Pith. sign in

REVIEW 3 major objections 4 minor 1 cited by

X-ray reverberation modelling of the observed UV/optical power spectra of quasars

T0 review · 3 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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

Pith's one-line read X-ray reverberation can explain quasar UV/optical flickering across 1300–4000 Å, provided the X-ray corona is powered by the accretion flow.

desk verdict Worth reading: a transparent grid fit of X-ray reverberation to ensemble quasar PSDs, but the headline spin/height constraints and the case-A-vs-B rejection all rest on an adopted X-ray PSD that the paper does not stress-test. read the letter →

arxiv 2509.03159 v1 pith:FOUDRUBU submitted 2025-09-03 astro-ph.HE astro-ph.GA

M. Papoutsis , I. E. Papadakis , C. Panagiotou , E. Kammoun , M. Dovciak This is my paper
classification astro-ph.HEastro-ph.GA
keywords accretiondiscsactivegalacticnucleiX-rayreverberationquasarvariabilitypowerspectracoronageometryblackholespinlamp-postmodel
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 tests whether the observed ultraviolet and optical variability of distant quasars can be produced entirely by X-ray illumination of the accretion disc—the X-ray reverberation scenario. Using a lamp-post corona model and observed ensemble power spectra of SDSS Stripe-82 quasars, the authors claim that reverberation alone fits the power spectra well at all wavelengths from 1300 Å to 4000 Å. The fits require that the corona be powered by energy extracted from the accretion disc, not by an external source; an externally powered corona under-predicts the variability amplitude by more than an order of magnitude. The best fits also imply black hole spins lower than about 0.7 and a compact X-ray source sitting 20–60 gravitational radii above the disc.

What carries the argument

The disc transfer function |Γλ(ν)|², computed as the Fourier transform of the disc response function Ψλ(t), is the central object. It converts an assumed X-ray power spectrum into a predicted UV/optical power spectrum through PSDλ(ν) = |Γλ,norm(ν)|² · PSDX(ν) · L²_X,Edd. The response functions come from the KYNXiltr code, which incorporates general-relativistic light bending, disc reflection, and a distinction between accretion-powered (case A) and externally powered (case B) coronae. The transfer function's frequency shape—flat at low frequencies, bending down at high frequencies, with an additional flattening feature for case A—is what allows the model to match the observed power spectra.

What would settle it

Measure the X-ray power spectrum of a Stripe-82 quasar with the same black hole mass and accretion rate as the sample bins; if its break frequency or normalization deviates from the assumed Seyfert scaling relations by more than the adopted 0.1 dex uncertainty, the predicted UV/optical PSD amplitude would shift linearly and the spin and height constraints would no longer hold. A second test: search the UV/optical PSD of a lower-mass AGN for the predicted high-frequency flattening feature (around ν ≳ 0.1 day⁻¹ for M_BH = 8×10⁸ M☉); its absence would rule out the accretion-powered corona signatu

Watch

Extended reading notes

Core claim

Under the assumption that each quasar's X-ray power spectrum follows a bending power law calibrated on local Seyferts, the authors show that the predicted UV/optical power spectrum, computed as the disc transfer function times the X-ray power spectrum, reproduces the observed quasar power spectra across six rest-frame wavelengths. The central quantitative result is the rejection of an externally powered corona: in that case the transfer function normalized to the disc flux is more than ten times too small to match the observed variability. Only an accretion-powered corona, with roughly 70–80 percent of the inner accretion power transferred to the X-ray source, matches the data. The best-fit

Load-bearing premise

The predicted UV/optical power spectrum is directly proportional to an assumed X-ray power spectrum that is calibrated from nearby Seyferts, not measured for these quasars; if the true quasar X-ray PSD differs in normalization or shape, the inferred corona height, spin, and rejection of the external corona would change.

Editorial extensions

If this is right

  • If correct, the observed UV/optical variability of quasars in the Stripe-82 sample is dominated by X-ray reprocessing rather than intrinsic disc fluctuations, since adding an intrinsic −1 slope power-law component does not significantly improve the fits.
  • The inferred corona height of 20–60 Rg and spin below 0.7 become physical constraints on the accretion geometry of luminous quasars, consistent with earlier micro-lensing disc-size and time-lag results.
  • The high-frequency flattening feature predicted for an accretion-powered corona offers a direct observational test: it should appear in the UV/optical PSDs of lower-mass AGNs at frequencies above the Poisson noise level.
  • The model reinforces the 'universal PSD shape' idea: after rescaling by the gravitational timescale, the predicted power spectra for different black hole masses converge to a common shape.
  • Future dense long-baseline monitoring (e.g., LSST, ULTRASAT, UVEX) could extend these power spectra over a wide frequency range and sharpen the constraints on corona height, spin, and the accretion-power transfer fraction.

Reading between the lines

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

  • A natural extension is to test the model on individual quasars with measured X-ray light curves, rather than ensemble-averaged spectra, which would remove the main systematic uncertainty and directly check the predicted UV/optical lag and PSD amplitude.
  • The result that an externally powered corona under-predicts variability by more than an order of magnitude suggests that any successful alternative to the lamp-post model must also produce a large reprocessed-to-intrinsic disc flux ratio in the UV/optical bands.
  • The spin constraint (α* < 0.7) is conditional on the assumed X-ray PSD normalization; if quasar X-ray PSDs are systematically more variable than Seyfert scaling relations predict, the spin and height estimates would shift.
  • Because the model assumes a single fixed spin for all quasars in the sample, a more realistic treatment allowing a spin distribution would change the inferred parameters and likely improve the scatter in the residuals; this could be tested with mock quasar populations.
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

3 major / 4 minor

Summary. The paper computes accretion-disc response/transfer functions for X-ray reprocessing using the KYNXiltr/KYNSED models (K23, Dovčiak et al. 2022), studies their dependence on wavelength, Ltransf/Ldisc, BH spin, and fcol, and then fits UV/optical power spectra predicted from an assumed X-ray bending-power-law PSD to the ensemble quasar PSDs of Petrecca et al. (2024). The claim is that X-ray reverberation can fit the observed quasar PSDs from 1300–4000 Å, but only for an accretion-powered corona, with best-fit parameters Ltransf/Ldisc ~ 0.7–0.8, h ~ 20–60 Rg, fcol ~ 2, and spin < 0.7. The central formalism is standard and the grid fitting is transparent, but the physical conclusions are conditional on an assumed X-ray PSD normalization calibrated on local Seyferts, and the statistical treatment of the P24 PSD points as independent data is questionable.

Significance. If the result holds, it would be an important step: the apparently universal UV/optical PSD shape of quasars could be interpreted physically as X-ray disc reprocessing, and the inferred corona height, spin, and coronal power source would be directly relevant to AGN accretion physics. The paper uses a state-of-the-art radiative-transfer code, computes transfer functions from physical inputs rather than fitting them, and is transparent about its assumptions and limitations. However, the headline constraints are only as secure as the adopted X-ray PSD, which is not measured for these quasars; the discrimination between the two coronal power scenarios is essentially an amplitude argument, and the statistical framework treats strongly correlated, derived PSD points as independent. The qualitative agreement is encouraging, but the quantitative parameter claims need additional robustness analysis.

major comments (3)
  1. [§7.2, Eq. (10)] The rejection of the externally powered corona is presented as an amplitude argument, but §7.2 compares only |Γ_norm|^2 and states it is “more than 10 times lower” in case B. The actual model PSD is PSD_{λ,mod} = |Γ_norm|^2 · PSD_X · L_X^2 (Eq. 10). In case B, increasing |Ltransf/Ldisc| increases L_X^2 while decreasing |Γ_norm|^2, so the net amplitude may be substantially less suppressed than the transfer-function amplitude alone. Please report the net product |Γ_norm|^2·L_X^2 over the allowed case-B grid, and show explicitly that even including L_X^2 the case-B model cannot reach the observed PSD amplitude.
  2. [§5.1, Eqs. (7)–(10), §8] The X-ray PSD of the target quasars is unknown and is taken from local Seyfert scalings: A = 6×10^{-3} ṁ^{-0.8} (Eq. 8) and the McHardy et al. (2006) bend relation (Eq. 9). Since Eq. (10) is linear in PSD_X, the predicted UV/optical amplitude scales linearly with A. A factor-of-2–3 change in A—well within the scatter of Ponti et al. (2012) and the extrapolation from local Seyferts to z~1–2 quasars—rescales all model amplitudes and could shift the inferred Ltransf/Ldisc, h, fcol, and spin, and could alter the case-B conclusion. The authors acknowledge the unknown X-ray PSD in §8, but they do not quantify the sensitivity of the headline parameters to A. Please add a sensitivity test, e.g. refit with A varied by its 1σ scatter or by factors of 2–3.
  3. [§6, Eq. (12)] The three rest-frame frequencies used in the χ² fit are not independent measurements: they derive from P24’s two-parameter power-law fit, log PSD(ν) = log PSD_amp + PSD_slope[log ν + 2.6], so the PSD values at the three frequencies are perfectly correlated functions of the fitted amplitude and slope. Treating them as 18 independent points per (MBH, ṁEdd) bin in Eq. (12) overstates the number of constraints and affects the reported χ² and the parameter uncertainties. Please fit directly in the (PSD_amp, PSD_slope) space, propagate the P24 covariance matrix, or otherwise justify the independence assumption.
minor comments (4)
  1. [§7.1, Table 1] The fcol grid contains only 1, 1.7, and 2.5, but the text reports a mean best-fit fcol ~ 2. Please clarify whether the reported value is an interpolation between grid points or a grid-edge/integrated estimate, and indicate the resulting systematic uncertainty.
  2. [§5.3] The aliasing correction uses a single rest-frame sampling rate and a simplified cutoff at (1+z)/90 day^{-1}, although the P24 light curves are irregularly binned. The ad hoc 0.1 dex model error may not fully cover this simplification. A short discussion or test of the sensitivity of the fits to the aliasing prescription would strengthen the conclusions.
  3. [§3.4.5, Fig. 2] The statement that the dependence of |Γ_norm|^2 on fcol “becomes weaker (and even non-existent in the case B corona)” is based on λ=3000 Å and the fiducial parameters; please state explicitly that this is parameter-dependent, since the text just before it describes a general fcol dependence.
  4. [General] There are numerous typographical issues: “di fferent”, “˙mEdd” rendering, “Sect. 8.1” equation references, and the use of “K21a/K23/P22” without expanding the first time in the abstract or introduction. A careful language and notation pass is needed.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the UV/optical PSD is computed by convolving an externally assumed X-ray PSD with physically computed transfer functions, and the fits are compared against observed PSDs rather than fed back into the model.

full rationale

The derivation chain is: assume a lamp-post X-ray source and disc response (K23 code); compute the response/transfer functions from radiative transfer; adopt an X-ray PSD shape, normalization and bend frequency from external local-Seyfert relations (Ponti et al. 2012; McHardy et al. 2006; Eqs. 7-9); form model UV/optical PSDs via Eqs. (2) and (10); and fit to the P24 observed ensemble PSDs. At no step is a fitted parameter reinserted as a prediction: Ltransf/Ldisc, h, fcol and alpha* are free parameters entering the transfer function, and they are determined by the fit, not defined from the observed PSDs. The X-ray PSD normalization A is not fitted to the UV/optical data; it is an external input, so the proportionality PSD_lambda,mod = |Gamma_norm|^2 * PSD_X * L_X^2 (Eq. 10) makes the predictions conditional on that input, but not circular. The paper explicitly acknowledges this limitation in Sect. 8: 'the X-ray PSD of the quasars in the sample is unknown' and that it 'directly affects the model UV/optical PSD'. The self-citations (K21a, K23, P22, KYNSED, and the supporting Papadakis et al. 2022 / Langis et al. 2024 comparisons) are methodological or corroborative; no uniqueness theorem is imported from them, and the model is judged against the P24 SDSS PSD measurements, which are empirical even though one author overlaps. The case-A/case-B distinction and the rejection of the externally powered corona are outputs of the comparison, not restatements of the model inputs. Overall, the paper's central claim is a conditional fit rather than a self-referential derivation; the main caveat is sensitivity to the assumed X-ray PSD amplitude, which is a limitation, not circularity.

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

The model's predictive power rests on a chain of empirical X-ray PSD relations and on the physical correctness of the KYNXiltr/KYNSED code. The key fitted parameters are the corona power ratio, height, colour-correction factor, and spin. No new particles or forces are postulated; the lamp-post corona is a standard, not invented, entity.

free parameters (4)
  • Ltransf/Ldisc = best-fit ~0.7-0.8 for alpha*=0; grid -1.9 to 0.9
    Ratio of X-ray luminosity to disc accretion power; sign distinguishes accretion-powered (case A) from externally powered (case B) corona. Fitted per (MBH, mdot) bin.
  • Corona height h = ~60 Rg (alpha*=0), 20-40 Rg (alpha*=0.7); grid 2.5-90 Rg
    Height of the lamp-post X-ray source; fitted per bin; controls amplitude and break of the transfer function.
  • Colour-correction factor fcol = ~2 (grid 1, 1.7, 2.5)
    Spectral hardening factor; fitted per bin; strongly affects transfer function amplitude.
  • Black hole spin alpha* = 0, 0.7, 0.998; 0.998 disfavoured by Delta chi2 > 38
    Global parameter fixed across all mass/accretion bins; conclusion 'spin < 0.7' depends on the coarse grid.
assumptions (6)
  • domain assumption Quasar X-ray PSDs follow a bending power law with low-frequency slope -1 and high-frequency slope -2 (Eq. 7).
    The X-ray PSDs of the target quasars are not measured; this shape is imported from local Seyfert studies (Uttley 2002, Markowitz 2003, McHardy 2004). It directly sets the shape of model UV/optical PSDs in Eq. (10).
  • domain assumption X-ray PSD normalization A = 6e-3 mdot^-0.8 (Eq. 8).
    From Ponti et al. 2012 and Paolillo et al. 2017. The amplitude of the predicted UV/optical PSD scales with A, so the case B rejection depends on this value.
  • domain assumption X-ray PSD bend frequency obeys the McHardy et al. 2006 relation log Tb = 2.1 log MBH6 - 0.98 log Lbol44 - 2.32 (Eq. 9).
    Empirical relation from nearby AGN, extrapolated to high-redshift quasars; positions the X-ray break that shapes the model UV/optical PSD.
  • domain assumption Eddington ratio equals accretion rate, lambda_Edd ~ mdot_Edd.
    Used to map P24's [MBH, Lbol] bins to the KYNXiltr input mdot (Sect. 4). Deviations shift the assumed X-ray PSD normalization and break.
  • domain assumption Lamp-post point-source corona geometry with isotropic emission, XILLVER reflection, and color-corrected blackbody disc emission.
    The entire transfer function calculation in Sect. 3.1 assumes this geometry. Authors defend it via KYNSED/MONK comparisons and Zhang et al. 2024, but the response functions are not directly validated in this paper.
  • domain assumption KYNXiltr/KYNSED correctly compute response functions, disc fluxes, and X-ray luminosity for the model parameters.
    All model power spectra depend on this code suite; no independent verification or machine-checked proof is provided.

how reviews work

0 comments
Cite this review

Pith. "Pith review of X-ray reverberation modelling of the observed UV/optical power spectra of quasars." pith.science (2026). https://pith.science/paper/FOUDRUBU

@misc{pith2026250903159,
  author       = {Pith},
  title        = {Pith review of: X-ray reverberation modelling of the observed UV/optical power spectra of quasars},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FOUDRUBU}},
  note         = {Machine review of arXiv:2509.03159}
}
abstract

Over the past decade, a significant amount of effort has been put into investigating the ultraviolet (UV) and optical variability of active galactic nuclei (AGNs). Comprehensive studies of intensive multi-wavelength monitoring and surveys of local and high-redshift AGNs have shown that X-ray illumination of AGN accretion discs is a potential explanation for the observed variability. Our main objective is to study the UV/optical power spectra of AGNs under the assumption of X-ray reverberation and to test whether this model can explain the observed power spectra of distant quasars. To do this, we computed the disc transfer function in the case of X-ray reverberation using a recent physical model and studied its dependence on the parameters of the model. This model allows us to explore the variability of X-ray illuminated discs under the scenario in which the X-ray corona is powered by the accretion process or by an external source. We then calculated UV/optical power spectra using the disc transfer function and assuming a bending power law for the X-ray power spectrum. We fitted our models to the observed power spectra of quasars determined by a recent power spectrum analysis of the SDSS Stripe-82 light curves. We demonstrate that X-ray reverberation can fit the power spectra of quasars in our sample well at all wavelengths, from $\sim 1300$\AA\ up to $4000$\AA. Our best-fit models imply that the X-ray corona is powered by the accretion disc, and that the black hole spin is probably lower than 0.7, while the X-ray corona height is in the range of $20 - 60 R_{g}$. This is in agreement with previous findings from the application of the X-ray reverberation model to the quasar micro-lensing disc size problem, as well as recent time-lag measurements.

Figures

Figures reproduced from arXiv: 2509.03159 by the authors.

Figure 1
Figure 1. Transfer functions |Γλ(ν)| 2 (top panel) and normalised transfer functions |Γλ,norm(ν)| 2 (bottom panel) in three wavebands: λ = 1300Å (blue line), λ = 3000Å (green line), and λ = 5000Å (red line). The solid lines correspond to the case of an accretion-powered corona (case A) with Ltransf/Ldisc = 0.5 and the dashed lines to an externally powered corona (case B) with Ltransf/Ldisc = −0.5. The fiducial parameters are:… view at source ↗
Figure 2
Figure 2. Transfer functions |Γλ(ν)| 2 (top panels) and normalised transfer functions |Γλ,norm(ν)| 2 (bottom panels) for different values of Ltransf/Ldisc (left panels), α ∗ (middle panels), and fcol (right panels) at λ = 3000Å. We show positive values of Ltransf/Ldisc with solid lines (case A) and negative ones with dashed lines (case B). The fiducial parameters are: MBH = 8 × 108M⊙, ˙mEdd=0.1, h = 20, Γ = 2, θ = 30o , Ecut … view at source ↗
Figure 3
Figure 3. Blue circles, green squares, and red triangles indicate the observed PSDs at λ=1300Å, 2300Å, 4000Å, respectively, for all combinations of BH mass and accretion rate we considered. Solid blue lines, dashed green lines, and dash-dotted red lines indicate the best-fit models for α ∗=0, in the case in which the corona is powered by the accretion process (note that, for clarity reasons, the ranges of the axes are differe… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Model power spectrum at λ = 3000Å (solid blue line) for the parameters: MBH = 2 × 108M⊙, ˙mEdd = 0.4, h = 20, Γ = 2, θ = 30o , α ∗ = 0, Rout = 5000Rg, Ecut = 150keV, Ltransf/Ldisc = 0.5, and fcol = 1.7. The dashed red line and the dash-dotted green lines indicate the c…
Figure 5
Figure 5. Figure 5: Best-fit results in the case A corona. The α ∗=0 best-fitting Ltransf/Ldisc (left panel), h (middle panel), and fcol (right panel) are plotted as a function of ˙mEdd for logMBH = 8.3 (blue circle points), logMBH = 8.9 (green square points), and logMBH = 9.5 (red triang…
Figure 6
Figure 6. Figure 6: Model power spectrum for log MBH = 8.3, 8.9, 9.5, ˙mEdd= 0.1 and λ = 3000Å, and their rescaled versions shown with the black lines (see Sect.8.2 for more details). could complement these long-term light curves with minute￾scale cadence ultraviolet observations over mon…

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.

  1. X-ray disc reverberation modelling of the X-ray/UV/optical spectral/timing properties of Fairall 9

    astro-ph.GA 2026-07 conditional novelty 5.0 of 10

    X-ray reprocessing by the accretion disc in Fairall 9 simultaneously explains the SED, UV/optical power spectra, and interband time lags only if the corona is powered externally rather than by accretion.

Reference graph

Works this paper leans on

55 extracted references · 34 canonical work pages · cited by 1 Pith paper

  1. [1]

    2024, A&A, 684, A133

    Arévalo, P., Churazov, E., Lira, P., et al. 2024, A&A, 684, A133

    work page 2024

  2. [2]

    2009, ApJ, 696, 1241

    Bauer, A., Baltay, C., Coppi, P., et al. 2009, ApJ, 696, 1241

    2009

  3. [3]

    C., Kulkarni, S

    Bellm, E. C., Kulkarni, S. R., Graham, M. J., et al. 2019, PASP, 131, 018002

    2019

  4. [4]

    M., Gelbord, J., Li, Y .-R., et al

    Cackett, E. M., Gelbord, J., Li, Y .-R., et al. 2020, ApJ, 896, 1

    work page 2020

  5. [5]

    2017, MNRAS, 472, 4051

    Calderone, G., Nicastro, L., Ghisellini, G., et al. 2017, MNRAS, 472, 4051

    work page 2017

  6. [6]

    C., Magnier, E

    Chambers, K. C., Magnier, E. A., Metcalfe, N., et al. 2016, arXiv e-prints, arXiv:1612.05560

    arXiv 2016

  7. [7]

    2006, in Astronomical Society of the Pacific Conference Series, V ol

    Czerny, B. 2006, in Astronomical Society of the Pacific Conference Series, V ol. 360, AGN Variability from X-Rays to Radio Waves, ed. C. M. Gaskell, I. M

    2006

  8. [8]

    Davis, S. W. & El-Abd, S. 2019, ApJ, 874, 23 De Cicco, D., Bauer, F. E., Paolillo, M., et al. 2022, A&A, 664, A117 Dovˇciak, M., Papadakis, I. E., Kammoun, E. S., & Zhang, W. 2022, A&A, 661, A135

    work page 2019

Show all 55 references
  1. [9]

    M., Gelbord, J., et al

    Edelson, R., Peterson, B. M., Gelbord, J., et al. 2024, ApJ, 973, 152

    2024

  2. [10]

    E., Epitropakis, A., et al

    Emmanoulopoulos, D., Papadakis, I. E., Epitropakis, A., et al. 2016, MNRAS, 461, 1642

    2016

  3. [11]

    E., Dovˇciak, M., et al

    Epitropakis, A., Papadakis, I. E., Dovˇciak, M., et al. 2016, A&A, 594, A71 Gaia Collaboration, Prusti, T., de Bruijne, J. H. J., et al. 2016, A&A, 595, A1 García, J., Dauser, T., Reynolds, C. S., et al. 2013, ApJ, 768, 146 García, J. A., Fabian, A. C., Kallman, T. R., et al. ...

    2016

  4. [12]

    2021, ARA&A, 59, 21

    Gezari, S. 2021, ARA&A, 59, 21

    2021

  5. [13]

    E., Kim, D

    Gianolli, V . E., Kim, D. E., Bianchi, S., et al. 2023, MNRAS, 523, 4468 González-Martín, O. & Vaughan, S. 2012, A&A, 544, A80

    2023

  6. [14]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357

    2020

  7. [15]

    R., Grier, C

    Homayouni, Y ., Trump, J. R., Grier, C. J., et al. 2019, ApJ, 880, 126

    2019

  8. [16]

    Hunter, J. D. 2007, Computing in Science and Engineering, 9, 90 Ivezi´c, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111

    2007

  9. [17]

    K., Joshi, R., Chand, H., et al

    Jha, V . K., Joshi, R., Chand, H., et al. 2022, MNRAS, 511, 3005

    2022

  10. [18]

    J., Greene, J

    Jiang, Y .-F., Green, P. J., Greene, J. E., et al. 2017, ApJ, 836, 186

    2017

  11. [19]

    S., Robin, L., Papadakis, I

    Kammoun, E. S., Robin, L., Papadakis, I. E., Dov ˇciak, M., & Panagiotou, C. 2023, MNRAS, 526, 138

    2023

  12. [20]

    A., et al

    Kara, E., Mehdipour, M., Kriss, G. A., et al. 2021, ApJ, 922, 151

    2021

  13. [21]

    R., Harrison, F

    Kulkarni, S. R., Harrison, F. A., Grefenstette, B. W., et al. 2021, arXiv e-prints, arXiv:2111.15608

    2021 arXiv

  14. [22]

    A., Papadakis, I

    Langis, D. A., Papadakis, I. E., Kammoun, E., Panagiotou, C., & Dov ˇciak, M. 2024, A&A, 691, A252

    2024

  15. [23]

    Lyubarskii, Y . E. 1997, MNRAS, 292, 679

    1997

  16. [24]

    L., Ivezi´c, Ž., Sesar, B., et al

    MacLeod, C. L., Ivezi´c, Ž., Sesar, B., et al. 2012, ApJ, 753, 106

    2012

  17. [25]

    2003, ApJ, 593, 96

    Markowitz, A., Edelson, R., Vaughan, S., et al. 2003, ApJ, 593, 96

    2003

  18. [26]

    M., Koerding, E., Knigge, C., Uttley, P., & Fender, R

    McHardy, I. M., Koerding, E., Knigge, C., Uttley, P., & Fender, R. P. 2006, Nature, 444, 730

    2006

  19. [27]

    M., Papadakis, I

    McHardy, I. M., Papadakis, I. E., Uttley, P., Page, M. J., & Mason, K. O. 2004, MNRAS, 348, 783

    2004

  20. [28]

    2018, ApJ, 862, 123

    Mudd, D., Martini, P., Zu, Y ., et al. 2018, ApJ, 862, 123

    2018

  21. [29]

    2024, MNRAS, 527, 6233

    Mummery, A. 2024, MNRAS, 527, 6233

    2024

  22. [30]

    F., Edelson, R., Baumgartner, W., & Gandhi, P

    Mushotzky, R. F., Edelson, R., Baumgartner, W., & Gandhi, P. 2011, ApJ, 743, L12

    2011

  23. [31]

    2022, MNRAS, 509, 2637

    Netzer, H. 2022, MNRAS, 509, 2637

    2022

  24. [32]

    Novikov, I. D. & Thorne, K. S. 1973, in Black Holes (Les Astres Occlus), 343– 450

    1973

  25. [33]

    2022, ApJ, 935, 93

    Panagiotou, C., Papadakis, I., Kara, E., Kammoun, E., & Dovˇciak, M. 2022, ApJ, 935, 93

    2022

  26. [34]

    N., et al

    Paolillo, M., Papadakis, I., Brandt, W. N., et al. 2017, MNRAS, 471, 4398

    2017

  27. [35]

    2016, A&A, 588, A13

    Papadakis, I., Pecháˇcek, T., Dovˇciak, M., et al. 2016, A&A, 588, A13

    2016

  28. [36]

    E., Dovˇciak, M., & Kammoun, E

    Papadakis, I. E., Dovˇciak, M., & Kammoun, E. S. 2022, A&A, 666, A11

    2022

  29. [37]

    E., Panagiotou, C., Dov ˇciak, M., & Kammoun, E

    Papoutsis, M., Papadakis, I. E., Panagiotou, C., Dov ˇciak, M., & Kammoun, E. 2024, A&A, 691, A60

    2024

  30. [38]

    E., Paolillo, M., De Cicco, D., & Bauer, F

    Petrecca, V ., Papadakis, I. E., Paolillo, M., De Cicco, D., & Bauer, F. E. 2024, A&A, 686, A286 Podgorný, J., Dovˇciak, M., Goosmann, R., et al. 2023, MNRAS, 524, 3853

    2024

  31. [39]

    2012, A&A, 542, A83

    Ponti, G., Papadakis, I., Bianchi, S., et al. 2012, A&A, 542, A83

    2012

  32. [40]

    A., Nandra, K., Stewart, G

    Pounds, K. A., Nandra, K., Stewart, G. C., George, I. M., & Fabian, A. C. 1990, Nature, 344, 132

    1990

  33. [41]

    1981, Spectral Analysis and Time Series, Probability and mathe- matical statistics (Academic Press)

    Priestley, M. 1981, Spectral Analysis and Time Series, Probability and mathe- matical statistics (Academic Press)

    1981

  34. [42]

    S., & Kotilainen, J

    Rakshit, S., Stalin, C. S., & Kotilainen, J. 2020, ApJS, 249, 17

    2020

  35. [43]

    R., Fabian, A

    Ross, R. R., Fabian, A. C., & Mineshige, S. 1992, MNRAS, 258, 189

    1992

  36. [44]

    O., et al

    Sagiv, I., Gal-Yam, A., Ofek, E. O., et al. 2014, AJ, 147, 79

    2014

  37. [45]

    Shakura, N. I. & Sunyaev, R. A. 1973, A&A, 24, 337

    1973

  38. [46]

    L., Mushotzky, R

    Smith, K. L., Mushotzky, R. F., Boyd, P. T., et al. 2018, ApJ, 857, 141

    2018

  39. [47]

    P., Hinkle, J

    Treiber, H. P., Hinkle, J. T., Fausnaugh, M. M., et al. 2023, MNRAS, 525, 5795

    2023

  40. [48]

    Ulrich, M.-H., Maraschi, L., & Urry, C. M. 1997, ARA&A, 35, 445

    1997

  41. [49]

    M., & Papadakis, I

    Uttley, P., McHardy, I. M., & Papadakis, I. E. 2002, MNRAS, 332, 231 van der Klis, M. 1988, Timing Neutron Stars, 262, 27 van Velzen, S., Holoien, T. W. S., Onori, F., Hung, T., & Arcavi, I. 2020, Space Sci. Rev., 216, 124

    2002

  42. [50]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261

    2020

  43. [51]

    2024, Space Sci

    Werner, N., ˇRípa, J., Thöne, C., et al. 2024, Space Sci. Rev., 220, 11

    2024

  44. [52]

    M., et al

    Yu, Z., Martini, P., Davis, T. M., et al. 2020, ApJS, 246, 16 Zajaˇcek, M., Czerny, B., Jaiswal, V . K., et al. 2024, Space Sci. Rev., 220, 29

    2020

  45. [53]

    2019, ApJ, 875, 148

    Zhang, W., Dovˇciak, M., & Bursa, M. 2019, ApJ, 875, 148

    2019

  46. [54]

    2024, MNRAS, 532, 3786

    Zhang, W., Dovˇciak, M., Bursa, M., Svoboda, J., & Karas, V . 2024, MNRAS, 532, 3786

    2024

  47. [55]

    S., Kozłowski, S., & Udalski, A

    Zu, Y ., Kochanek, C. S., Kozłowski, S., & Udalski, A. 2013, ApJ, 765, 106 Article number, page 12 of 13 M. Papoutsis et al.: UV/opt AGN variability Appendix A: The best-fit residuals λ(Å) Fig. A.1. The best-fit residuals (i.e. (data-model) /error) for the case where the coron...

    2013

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.