2.1 and 5.5; the paper acknowledges this limitation and estimates that an ISM component would shift mu to lower values and increase the high-redshift absorbed fraction.
  • domain assumption The receding-torus relation rdust = R0 (L/10^44)^alpha with free R0 and alpha controls the covering factor.
    Sec. 3.3.2; physically motivated by dust sublimation, but the slope alpha is a free parameter and the model does not test Eddington-ratio-dependent alternatives.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Self-consistent population synthesis of AGN from observational constraints in the X-rays." pith.science (2026). https://pith.science/paper/7Y42QT4S

@misc{pith2026250614885,
  author       = {Pith},
  title        = {Pith review of: Self-consistent population synthesis of AGN from observational constraints in the X-rays},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7Y42QT4S}},
  note         = {Machine review of arXiv:2506.14885}
}
abstract

The cosmic X-ray background (CXB) is produced by the emission of unresolved active galactic nuclei (AGN), thus providing key information about the properties of the primary and reprocessed X-ray emission components of the AGN population. Equally important, studies of individual sources provide additional constraints on the properties of AGN, such as their luminosity and obscuration. Until now, these constraints have not been self-consistently addressed by intrinsically linking emission, absorption, and reflection. Here we perform numerical simulations with the ray-tracing code, RefleX, which allows us to self-consistently model the X-ray emission of AGN with flexible geometries for the circumnuclear medium. Using the RefleX-simulated emission of an AGN population, we attempt to simultaneously reproduce the CXB and absorption properties measured in the X-rays, namely the observed fraction of $N_{\mathrm{H}}$ in bins of log($N_{\mathrm{H}}$) and the fraction of absorbed AGN, including their redshift and luminosity evolution. We sample an intrinsic X-ray luminosity function and construct gradually more complex physically motivated geometrical models. We examine how well each model can match all observational constraints using a simulation-based inference (SBI) approach. We find that, while the simple unification model can reproduce the CXB, a luminosity dependent dusty torus is needed to reproduce the absorption properties. When adding an accretion disc, the model best matches all constraints simultaneously. Our synthetic population is able to reproduce the dependence of the covering factor on luminosity, the AGN number counts from several surveys, and the observed correlation between reflection and obscuration. Finally, we derive an intrinsic Compton-thick fraction of 21$\pm$7%, consistent with local observations.

Figures

Figures reproduced from arXiv: 2506.14885 by the authors.

Figure 1
Figure 1. X-ray observables used as constraints in this work: [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Torus geometry in RefleX. The inner radius, Rin, is de￾fined as the radius of the cross-section, while the outer radius, Rout, is measured from the centre of the torus to the centre of the cross-section. The equatorial column density, NH,eq, is measured across the diameter of the cross-section (white dashed line). The X-ray point source is placed at the centre of the torus. 3.3. Models The physical models of the AGN… view at source ↗
Figure 3
Figure 3. Accretion disc and double X-ray source geometry in R [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Sky fraction as a function of 14–195 keV and 2–10 keV [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Median model (black line) for the CXB in the range 2– [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Median models (black lines) for the three observed absorption properties using the simple torus model (top row) and the LD [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Median models (black lines) using the LD torus model applied on the four combined constraints: [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Same as Fig [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: Average torus covering factor of our local synthetic pop [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]
Figure 10
Figure 10. Figure 10: X-ray luminosity function of absorbed (red points) and [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 11
Figure 11. Figure 11: Differential number counts as a function of flux produced by the LD+AD model (black) in the left: 2–10 keV, centre: 8–24 keV and right: 14–195 keV energy ranges. The error bars represent the 68% confidence interval of the model based on the posterior of the parameters…
Figure 12
Figure 12. Figure 12: Observed correlation between the fraction of reflected [PITH_FULL_IMAGE:figures/full_fig_p012_12.png]
Figure 13
Figure 13. Figure 13: Left: Contribution of unobscured (log(NH/ cm−2 < 22; dashed-dotted line), Compton-thin (22 ≤ log(NH/ cm−2 ) < 24; dotted line) and CT sources (log(NH/ cm−2 ) ≥ 24; dashed line) to the CXB (solid line) reproduced by the median parameters of the LD torus model. Right: A…
Figure 14
Figure 14. Figure 14: Intrinsic NH distribution (black line) of our local syn￾thetic population produced by the LD+AD model with the com￾bined constraints. Our observed distribution (green line) from [PITH_FULL_IMAGE:figures/full_fig_p013_14.png]

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

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

  1. Unification models of Active Galactic Nuclei

    astro-ph.GA 2026-06 unverdicted novelty 2.0 of 10

    Overview chapter summarizing traditional orientation-based and radiation-regulated unification models for AGN, including evolutionary aspects and changing-look AGN.

Reference graph

Works this paper leans on

70 extracted references · 37 canonical work pages · cited by 1 Pith paper

  1. [1]

    L., Georgakakis, A., et al

    Aird, J., Coil, A. L., Georgakakis, A., et al. 2015, MNRAS, 451, 1892

    2015

  2. [2]

    2008, ApJ, 689, 666

    Ajello, M., Greiner, J., Sato, G., et al. 2008, ApJ, 689, 666

    2008

  3. [3]

    2012, A&A, 546, A98

    Akylas, A., Georgakakis, A., Georgantopoulos, I., Brightman, M., & Nandra, K. 2012, A&A, 546, A98

    2012

  4. [4]

    & Georgantopoulos, I

    Akylas, A. & Georgantopoulos, I. 2019, A&A, 625, A131

    work page 2019

  5. [5]

    T., Treister, E., Urry, C

    Ananna, T. T., Treister, E., Urry, C. M., et al. 2019, ApJ, 871, 240

    2019

  6. [6]

    T., Weigel, A

    Ananna, T. T., Weigel, A. K., Trakhtenbrot, B., et al. 2022, ApJS, 261, 9

    2022

  7. [7]

    1993, ARA&A, 31, 473

    Antonucci, R. 1993, ARA&A, 31, 473

    1993

  8. [8]

    Arnaud, K. A. 1996, in Astronomical Society of the Pacific Conference Series, V ol. 101, Astronomical Data Analysis Software and Systems V , ed. G. H. Jacoby & J. Barnes, 17 Balokovi´c, M., Harrison, F. A., Madejski, G., et al. 2020, ApJ, 905, 41

    work page 1996

Show all 70 references
  1. [9]

    H., Tueller, J., Markwardt, C

    Baumgartner, W. H., Tueller, J., Markwardt, C. B., et al. 2013, ApJS, 207, 19

    2013

  2. [10]

    G., Gandhi, P., Buchner, J., et al

    Boorman, P. G., Gandhi, P., Buchner, J., et al. 2025, ApJ, 978, 118

    2025

  3. [11]

    Brandt, W. N. & Alexander, D. M. 2015, A&A Rev., 23, 1

    2015

  4. [12]

    Brandt, W. N. & Yang, G. 2022, in Handbook of X-ray and Gamma-ray Astro- physics, ed. C. Bambi & A. Sangangelo, 78

    2022

  5. [13]

    & Nandra, K

    Brightman, M. & Nandra, K. 2011, MNRAS, 413, 1206

    2011

  6. [14]

    2015, ApJ, 802, 89

    Buchner, J., Georgakakis, A., Nandra, K., et al. 2015, ApJ, 802, 89

    2015

  7. [15]

    2011, ApJ, 728, 58

    Burlon, D., Ajello, M., Greiner, J., et al. 2011, ApJ, 728, 58

    2011

  8. [16]

    2017, ApJ, 837, 19

    Cappelluti, N., Li, Y ., Ricarte, A., et al. 2017, ApJ, 837, 19

    2017

  9. [17]

    2012, MNRAS, 427, 651

    Cappelluti, N., Ranalli, P., Roncarelli, M., et al. 2012, MNRAS, 427, 651

    2012

  10. [18]

    2007, A&A, 467, 529

    Churazov, E., Sunyaev, R., Revnivtsev, M., et al. 2007, A&A, 467, 529

    2007

  11. [19]

    N., Aird, J., Evans, P

    Delaney, J. N., Aird, J., Evans, P. A., et al. 2023, MNRAS, 521, 1620

    2023

  12. [20]

    Draine, B. T. 2003, ApJ, 598, 1026

    2003

  13. [21]

    Draper, A. R. & Ballantyne, D. R. 2009, ApJ, 707, 778

    2009

  14. [22]

    & Walter, R

    Esposito, V . & Walter, R. 2016, A&A, 590, A49

    2016

  15. [23]

    2000, ApJ, 539, L13

    Gebhardt, K., Bender, R., Bower, G., et al. 2000, ApJ, 539, L13

    2000

  16. [24]

    1994, MNRAS, 267, 743

    Ghisellini, G., Haardt, F., & Matt, G. 1994, MNRAS, 267, 743

    1994

  17. [25]

    2007, A&A, 463, 79

    Gilli, R., Comastri, A., & Hasinger, G. 2007, A&A, 463, 79

    2007

  18. [26]

    2022, A&A, 666, A17 GRA VITY Collaboration, Dexter, J., Shangguan, J., et al

    Gilli, R., Norman, C., Calura, F., et al. 2022, A&A, 666, A17 GRA VITY Collaboration, Dexter, J., Shangguan, J., et al. 2020, A&A, 635, A92

    2022

  19. [27]

    S., Nonnenmacher, M., & Macke, J

    Greenberg, D. S., Nonnenmacher, M., & Macke, J. H. 2019, arXiv e-prints, arXiv:1905.07488

    2019 arXiv

  20. [28]

    E., Matteson, J

    Gruber, D. E., Matteson, J. L., Peterson, L. E., & Jung, G. V . 1999, ApJ, 520, 124 Güver, T. & Özel, F. 2009, MNRAS, 400, 2050

    1999

  21. [29]

    & Maraschi, L

    Haardt, F. & Maraschi, L. 1991, ApJ, 380, L51

    1991

  22. [30]

    A., Aird, J., Civano, F., et al

    Harrison, F. A., Aird, J., Civano, F., et al. 2016, ApJ, 831, 185

    2016

  23. [31]

    2008, A&A, 490, 905

    Hasinger, G. 2008, A&A, 490, 905

    2008

  24. [32]

    Hickox, R. C. & Alexander, D. M. 2018, ARA&A, 56, 625 Hönig, S. F. & Beckert, T. 2007, MNRAS, 380, 1172

    2018

  25. [33]

    2020, A&A, 639, A51

    Iwasawa, K., Comastri, A., Vignali, C., et al. 2020, A&A, 639, A51

    2020

  26. [34]

    Kormendy, J. & Ho, L. C. 2013, ARA&A, 51, 511

    2013

  27. [35]

    2017, ApJ, 850, 74

    Koss, M., Trakhtenbrot, B., Ricci, C., et al. 2017, ApJ, 850, 74

    2017

  28. [36]

    1991, MNRAS, 252, 586

    Lawrence, A. 1991, MNRAS, 252, 586

    1991

  29. [37]

    D., Xue, Y

    Lehmer, B. D., Xue, Y . Q., Brandt, W. N., et al. 2012, ApJ, 752, 46

    2012

  30. [38]

    2017, ApJS, 232, 8

    Liu, T., Tozzi, P., Wang, J.-X., et al. 2017, ApJS, 232, 8

    2017

  31. [39]

    2003, ApJ, 591, 1220

    Lodders, K. 2003, ApJ, 591, 1220

    2003

  32. [40]

    1998, AJ, 115, 2285

    Magorrian, J., Tremaine, S., Richstone, D., et al. 1998, AJ, 115, 2285

    1998

  33. [41]

    & Matt, G

    Martocchia, A. & Matt, G. 1996, MNRAS, 282, L53 Article number, page 13 of 16 A&A proofs:manuscript no. Maindoc

    1996

  34. [42]

    S., Carrera, F

    Mateos, S., Warwick, R. S., Carrera, F. J., et al. 2008, A&A, 492, 51

    2008

  35. [43]

    D., Wallis, C

    McEwen, J. D., Wallis, C. G. R., Price, M. A., & Spurio Mancini, A. 2021, arXiv e-prints, arXiv:2111.12720

    2021 arXiv

  36. [44]

    2022, MNRAS, 512, 2961

    McKaig, J., Ricci, C., Paltani, S., & Satyapal, S. 2022, MNRAS, 512, 2961

    2022

  37. [45]

    & Fabian, A

    Miniutti, G. & Fabian, A. C. 2004, MNRAS, 349, 1435

    2004

  38. [46]

    2009, A&A, 493, 501

    Moretti, A., Pagani, C., Cusumano, G., et al. 2009, A&A, 493, 501

    2009

  39. [47]

    M., Nikutta, R., Ivezi ´c, Ž., & Elitzur, M

    Nenkova, M., Sirocky, M. M., Nikutta, R., Ivezi ´c, Ž., & Elitzur, M. 2008, ApJ, 685, 160

    2008

  40. [48]

    & Ricci, C

    Paltani, S. & Ricci, C. 2017, A&A, 607, A31

    2017

  41. [49]

    M., et al

    Paltani, S., Walter, R., McHardy, I. M., et al. 2008, A&A, 485, 707

    2008

  42. [50]

    & Walter, R

    Panagiotou, C. & Walter, R. 2019, A&A, 626, A40

    2019

  43. [51]

    2021, A&A, 653, A162

    Panagiotou, C., Walter, R., & Paltani, S. 2021, A&A, 653, A162

    2021

  44. [52]

    A., Nandra, K., Stewart, G

    Pounds, K. A., Nandra, K., Stewart, G. C., George, I. M., & Fabian, A. C. 1990, Nature, 344, 132 Ramos Almeida, C. & Ricci, C. 2017, Nature Astronomy, 1, 679

    1990

  45. [53]

    2013, A&A, 555, A42

    Ranalli, P., Comastri, A., Vignali, C., et al. 2013, A&A, 555, A42

    2013

  46. [54]

    2003, A&A, 411, 329

    Revnivtsev, M., Gilfanov, M., Sunyaev, R., Jahoda, K., & Markwardt, C. 2003, A&A, 411, 329

    2003

  47. [55]

    T., Temple, M

    Ricci, C., Ananna, T. T., Temple, M. J., et al. 2022, ApJ, 938, 67

    2022

  48. [56]

    C., Fabian, A

    Ricci, C., Ho, L. C., Fabian, A. C., et al. 2018, MNRAS, 480, 1819

    2018

  49. [57]

    & Paltani, S

    Ricci, C. & Paltani, S. 2023, ApJ, 945, 55

    2023

  50. [58]

    J., et al

    Ricci, C., Ueda, Y ., Koss, M. J., et al. 2015, ApJ, 815, L13

    2015

  51. [59]

    Ricci, C., Walter, R., Courvoisier, T. J. L., & Paltani, S. 2011, A&A, 532, A102

    2011

  52. [60]

    2015, MNRAS, 454, 1202

    Sazonov, S., Churazov, E., & Krivonos, R. 2015, MNRAS, 454, 1202

    2015

  53. [61]

    2023, MNRAS, 525, 4735

    Tagliacozzo, D., Marinucci, A., Ursini, F., et al. 2023, MNRAS, 525, 4735

    2023

  54. [62]

    2020, Journal of Open Source Software, 5, 2505

    Tejero-Cantero, A., Boelts, J., Deistler, M., et al. 2020, Journal of Open Source Software, 5, 2505

    2020

  55. [63]

    M., & Virani, S

    Treister, E., Urry, C. M., & Virani, S. 2009, ApJ, 696, 110

    2009

  56. [64]

    Tristram, K. R. W., Burtscher, L., Jaffe, W., et al. 2014, A&A, 563, A82

    2014

  57. [65]

    Ueda, Y ., Akiyama, M., Hasinger, G., Miyaji, T., & Watson, M. G. 2014, ApJ, 786, 104

    2014

  58. [66]

    2003, ApJ, 598, 886

    Ueda, Y ., Akiyama, M., Ohta, K., & Miyaji, T. 2003, ApJ, 598, 886

    2003

  59. [67]

    P., & Spaans, M

    Wada, K., Papadopoulos, P. P., & Spaans, M. 2009, ApJ, 702, 63

    2009

  60. [68]

    Wilkins, D. R. & Fabian, A. C. 2011, MNRAS, 414, 1269

    2011

  61. [69]

    Willingale, R., Starling, R. L. C., Beardmore, A. P., Tanvir, N. R., & O’Brien, P. T. 2013, MNRAS, 431, 394

    2013

  62. [70]

    2018, ApJ, 854, 33 Article number, page 14 of 16 D

    Zappacosta, L., Comastri, A., Civano, F., et al. 2018, ApJ, 854, 33 Article number, page 14 of 16 D. Gerolymatou et al: Self-consistent AGN population synthesis in the X-rays Appendix A: SBI posteriors Fig. A.1: Parameter posteriors using the simple torus model when applied on...

    2018

Pith tools

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