Pith. sign in

REVIEW 2 major objections 5 minor 1 cited by

BASS. XLIX. Characterization of highly luminous and obscured AGNs: local X-ray and [NeV]$\lambda$3426 emission in comparison with the high-redshift Universe

T0 review · 2 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Pith's one-line read Local AGNs show [NeV] where JWST AGNs show none, hinting high-redshift obscured AGNs are weaker in [NeV] or harder to detect.

desk verdict A solid local benchmark paper whose headline [NeV] comparison with JWST AGNs is real but not yet pinned down, because the [OIII] peak normalization does not by itself guarantee comparable NLR ionization conditions. read the letter →

arxiv 2507.10674 v1 pith:FDVQPTGA submitted 2025-07-14 astro-ph.GA astro-ph.HE

classification astro-ph.GAastro-ph.HE PACS 95.85.Nv98.54.Cm
keywords activegalacticnucleiobscuredAGN[Nev]λ3426emissionSwift/BATX-rayspectroscopyJWSThigh-redshiftEddingtonratioforbiddenregion
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 examines 21 of the most luminous, heavily obscured AGNs in the local Universe ($z<0.6$, absorption-corrected 2-10 keV luminosity above $10^{44.6}$ erg s$^{-1}$), selected in ultra-hard X-rays and modeled with NuSTAR together with softer X-ray data. It finds that the [Ne v]$\lambda$3426 line is detected in 85% of the sources, confirming that this high-ionization line remains a reliable AGN tracer even when the X-ray continuum is heavily absorbed. The central comparison stacks local optical spectra and renormalizes them to the same [O iii]$\lambda$5007 peak flux as JWST-selected narrow-line AGNs at $z=2$-$9$; the local stack shows [Ne v]$\lambda$3426 at about six times the JWST stack noise, while the JWST stack shows none. The paper interprets this as evidence that high-redshift obscured AGNs may be intrinsically weaker in [Ne v], or that the line is harder to detect in those environments, and presents the local sources as a benchmark for interpreting X-ray-weak AGNs at high redshift.

What carries the argument

The central object is the forbidden [Ne v] line at 3426 Å, a high-ionization narrow-line-region transition (ionization potential above 97 eV) that survives heavy torus absorption and therefore traces the AGN even when X-rays are blocked. The comparison is carried by a stacking and normalization procedure: local high-resolution spectra are degraded to JWST resolution, both stacks are renormalized to the [O iii]$\lambda$5007 peak flux, and the [Ne v] strength is read against the line-free rms of the JWST stack. Matching in [O iii] is the step meant to hold ionization conditions fixed, and the six-times-noise [Ne v] excess is the observable contrast the paper uses to distinguish local from high-redshift behavior.

What would settle it

Take the same JWST-selected narrow-line AGN sample and integrate a stack until the line-free rms in the 3250-3700 Å region drops below one sixth of the local [Ne v] flux; if [Ne v]$\lambda$3426 still does not appear at the local scaling level $L_{\rm [NeV]}/L_{\rm [OIII]} \simeq 0.06$, the paper's interpretation survives, and if it appears, the original deficit was a noise-level artifact of the comparison.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is a contrast between local and high-redshift obscured AGNs. When the stacked spectrum of 21 low-redshift, highly luminous, optically obscured AGNs is normalized to the [O iii]$\lambda$5007 peak flux of the JWST-selected narrow-line AGN stack at $z=2$-$9$, the local [Ne v]$\lambda$3426 line reaches roughly six times the JWST stack noise, while [Ne v] is absent from the JWST stack. Because the samples are matched in [O iii], the authors expect comparable ionization conditions, so the missing line suggests either intrinsically weaker [Ne v] production in high-redshift AGNs or heavy attenuation that makes [Ne v] harder to see there. The paper also characterizes the local sample as occupying a luminous, obscured region of the luminosity-column-density plane that previous surveys left almost empty, with half of the mass-estimated sources in the $N_H$-$\lambda_{\rm Edd}$ forbidden region and frequent flux and column-density variability.

Load-bearing premise

The load-bearing premise is that normalizing both stacks to the peak flux of [O iii]$\lambda$5007 makes the narrow-line regions of local and high-redshift AGNs physically comparable, so the missing [Ne v] in the JWST stack is a real difference in AGN properties rather than a difference in ionization parameter, metallicity, or dust geometry.

Editorial extensions

If this is right

  • If the [Ne v] deficit is real, JWST-selected narrow-line AGNs at $z=2$-$9$ are not simple high-luminosity analogs of local obscured AGNs, and their X-ray weakness may come with genuinely different narrow-line-region physics.
  • The 85% [Ne v] detection rate in heavily obscured local AGNs means future optical and near-infrared spectroscopy can uncover obscured AGNs whose X-rays are almost completely absorbed, including in high-redshift surveys.
  • The concentration of sources in the $N_H$-$\lambda_{\rm Edd}$ forbidden region with variability and outflow signatures supports a transient phase in which AGN feedback is clearing the obscuring material, which would affect how obscured fractions are interpreted as evolutionary states.
  • Deep X-ray surveys with planned observatories should recover column density, photon index, and luminosity for similar sources out to $z\sim5$, turning the local benchmark into a direct high-redshift measurement.

Reading between the lines

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

  • A testable extension is to search for the missing high-redshift [Ne v] at mid-infrared wavelengths where dust attenuation is much weaker; a detection there would attribute the 3426 Å deficit to dust, while a non-detection would favor intrinsically weaker coronal emission.
  • The [O iii]-matching assumption can be checked directly by also matching the local and JWST stacks on line ratios such as [O iii]/Hβ or on [O iii] line width, because if those differ, the peak-flux normalization alone does not guarantee comparable ionization conditions.
  • If the deficit is intrinsic, it implies measurable redshift evolution in the ionization state or metallicity of narrow-line regions, which could be mapped by stacking JWST spectra in redshift bins and comparing dust-corrected [Ne v]/[O iii] ratios.
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

2 major / 5 minor

Summary. The paper analyzes 21 Swift/BAT-selected Seyfert 1.9/2 AGNs at z<0.6 with 2-10 keV intrinsic luminosities above 10^44.6 erg/s (with five falling slightly below after detailed fitting). Using NuSTAR, XMM-Newton, Suzaku, and Chandra spectra, the authors fit four torus models with MCMC, deriving median log NH ~ 23.5, Gamma ~ 1.78, and LX ~ 10^44.7. They report a weak Gamma-lambda_Edd correlation, 6/12 sources in the NH-lambda_Edd forbidden region, variability in 11/13 multi-epoch sources, Fe Kalpha equivalent widths, and an 85% [NeV] lambda3426 detection rate. The central comparison stacks local optical spectra and the M24 JWST stack of z=2-9 narrow-line AGNs normalized to [OIII] lambda5007 peak flux, finding that [NeV] lambda3426 is present in the local stack but absent in the JWST stack, and interprets this as possible intrinsic weakness or detectability challenges at high redshift. The paper closes with AXIS/NewAthena simulations.

Significance. If the [NeV] comparison is robust, it would be an important constraint on the nature of X-ray-weak JWST-selected AGNs, and the local sample is genuinely valuable as a benchmark in a parameter space (log LX > 44.6, log NH > 22) that few other surveys populate. The X-ray spectral analysis is careful: four torus models, MCMC posterior exploration, DIC-based model selection, multi-epoch variability, and cross-checks against BASS DR1/DR2 are presented in detail. The paper also makes concrete, falsifiable predictions for AXIS and NewAthena count rates and parameter recovery. However, the high-redshift inference currently rests on a peak-flux normalization and local scaling relations whose transferability to z~2-9 is asserted rather than demonstrated; this weakens the paper's headline result.

major comments (2)
  1. [Section 6.2 / Figure 8] The conclusion that high-redshift JWST narrow-line AGNs are intrinsically weaker in [NeV] lambda3426 (or that the line is harder to detect there) is based entirely on comparing stacks normalized to the [OIII] lambda5007 peak flux. The paper does not establish that this normalization equates the narrow-line-region conditions in the two samples: the residual spectrum in Figure 8 shows line-width and peak-shape differences, and Section 7.2 supports the comparison only by asserting that matching [OIII] implies comparable ionization conditions. A broader high-redshift [OIII] profile, a softer ionizing SED, or a different ionization parameter would suppress the peak-normalized [NeV] signature by factors of several without any change in the integrated [NeV]/[OIII] ratio. The reported 'six times the noise' is a peak-height statement, not an integrated-flux measurement, and no quantitative upper limit on [NeV] in the JWST stack is given. I request integrated line-flux measurements (or a line-width-matched reanalysis) and a discussion of how the normalization affects the inferred deficit before the high-z conclusion is drawn.
  2. [Section 6.2] The 'expected [NeV] detectability' argument combines log(LX/L[OIII]) ~ 2.1 with the R25 relation log(L[NeV]/LX) ~ -3.36 to predict L[NeV]/L[OIII] ~ 0.06, then states this corresponds to a [NeV] flux 'well above the JWST noise level.' Both input relations are calibrated on local samples, the R25 relation specifically on BASS DR2 with overlapping authorship, and the step from integrated luminosity ratios to a peak-flux detection threshold in a lower-resolution JWST stack requires explicit assumptions about line widths, line ratios, and the [OIII]/[NeV] ratio. None of these assumptions is tested or propagated into an uncertainty. As written, the argument assumes the very similarity (local NLR physics) that the comparison is meant to test, and it should be either quantified with a photoionization calculation or removed as a supporting pillar of the high-z inference.
minor comments (5)
  1. [Abstract / Section 5.6] The abstract reports that 82^{+6}_{-16}% of the 13 multi-epoch sources vary in either flux or NH, with 73^{+9}_{-16}% varying in flux, while Section 5.6 reports 85^{+5}_{-15}% and 77^{+8}_{-15}% for the same quantities; the NH fraction (33%) agrees. These numbers should be reconciled.
  2. [Section 6.2] Section 6.1 states that optical data for BAT ID 119 are unsuitable for detailed analysis, but Section 6.2 says the stacked spectra were produced 'across all 21 sources in our sample.' Please clarify whether the stack contains 20 or 21 objects and how the source without usable optical data was treated.
  3. [Section 5.6] The variability statistic in Eq. (9) is applied to samples with as few as two epochs and asymmetric uncertainties, but the null distribution is assumed to be chi-squared without validation; a Monte Carlo calibration of the p-values (or a caveat about their interpretation) would make the variability fractions in Table 4 more robust.
  4. [Section 4 / Table 2] The text says the cut-off energy is fixed to 200 keV, and Table 2 reports the best-fit model and chi2/dof per source, but the appendix figures would benefit from a single summary table of the adopted model components (e.g., which sources use two apec components). The source-by-source notes are clear but spread over Appendix A.
  5. [Throughout] There are several typographical issues, including 'variaiblity' in Section 5.6 and the inconsistent use of 'RXTorusD' versus 'RXTorus' in Section 4.1.2; a careful proofread is needed.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity; the high-redshift [NeV] comparison is anchored to an external JWST stack, and the R25 scaling argument is a non-load-bearing consistency check.

full rationale

The paper's main observational result is a direct comparison between a locally stacked spectrum and the external M24 JWST stack, normalized to [O III] lambda 5007 peak flux; the local [NeV] lambda 3426 excess is measured from the stacks themselves and is not computed from any fitted parameter. The only arguably self-referential element is the Section 6.2 'expected [NeV]/[O III]' estimate, which combines the R25 log(L[NeV]/LX) relation calibrated on BASS DR2 (overlapping authors) with an average LX/L[O III] ratio and then notes that it 'matches what we observe in our stacked spectrum.' This is a consistency check rather than an input to the central finding: the discrepancy with JWST stands independently of the scaling relations. The Section 7.2 claim that [O III] matching ensures comparable ionization conditions is an unsupported physical assumption and a correctness risk, but not a circular reduction, since the [O III] normalization is an external matching scheme adopted from M24 rather than defined in terms of the [NeV] result. No fitted parameter is defined in terms of the predicted quantity, and no uniqueness or ansatz is imported from the authors' prior work to force the conclusion.

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

The central claims rest on a relatively small sample (21 sources, 12 with black hole masses) and on a chain of modeling and scaling assumptions: torus models, bolometric corrections, the forbidden-region theory, and the extrapolation of local [NeV]/X-ray scaling relations to high redshift. No new physical entities are introduced. The hand-chosen correction factor for the Gilli et al. comparison and the assumed Gamma=1.8 for luminosity selection are additional free choices that should be disclosed when the results are used.

free parameters (6)
  • Photon index Gamma = median 1.78, range ~1.60-2.29
    Free parameter in the X-ray spectral fits; enters the Gamma-lambda_Edd correlation and the absorption corrections. Standard fit parameter.
  • Line-of-sight column density NH = median log NH/cm^-2 = 23.5
    Free parameter in the torus model fits; central to the obscuration classification, the forbidden region analysis, and NH variability fractions.
  • Scattering fraction fscatt = 0.2-14.4% across sources
    Free parameter constrained to 0-20% of primary continuum; affects the intrinsic continuum and soft X-ray modeling.
  • Torus geometry parameters (theta_i, covering factor, TORsigma, CTKcover) = theta_i ~35-83 deg; C.F. ~0.4-0.8
    Fitted parameters of the chosen torus models. Covering factors for UXCLUMPY are derived by interpolating tabulated values from Boorman et al. (2024), which includes a co-author of this paper.
  • Hand-chosen correction factor for Gilli et al. (2010) comparison = 14
    Applied to convert observed to intrinsic X-ray fluxes for the Compton-thick threshold comparison, assuming Gamma=1.8 and NH=1e24 cm^-2. Ad hoc scaling affecting Figure 7.
  • Assumed photon index for DR3 luminosity selection = 1.8
    Used to estimate 2-10 keV luminosities from 14-195 keV fluxes for 105-month catalog sources during sample selection; contributes to five sources falling below threshold after re-fitting.
assumptions (6)
  • domain assumption Torus models (MYTorus, RXTorusD, UXCLUMPY) correctly describe obscuration and X-ray reprocessing in AGNs.
    The spectral fitting and derived NH, covering factor, and Gamma rely on these models (Section 4.1).
  • domain assumption The Fabian et al. (2008) and Ricci et al. (2017b) radiation-pressure forbidden region model applies to this sample.
    Used to identify 6/12 sources as transitional and to interpret their variability and outflow properties (Section 5.3).
  • domain assumption The Duras et al. (2020) bolometric correction is valid for this high-luminosity obscured sample.
    Lbol and hence lambda_Edd are computed from the 2-10 keV LX using this correction (Section 5.2).
  • domain assumption The R25 scaling relation log(L[NeV]/LX) = -3.36 and log(LX/L[OIII]) ~ 2.1 extend to z=2-9 JWST-selected AGNs.
    Used to argue [NeV] should be detectable in the JWST stack (Section 6.2). R25 is a companion paper with overlapping authors.
  • domain assumption Normalizing to [OIII] lambda 5007 peak flux yields comparable NLR ionization conditions between local and high-z samples.
    Load-bearing for the [NeV] discrepancy interpretation (Section 7.2).
  • domain assumption The phabs and apec model components correctly account for Galactic absorption and host-galaxy thermal emission.
    Baseline model components assumed to describe soft X-ray emission (Section 4).

how reviews work

0 comments
Cite this review

Pith. "Pith review of BASS. XLIX. Characterization of highly luminous and obscured AGNs: local X-ray and [NeV]$\lambda$3426 emission in comparison with the high-redshift Universe." pith.science (2026). https://pith.science/paper/FDVQPTGA

@misc{pith2026250710674,
  author       = {Pith},
  title        = {Pith review of: BASS. XLIX. Characterization of highly luminous and obscured AGNs: local X-ray and [NeV]$\lambda$3426 emission in comparison with the high-redshift Universe},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FDVQPTGA}},
  note         = {Machine review of arXiv:2507.10674}
}
abstract

We present a detailed analysis of the most luminous and obscured Active Galactic Nuclei (AGNs) detected in the ultra-hard X-ray band (14-195 keV) by Swift/BAT. Our sample comprises 21 X-ray luminous (log $L_X/{\rm erg\,s^{-1}}>44.6$, 2-10 keV) AGNs at $z<0.6$, optically classified as Seyfert 1.9-2. Using NuSTAR, XMM-Newton, Suzaku, and Chandra, we constrain AGN properties such as absorption column density $N_H$, photon index $\Gamma$, intrinsic $L_X$, covering factor, and iron K$\alpha$ equivalent width. For sources with black hole mass estimates (12/20), we find a weak correlation between $\Gamma$ and Eddington ratio ($\lambda_{Edd}$). Of these, six ($50\pm13\%$) lie in the $N_H$-$\lambda_{Edd}$ "forbidden region'' and exhibit a combined higher prevalence of $N_H$ variability and outflow signatures, suggesting a transitional phase where AGN feedback may be clearing the obscuring material. For the 13/21 sources with multi-epoch X-ray spectra, $82^{+6}_{-16}\%$ exhibit variability in either 2-10 keV flux ($73^{+9}_{-16}\%$) or line-of-sight $N_H$ ($33^{+15}_{-10}\%$). For the 20/21 sources with available near-UV/optical spectroscopy, we detect [NeV]$\lambda$3426 in 17 ($85^{+5}_{-11}\%$), confirming its reliability to probe AGN emission even in heavily obscured systems. When normalized to the same [OIII]$\lambda$5007 peak flux as $z = 2$-$9$ narrow-line AGNs identified with JWST, our sample exhibits significantly stronger [NeV]$\lambda$3426 emission, suggesting that high-redshift obscured AGNs may be intrinsically weaker in [NeV]$\lambda$3426 or that [NeV]$\lambda$3426 is more challenging to detect in those environments. The sources presented here serve as a benchmark for high-redshift analogs, showing the potential of [NeV]$\lambda$3426 to reveal obscured AGNs and the need for future missions to expand X-ray studies into the high-redshift Universe.

Figures

Figures reproduced from arXiv: 2507.10674 by the authors.

Figure 1
Figure 1. The selected sample consists of 21 AGNs with log LX/erg s−1 > 44.6, indicated by the red line, classified as Seyfert 1.9 and 2 (Koss et al. 2022b). The filled histograms represent the selected sources, while the full histograms show the full sample of Seyfert 1.9 and 2 (orange and blue, respec￾tively) from BASS DR3 (Koss et al. in prep.). Additional details on the sample selection are provided in the text. high-SNR … view at source ↗
Figure 2
Figure 2. Eddington ratio (λEdd) versus photon index (Γ) from our sample (blue points), with the best-fit linear rela￾tion (y = 0.14x − 1.85) shown as a blue line. The shaded region represents the 1σ uncertainty. For comparison, the best-fit relations from Risaliti et al. 2009 (grey dotted line), Trakhtenbrot et al. 2017 (purple dashed line), and Bright￾man et al. 2016 (olive dotted-dashed line) are shown. Note that Trakhtenb… view at source ↗
Figure 3
Figure 3. Eddington ratio (λEdd) versus absorption (NH) for the 12 sources for which λEdd was computed, color-coded by X-ray luminosity (LX). 6/12 sources are inside the so￾called “forbidden” region (shaded grey), where AGNs are expected to be caught in a transitional phase where out￾flows/winds and/or changes in NH are expected. case), it is noteworthy that one of the outflow sources outside this area (BAT ID 199) lies not f… view at source ↗
Figures from the paper (16 more)
Figure 5
Figure 5. Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: Left: Fe Kα rest-frame equivalent width as a function of the derived NH. The shaded areas represent model predictions from e-torus (Ikeda et al. 2009) (olive) and MYTorus (Murphy & Yaqoob 2009b) (grey), showing the range of all possible inclination angles. The torus op…
Figure 7
Figure 7. Figure 7: Left panel: [Ne v]λ3426 flux versus absorption-corrected 2–10 keV flux for our sample. For comparison, we show the R25 median relation for the BASS DR2 sample (olive dashed line), along with its 1σ and 2σ uncertainties (olive shaded areas), and the Compton-thick AGN th…
Figure 8
Figure 8. Figure 8: Comparison between the stacked high-redshift JWST spectrum from M24 (red dashed lines) and our stacked spectrum of low-redshift, highly luminous BASS AGNs (blue solid lines). The shaded blue region shows the 1σ uncertainty derived via bootstrap resampling. The green li…
Figure 9
Figure 9. Figure 9 [PITH_FULL_IMAGE:figures/full_fig_p022_9.png]
Figure 10
Figure 10. Figure 10: Comparison of column density (log NH; left), photon-index (Γ; center), and intrinsic luminosity (log LX; right) as a function of redshift for simulated observations with AXIS (top panels) and NewAthena (bottom panels). Simulations are based on the best-fit model of 2M…
Figure 11
Figure 11. Figure 11: Comparison of NH values derived using different torus models for the sources in our sample. Each panel shows pairwise comparisons between models, with the 1:1 relation indicated by a dashed grey line and a shaded region representing ±0.2 dex deviation. Top panel: MYTo…
Figure 12
Figure 12. Figure 12: Comparison of LX, NH, and Γ (left, middle, and right panels, respectively) with Ricci et al. (2017a) for the 11 sources that are in both samples. The grey dashed lines represent the 1:1 relations. The shaded grey areas in the left and middle panels indicate the 0.5 de…
Figure 14
Figure 14. Figure 14: Color-color classification using WISE W1, W2, and W3 color cuts. Sources within the region outlined by the solid grey lines (Mateos et al. 2012) or above the dashed grey line (Stern et al. 2012) are classified as AGN. One source falls outside both criteria but lies ve…
Figure 15
Figure 15. Figure 15: Optical classification from emission line diag￾nostic diagrams (Baldwin et al. 1981; Kewley et al. 2001; Kauffmann et al. 2003; Kewley et al. 2006; Schawinski et al. 2007). Upper limits are represented by empty markers ori￾ented toward the limit. The uncertainties on …
Figure 16
Figure 16. Figure 16: Best-fit unfolded spectra of the sources analyzed in this work. The corresponding models are shown as dashed lines, and the BAT ID is indicated in the top-left corner of each panel. For clarity, NuSTAR spectra are shown in orange, while soft X-ray spectra (XMM-Newton,…
Figure 17
Figure 17. Figure 17: Same as [PITH_FULL_IMAGE:figures/full_fig_p032_17.png]
Figure 18
Figure 18. Figure 18: Same as [PITH_FULL_IMAGE:figures/full_fig_p033_18.png]
Figure 19
Figure 19. Figure 19: Observed 2–10 keV best-fit flux in erg/s/cm2 (left panels), the constant CAGN accounting for intrinsic variability (middle panels), and logarithm of line-of-sight NH in cm−2 (right panels) for sources with multi-epoch observations. BAT IDs are indicated in the top-rig…
Figure 20
Figure 20. Figure 20: Same as [PITH_FULL_IMAGE:figures/full_fig_p035_20.png]
Figure 21
Figure 21. Figure 21: Same as [PITH_FULL_IMAGE:figures/full_fig_p036_21.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. X-ray Absorption Variability in NGC 1142: Another Constraint on the Nature of the Torus/Broad-Line Region in Active Galactic Nuclei

    astro-ph.GA 2026-07 conditional novelty 4.5 of 10

    NGC 1142’s NH varies across nine epochs; detection probability scales with observation count, and simple cloud simulations favor many simultaneous eclipsing clouds.

Reference graph

Works this paper leans on

230 extracted references · 28 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, doi: 10.1093/mnras/stv1062

  2. [2]

    1974, IEEE Transactions on Automatic Control, 19, 716, doi: 10.1109/TAC.1974.1100705

    Akaike, H. 1974, IEEE Transactions on Automatic Control, 19, 716, doi: 10.1109/TAC.1974.1100705

  3. [3]

    2008, A&A, 479, 735, doi: 10.1051/0004-6361:20077791

    Akylas, A., & Georgantopoulos, I. 2008, A&A, 479, 735, doi: 10.1051/0004-6361:20077791

  4. [4]

    T., Bogd´ an,´A., Kov´ acs, O

    Ananna, T. T., Bogd´ an,´A., Kov´ acs, O. E., Natarajan, P., & Hickox, R. C. 2024, ApJL, 969, L18, doi: 10.3847/2041-8213/ad5669

  5. [5]

    T., Treister, E., Urry, C

    Ananna, T. T., Treister, E., Urry, C. M., et al. 2019, ApJ, 871, 240, doi: 10.3847/1538-4357/aafb77

  6. [6]

    T., Weigel, A

    Ananna, T. T., Weigel, A. K., Trakhtenbrot, B., et al. 2022a, ApJS, 261, 9, doi: 10.3847/1538-4365/ac5b64

  7. [7]

    T., Urry, C

    Ananna, T. T., Urry, C. M., Ricci, C., et al. 2022b, ApJL, 939, L13, doi: 10.3847/2041-8213/ac9979

  8. [8]

    1989, GeoCoA, 53, 197, doi: 10.1016/0016-7037(89)90286-X

    Anders, E., & Grevesse, N. 1989, GeoCoA, 53, 197, doi: 10.1016/0016-7037(89)90286-X

Show all 230 references
  1. [9]

    1993, ARA&A, 31, 473, doi: 10.1146/annurev.aa.31.090193.002353

    Antonucci, R. 1993, ARA&A, 31, 473, doi: 10.1146/annurev.aa.31.090193.002353

  2. [10]

    2018, A&A, 616, A170, doi: 10.1051/0004-6361/201732322

    Arcodia, R., Campana, S., Salvaterra, R., & Ghisellini, G. 2018, A&A, 616, A170, doi: 10.1051/0004-6361/201732322

  3. [11]

    Arnaud, K. A. 1996, in Astronomical Society of the Pacific Conference Series, Vol. 101, Astronomical Data Analysis Software and Systems V, ed. G. H. Jacoby & J. Barnes, 17 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-...

  4. [12]

    2010, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol

    Bacon, R., Accardo, M., Adjali, L., et al. 2010, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 7735, Ground-based and Airborne Instrumentation for Astronomy III, ed. I. S. McLean, S. K. Ramsay, & H. Takami, 773508, doi: 10.1117/12.856027

  5. [13]

    2019, MNRAS, 488, 4317, doi: 10.1093/mnras/stz1995

    Baek, J., Chung, A., Schawinski, K., et al. 2019, MNRAS, 488, 4317, doi: 10.1093/mnras/stz1995

  6. [14]

    Baggen, J. F. W., van Dokkum, P., Brammer, G., et al. 2024, ApJL, 977, L13, doi: 10.3847/2041-8213/ad90b8

  7. [15]

    Baldwin, J. A. 1977, ApJ, 214, 679, doi: 10.1086/155294

  8. [16]

    A., Phillips, M

    Baldwin, J. A., Phillips, M. M., & Terlevich, R. 1981, PASP, 93, 5, doi: 10.1086/130766 Balokovi´ c, M., Cabral, S. E., Brenneman, L., & Urry, C. M. 2021, ApJ, 916, 90, doi: 10.3847/1538-4357/abff4d Balokovi´ c, M., Brightman, M., Harrison, F. A., et al. 2018, ApJ, 854, 42, do...

  9. [17]

    2024, A&A, 685, A141, doi: 10.1051/0004-6361/202245288

    Barchiesi, L., Vignali, C., Pozzi, F., et al. 2024, A&A, 685, A141, doi: 10.1051/0004-6361/202245288

  10. [18]

    2020, Astronomische Nachrichten, 341, 224, doi: https://doi.org/10.1002/asna.202023782

    Barret, D., Decourchelle, A., Fabian, A., et al. 2020, Astronomische Nachrichten, 341, 224, doi: https://doi.org/10.1002/asna.202023782

  11. [19]

    H., Tueller, J., Markwardt, C

    Baumgartner, W. H., Tueller, J., Markwardt, C. B., et al. 2013, ApJS, 207, 19, doi: 10.1088/0067-0049/207/2/19

  12. [20]

    2015, MNRAS, 454, 3622, doi: 10.1093/mnras/stv2181

    Berney, S., Koss, M., Trakhtenbrot, B., et al. 2015, MNRAS, 454, 3622, doi: 10.1093/mnras/stv2181

  13. [21]

    2007, A&A, 467, L19, doi: 10.1051/0004-6361:20077331

    Bianchi, S., Guainazzi, M., Matt, G., & Fonseca Bonilla, N. 2007, A&A, 467, L19, doi: 10.1051/0004-6361:20077331

  14. [22]

    J., Bazzano, A., Malizia, A., et al

    Bird, A. J., Bazzano, A., Malizia, A., et al. 2016, ApJS, 223, 15, doi: 10.3847/0067-0049/223/1/15

  15. [23]

    G., Gandhi, P., Balokovi´ c, M., et al

    Boorman, P. G., Gandhi, P., Balokovi´ c, M., et al. 2018, MNRAS, 477, 3775, doi: 10.1093/mnras/sty861

  16. [24]

    G., Torres-Alb` a, N., Annuar, A., et al

    Boorman, P. G., Torres-Alb` a, N., Annuar, A., et al. 2024, Frontiers in Astronomy and Space Sciences, 11, 1335459, doi: 10.3389/fspas.2024.1335459

  17. [25]

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

    Boorman, P. G., Gandhi, P., Buchner, J., et al. 2025, ApJ, 978, 118, doi: 10.3847/1538-4357/ad8236

  18. [27]

    D., Mainieri, V., et al

    Brightman, M., Silverman, J. D., Mainieri, V., et al. 2013, MNRAS, 433, 2485, doi: 10.1093/mnras/stt920

  19. [28]

    2015, ApJ, 805, 41, doi: 10.1088/0004-637X/805/1/41

    Brightman, M., Balokovi´ c, M., Stern, D., et al. 2015, ApJ, 805, 41, doi: 10.1088/0004-637X/805/1/41

  20. [29]

    R., et al

    Brightman, M., Masini, A., Ballantyne, D. R., et al. 2016, ApJ, 826, 93, doi: 10.3847/0004-637X/826/1/93

  21. [30]

    Buchner, J., & Bauer, F. E. 2017, MNRAS, 465, 4348, doi: 10.1093/mnras/stw2955

  22. [31]

    Bauer, F. E. 2019, A&A, 629, A16, doi: 10.1051/0004-6361/201834771

  23. [32]

    2014, A&A, 564, A125, doi: 10.1051/0004-6361/201322971 —

    Buchner, J., Georgakakis, A., Nandra, K., et al. 2014, A&A, 564, A125, doi: 10.1051/0004-6361/201322971 —. 2015, ApJ, 802, 89, doi: 10.1088/0004-637X/802/2/89

  24. [33]

    2011, ApJ, 728, 58, doi: 10.1088/0004-637X/728/1/58

    Burlon, D., Ajello, M., Greiner, J., et al. 2011, ApJ, 728, 58, doi: 10.1088/0004-637X/728/1/58

  25. [34]

    2002, Model selection and multimodel inference: a practical information-theoretic approach (Springer Verlag)

    Burnham, K., & Anderson, D. 2002, Model selection and multimodel inference: a practical information-theoretic approach (Springer Verlag)

  26. [35]

    2020, A&A, 634, A114, doi: 10.1051/0004-6361/201936321

    Caglar, T., Burtscher, L., Brandl, B., et al. 2020, A&A, 634, A114, doi: 10.1051/0004-6361/201936321

  27. [36]

    J., Burtscher, L., et al

    Caglar, T., Koss, M. J., Burtscher, L., et al. 2023, ApJ, 956, 60, doi: 10.3847/1538-4357/acf11b 38 Peca et al

  28. [37]

    C., et al

    Calzetti, D., Armus, L., Bohlin, R. C., et al. 2000, ApJ, 533, 682, doi: 10.1086/308692

  29. [38]

    2011, PASA, 28, 128, doi: 10.1071/AS10046

    Cameron, E. 2011, PASA, 28, 128, doi: 10.1071/AS10046

  30. [39]

    2004, PASP, 116, 138, doi: 10.1086/381875

    Cappellari, M., & Emsellem, E. 2004, PASP, 116, 138, doi: 10.1086/381875

  31. [40]

    Carnall, A. C. 2017, arXiv e-prints, arXiv:1705.05165, doi: 10.48550/arXiv.1705.05165

  32. [41]

    1979, ApJ, 228, 939, doi: 10.1086/156922

    Cash, W. 1979, ApJ, 228, 939, doi: 10.1086/156922

  33. [42]

    L., Gilli, R., Mignoli, M., & Mazzolari, G

    Cavicchi, V. L., Gilli, R., Mignoli, M., & Mazzolari, G. 2025, Research Notes of the AAS, 9, 164, doi: 10.3847/2515-5172/adeb68

  34. [43]

    A., Endsley, R., et al

    Chisholm, J., Berg, D. A., Endsley, R., et al. 2024, MNRAS, 534, 2633, doi: 10.1093/mnras/stae2199

  35. [44]

    2019, A&A, 623, A172, doi: 10.1051/0004-6361/201834426

    Circosta, C., Vignali, C., Gilli, R., et al. 2019, A&A, 623, A172, doi: 10.1051/0004-6361/201834426

  36. [45]

    J., Yang, G., Papovich, C., et al

    Cleri, N. J., Yang, G., Papovich, C., et al. 2023a, ApJ, 948, 112, doi: 10.3847/1538-4357/acc1e6

  37. [46]

    J., Olivier, G

    Cleri, N. J., Olivier, G. M., Hutchison, T. A., et al. 2023b, ApJ, 953, 10, doi: 10.3847/1538-4357/acde55

  38. [47]

    2025, Nature Astronomy, 9, 36, doi: 10.1038/s41550-024-02416-3

    Cruise, M., Guainazzi, M., Aird, J., et al. 2025, Nature Astronomy, 9, 36, doi: 10.1038/s41550-024-02416-3

  39. [48]

    2020, MNRAS, 491, 944, doi: 10.1093/mnras/stz2910

    Curti, M., Mannucci, F., Cresci, G., & Maiolino, R. 2020, MNRAS, 491, 944, doi: 10.1093/mnras/stz2910

  40. [49]

    2024, A&A, 684, A75, doi: 10.1051/0004-6361/202346698 D’Amato, Q., Gilli, R., Vignali, C., et al

    Curti, M., Maiolino, R., Curtis-Lake, E., et al. 2024, A&A, 684, A75, doi: 10.1051/0004-6361/202346698 D’Amato, Q., Gilli, R., Vignali, C., et al. 2020, A&A, 636, A37, doi: 10.1051/0004-6361/201936175 de Graaff, A., Rix, H.-W., Naidu, R. P., et al. 2025, arXiv e-prints, arXiv:...

  41. [50]

    2020, A&A, 636, A73, doi: 10.1051/0004-6361/201936817

    Duras, F., Bongiorno, A., Ricci, F., et al. 2020, A&A, 636, A73, doi: 10.1051/0004-6361/201936817

  42. [51]

    J., Willott, C., Alberts, S., et al

    Eisenstein, D. J., Willott, C., Alberts, S., et al. 2023, arXiv e-prints, arXiv:2306.02465, doi: 10.48550/arXiv.2306.02465

  43. [52]

    2012, ApJL, 747, L33, doi: 10.1088/2041-8205/747/2/L33

    Elitzur, M. 2012, ApJL, 747, L33, doi: 10.1088/2041-8205/747/2/L33

  44. [53]

    C., Alston, W

    Fabian, A. C., Alston, W. N., Cackett, E. M., et al. 2017, Astronomische Nachrichten, 338, 269, doi: 10.1002/asna.201713341

  45. [54]

    C., Vasudevan, R

    Fabian, A. C., Vasudevan, R. V., & Gandhi, P. 2008, MNRAS, 385, L43, doi: 10.1111/j.1745-3933.2008.00430.x

  46. [55]

    M., & Reynolds, C

    Winter, L. M., & Reynolds, C. S. 2009, MNRAS, 394, L89, doi: 10.1111/j.1745-3933.2009.00617.x

  47. [56]

    L., Bagley, M

    Finkelstein, S. L., Bagley, M. B., Arrabal Haro, P., et al. 2022, ApJL, 940, L55, doi: 10.3847/2041-8213/ac966e

  48. [57]

    W., Lang, D., & Goodman, J

    Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306, doi: 10.1086/670067

  49. [58]

    C., Allen, G

    Fruscione, A., McDowell, J. C., Allen, G. E., et al. 2006, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 6270, Observatory Operations: Strategies, Processes, and Systems, ed. D. R. Silva & R. E. Doxsey, 62701V, doi: 10.1117/12.671760

  50. [59]

    2011, ApJ, 727, 19, doi: 10.1088/0004-637X/727/1/19

    Fukazawa, Y., Hiragi, K., Mizuno, M., et al. 2011, ApJ, 727, 19, doi: 10.1088/0004-637X/727/1/19

  51. [60]

    J., et al

    Gabriel, C., Denby, M., Fyfe, D. J., et al. 2004, in Astronomical Society of the Pacific Conference Series, Vol. 314, Astronomical Data Analysis Software and Systems (ADASS) XIII, ed. F. Ochsenbein, M. G. Allen, & D. Egret, 759

  52. [61]

    B., Stern, H

    Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. 2004, Bayesian Data Analysis, 2nd edn. (Chapman and Hall/CRC)

  53. [62]

    2010, A&A, 509, A38, doi: 10.1051/0004-6361/200912943

    Georgantopoulos, I., & Akylas, A. 2010, A&A, 509, A38, doi: 10.1051/0004-6361/200912943

  54. [63]

    1994, MNRAS, 267, 743, doi: 10.1093/mnras/267.3.743

    Ghisellini, G., Haardt, F., & Matt, G. 1994, MNRAS, 267, 743, doi: 10.1093/mnras/267.3.743

  55. [64]

    A., Maughan, B

    Giles, P. A., Maughan, B. J., Dahle, H., et al. 2017, MNRAS, 465, 858, doi: 10.1093/mnras/stw2621

  56. [65]

    2007, A&A, 463, 79, doi: 10.1051/0004-6361:20066334

    Gilli, R., Comastri, A., & Hasinger, G. 2007, A&A, 463, 79, doi: 10.1051/0004-6361:20066334

  57. [66]

    2010, A&A, 519, A92, doi: 10.1051/0004-6361/201014039

    Gilli, R., Vignali, C., Mignoli, M., et al. 2010, A&A, 519, A92, doi: 10.1051/0004-6361/201014039

  58. [67]

    2022, A&A, 666, A17, doi: 10.1051/0004-6361/202243708

    Gilli, R., Norman, C., Calura, F., et al. 2022, A&A, 666, A17, doi: 10.1051/0004-6361/202243708

  59. [68]

    2021, PyXspec: Python interface to XSPEC spectral-fitting program

    Gordon, C., & Arnaud, K. 2021, PyXspec: Python interface to XSPEC spectral-fitting program. http://ascl.net/2101.014

  60. [69]

    E., Labbe, I., Goulding, A

    Greene, J. E., Labbe, I., Goulding, A. D., et al. 2024, ApJ, 964, 39, doi: 10.3847/1538-4357/ad1e5f

  61. [70]

    Guainazzi, M., Matt, G., & Perola, G. C. 2005, A&A, 444, 119, doi: 10.1051/0004-6361:20053643

  62. [71]

    K., Ricci, C., Tortosa, A., et al

    Gupta, K. K., Ricci, C., Tortosa, A., et al. 2021, MNRAS, 504, 428, doi: 10.1093/mnras/stab839

  63. [72]

    K., Ricci, C., Temple, M

    Gupta, K. K., Ricci, C., Temple, M. J., et al. 2024, A&A, 691, A203, doi: 10.1051/0004-6361/202450567

  64. [73]

    2023, ApJ, 959, 39, doi: 10.3847/1538-4357/ad029e

    Harikane, Y., Zhang, Y., Nakajima, K., et al. 2023, ApJ, 959, 39, doi: 10.3847/1538-4357/ad029e

  65. [74]

    A., Craig, W

    Harrison, F. A., Craig, W. W., Christensen, F. E., et al. 2013, ApJ, 770, 103, doi: 10.1088/0004-637X/770/2/103

  66. [75]

    2008, A&A, 490, 905, doi: 10.1051/0004-6361:200809839

    Hasinger, G. 2008, A&A, 490, 905, doi: 10.1051/0004-6361:200809839

  67. [77]

    F., Hernquist, L., Cox, T

    Hopkins, P. F., Hernquist, L., Cox, T. J., et al. 2006, ApJS, 163, 1, doi: 10.1086/499298

  68. [78]

    F., Hernquist, L., Cox, T

    Hopkins, P. F., Hernquist, L., Cox, T. J., & Kereˇ s, D. 2008, ApJS, 175, 356, doi: 10.1086/524362

  69. [79]

    2020, ApJ, 895, 114, doi: 10.3847/1538-4357/ab9019

    Huang, J., Luo, B., Du, P., et al. 2020, ApJ, 895, 114, doi: 10.3847/1538-4357/ab9019

  70. [80]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90, doi: 10.1109/MCSE.2007.55

  71. [81]

    2017, ApJ, 835, 74, doi: 10.3847/1538-4357/835/1/74

    Ichikawa, K., Ricci, C., Ueda, Y., et al. 2017, ApJ, 835, 74, doi: 10.3847/1538-4357/835/1/74

  72. [82]

    2015, ApJ, 803, 57, doi: 10.1088/0004-637X/803/2/57

    Ichikawa, K., Packham, C., Ramos Almeida, C., et al. 2015, ApJ, 803, 57, doi: 10.1088/0004-637X/803/2/57

  73. [83]

    2019, ApJ, 870, 31, doi: 10.3847/1538-4357/aaef8f

    Ichikawa, K., Ricci, C., Ueda, Y., et al. 2019, ApJ, 870, 31, doi: 10.3847/1538-4357/aaef8f

  74. [84]

    2009, ApJ, 692, 608, doi: 10.1088/0004-637X/692/1/608

    Ikeda, S., Awaki, H., & Terashima, Y. 2009, ApJ, 692, 608, doi: 10.1088/0004-637X/692/1/608

  75. [85]

    2007, PASJ, 59, S113, doi: 10.1093/pasj/59.sp1.S113

    Ishisaki, Y., Maeda, Y., Fujimoto, R., et al. 2007, PASJ, 59, S113, doi: 10.1093/pasj/59.sp1.S113

  76. [86]

    1993, ApJL, 413, L15, doi: 10.1086/186948

    Iwasawa, K., & Taniguchi, Y. 1993, ApJL, 413, L15, doi: 10.1086/186948

  77. [87]

    2001, A&A, 365, L1, doi: 10.1051/0004-6361:20000036 Juodˇ zbalis, I., Ji, X., Maiolino, R., et al

    Jansen, F., Lumb, D., Altieri, B., et al. 2001, A&A, 365, L1, doi: 10.1051/0004-6361:20000036 Juodˇ zbalis, I., Ji, X., Maiolino, R., et al. 2024, MNRAS, 535, 853, doi: 10.1093/mnras/stae2367

  78. [88]

    2016, A&A, 592, A148, doi: 10.1051/0004-6361/201527968

    Kakkad, D., Mainieri, V., Padovani, P., et al. 2016, A&A, 592, A148, doi: 10.1051/0004-6361/201527968

  79. [89]

    Kalberla, P. M. W., Burton, W. B., Hartmann, D., et al. 2005, A&A, 440, 775, doi: 10.1051/0004-6361:20041864 Kallov´ a, K., Boorman, P. G., & Ricci, C. 2024, ApJ, 966, 116, doi: 10.3847/1538-4357/ad3235

  80. [90]

    S., Miller, J

    Kammoun, E. S., Miller, J. M., Koss, M., et al. 2020, ApJ, 901, 161, doi: 10.3847/1538-4357/abb29f

  81. [91]

    A., et al

    Kamraj, N., Brightman, M., Harrison, F. A., et al. 2022, ApJ, 927, 42, doi: 10.3847/1538-4357/ac45f6

  82. [92]

    M., Tremonti, C., et al

    Kauffmann, G., Heckman, T. M., Tremonti, C., et al. 2003, MNRAS, 346, 1055, doi: 10.1111/j.1365-2966.2003.07154.x

  83. [93]

    F., et al

    Kawamuro, T., Ricci, C., Mushotzky, R. F., et al. 2023, ApJS, 269, 24, doi: 10.3847/1538-4365/acf467

  84. [94]

    Kelly, B. C. 2007, The Astrophysical Journal, 665, 1489, doi: 10.1086/519947

  85. [95]

    J., & Dopita, M

    Kewley, L. J., & Dopita, M. A. 2002, ApJS, 142, 35, doi: 10.1086/341326

  86. [96]

    J., Dopita, M

    Kewley, L. J., Dopita, M. A., Sutherland, R. S., Heisler, C. A., & Trevena, J. 2001, ApJ, 556, 121, doi: 10.1086/321545

  87. [97]

    J., Groves, B., Kauffmann, G., & Heckman, T

    Kewley, L. J., Groves, B., Kauffmann, G., & Heckman, T. 2006, MNRAS, 372, 961, doi: 10.1111/j.1365-2966.2006.10859.x

  88. [98]

    D., Onoue, M., Inayoshi, K., et al

    Kocevski, D. D., Onoue, M., Inayoshi, K., et al. 2023, ApJL, 954, L4, doi: 10.3847/2041-8213/ace5a0

  89. [99]

    D., Finkelstein, S

    Kocevski, D. D., Finkelstein, S. L., Barro, G., et al. 2024, arXiv e-prints, arXiv:2404.03576, doi: 10.48550/arXiv.2404.03576

  90. [100]

    2024, arXiv e-prints, arXiv:2407.04777, doi: 10.48550/arXiv.2407.04777

    Kokubo, M., & Harikane, Y. 2024, arXiv e-prints, arXiv:2407.04777, doi: 10.48550/arXiv.2407.04777

  91. [101]

    2003, ApJL, 582, L15, doi: 10.1086/346145

    Komossa, S., Burwitz, V., Hasinger, G., et al. 2003, ApJL, 582, L15, doi: 10.1086/346145

  92. [102]

    2017, ApJ, 850, 74, doi: 10.3847/1538-4357/aa8ec9

    Koss, M., Trakhtenbrot, B., Ricci, C., et al. 2017, ApJ, 850, 74, doi: 10.3847/1538-4357/aa8ec9

  93. [103]

    J., Strittmatter, B., Lamperti, I., et al

    Koss, M. J., Strittmatter, B., Lamperti, I., et al. 2021, ApJS, 252, 29, doi: 10.3847/1538-4365/abcbfe

  94. [104]

    J., Trakhtenbrot, B., Ricci, C., et al

    Koss, M. J., Trakhtenbrot, B., Ricci, C., et al. 2022a, ApJS, 261, 1, doi: 10.3847/1538-4365/ac6c8f

  95. [105]

    J., Ricci, C., Trakhtenbrot, B., et al

    Koss, M. J., Ricci, C., Trakhtenbrot, B., et al. 2022b, ApJS, 261, 2, doi: 10.3847/1538-4365/ac6c05

  96. [106]

    J., Trakhtenbrot, B., Ricci, C., et al

    Koss, M. J., Trakhtenbrot, B., Ricci, C., et al. 2022c, ApJS, 261, 6, doi: 10.3847/1538-4365/ac650b

  97. [107]

    2007, PASJ, 59, 23, doi: 10.1093/pasj/59.sp1.S23

    Koyama, K., Tsunemi, H., Dotani, T., et al. 2007, PASJ, 59, 23, doi: 10.1093/pasj/59.sp1.S23

  98. [108]

    A., Sazonov, S

    Krivonos, R. A., Sazonov, S. Y., Kuznetsova, E. A., et al. 2022, MNRAS, 510, 4796, doi: 10.1093/mnras/stab3751

  99. [109]

    E., Matthee, J., et al

    Labbe, I., Greene, J. E., Matthee, J., et al. 2024, arXiv e-prints, arXiv:2412.04557, doi: 10.48550/arXiv.2412.04557

  100. [110]

    E., Bezanson, R., et al

    Labbe, I., Greene, J. E., Bezanson, R., et al. 2025, ApJ, 978, 92, doi: 10.3847/1538-4357/ad3551

  101. [111]

    C., et al

    Laha, S., Ricci, C., Mather, J. C., et al. 2025, Frontiers in Astronomy and Space Sciences, 11, 1530392, doi: 10.3389/fspas.2024.1530392

  102. [112]

    M., et al

    LaMassa, S., Peca, A., Urry, C. M., et al. 2024, ApJ, 974, 235, doi: 10.3847/1538-4357/ad6e7d

  103. [113]

    M., Georgakakis, A., Vivek, M., et al

    LaMassa, S. M., Georgakakis, A., Vivek, M., et al. 2019a, ApJ, 876, 50, doi: 10.3847/1538-4357/ab108b

  104. [114]

    M., Yaqoob, T., Boorman, P

    LaMassa, S. M., Yaqoob, T., Boorman, P. G., et al. 2019b, ApJ, 887, 173, doi: 10.3847/1538-4357/ab552c

  105. [115]

    2024, arXiv e-prints, arXiv:2409.13047, doi: 10.48550/arXiv.2409.13047

    Lambrides, E., Garofali, K., Larson, R., et al. 2024, arXiv e-prints, arXiv:2409.13047, doi: 10.48550/arXiv.2409.13047

  106. [116]

    2017, MNRAS, 467, 540, doi: 10.1093/mnras/stx055

    Lamperti, I., Koss, M., Trakhtenbrot, B., et al. 2017, MNRAS, 467, 540, doi: 10.1093/mnras/stx055

  107. [117]

    2015a, A&A, 573, A137, doi: 10.1051/0004-6361/201424924

    Lanzuisi, G., Ranalli, P., Georgantopoulos, I., et al. 2015a, A&A, 573, A137, doi: 10.1051/0004-6361/201424924

  108. [118]

    2015b, A&A, 578, A120, doi: 10.1051/0004-6361/201526036

    Lanzuisi, G., Perna, M., Delvecchio, I., et al. 2015b, A&A, 578, A120, doi: 10.1051/0004-6361/201526036

  109. [119]

    2018, MNRAS, 480, 2578, doi: 10.1093/mnras/sty2025

    Lanzuisi, G., Civano, F., Marchesi, S., et al. 2018, MNRAS, 480, 2578, doi: 10.1093/mnras/sty2025

  110. [120]

    N., et al

    Liu, H., Luo, B., Brandt, W. N., et al. 2021, ApJ, 910, 103, doi: 10.3847/1538-4357/abe37f 40 Peca et al

  111. [121]

    2017, ApJS, 232, 8, doi: 10.3847/1538-4365/aa7847

    Liu, T., Tozzi, P., Wang, J.-X., et al. 2017, ApJS, 232, 8, doi: 10.3847/1538-4365/aa7847

  112. [122]

    2016, MNRAS, 459, 1602, doi: 10.1093/mnras/stw753

    Liu, Z., Merloni, A., Georgakakis, A., et al. 2016, MNRAS, 459, 1602, doi: 10.1093/mnras/stw753

  113. [123]

    H., et al

    Lyu, J., Alberts, S., Rieke, G. H., et al. 2024, ApJ, 966, 229, doi: 10.3847/1538-4357/ad3643

  114. [124]

    2024, ApJL, 976, L24, doi: 10.3847/2041-8213/ad90e1

    Madau, P., & Haardt, F. 2024, ApJL, 976, L24, doi: 10.3847/2041-8213/ad90e1

  115. [125]

    K., Beardmore, A

    Madsen, K. K., Beardmore, A. P., Forster, K., et al. 2017, AJ, 153, 2, doi: 10.3847/1538-3881/153/1/2

  116. [126]

    2011, ApJ, 731, 53, doi: 10.1088/0004-637X/731/1/53

    Mainzer, A., Bauer, J., Grav, T., et al. 2011, ApJ, 731, 53, doi: 10.1088/0004-637X/731/1/53

  117. [127]

    2008, A&A, 488, 463, doi: 10.1051/0004-6361:200809678

    Maiolino, R., Nagao, T., Grazian, A., et al. 2008, A&A, 488, 463, doi: 10.1051/0004-6361:200809678

  118. [128]

    2024, A&A, 691, A145, doi: 10.1051/0004-6361/202347640

    Maiolino, R., Scholtz, J., Curtis-Lake, E., et al. 2024, A&A, 691, A145, doi: 10.1051/0004-6361/202347640

  119. [129]

    2025, MNRAS, 538, 1921, doi: 10.1093/mnras/staf359

    Maiolino, R., Risaliti, G., Signorini, M., et al. 2025, MNRAS, 538, 1921, doi: 10.1093/mnras/staf359

  120. [130]

    A., Jensen, L

    Malkan, M. A., Jensen, L. D., Rodriguez, D. R., Spinoglio, L., & Rush, B. 2017, ApJ, 846, 102, doi: 10.3847/1538-4357/aa8302

  121. [131]

    2018, ApJ, 854, 49, doi: 10.3847/1538-4357/aaa410

    Marchesi, S., Ajello, M., Marcotulli, L., et al. 2018, ApJ, 854, 49, doi: 10.3847/1538-4357/aaa410

  122. [132]

    2019, ApJ, 882, 162, doi: 10.3847/1538-4357/ab340a

    Marchesi, S., Ajello, M., Zhao, X., et al. 2019, ApJ, 882, 162, doi: 10.3847/1538-4357/ab340a

  123. [133]

    2016, ApJ, 830, 100, doi: 10.3847/0004-637X/830/2/100

    Marchesi, S., Lanzuisi, G., Civano, F., et al. 2016, ApJ, 830, 100, doi: 10.3847/0004-637X/830/2/100

  124. [134]

    2020, A&A, 642, A184, doi: 10.1051/0004-6361/202038622

    Marchesi, S., Gilli, R., Lanzuisi, G., et al. 2020, A&A, 642, A184, doi: 10.1051/0004-6361/202038622

  125. [135]

    2022, ApJ, 935, 114, doi: 10.3847/1538-4357/ac80be

    Marchesi, S., Zhao, X., Torres-Alb` a, N., et al. 2022, ApJ, 935, 114, doi: 10.3847/1538-4357/ac80be

  126. [136]

    M., et al

    Marcotulli, L., Ajello, M., Urry, C. M., et al. 2022, ApJ, 940, 77, doi: 10.3847/1538-4357/ac937f

  127. [138]

    J., Barcons, X., et al

    Mateos, S., Carrera, F. J., Barcons, X., et al. 2017, ApJL, 841, L18, doi: 10.3847/2041-8213/aa7268

  128. [139]

    2019, MNRAS, 482, 151, doi: 10.1093/mnras/sty2697

    Matt, G., & Iwasawa, K. 2019, MNRAS, 482, 151, doi: 10.1093/mnras/sty2697

  129. [140]

    P., Brammer, G., et al

    Matthee, J., Naidu, R. P., Brammer, G., et al. 2024, ApJ, 963, 129, doi: 10.3847/1538-4357/ad2345

  130. [141]

    2024, arXiv e-prints, arXiv:2408.15615, doi: 10.48550/arXiv.2408.15615

    Mazzolari, G., Scholtz, J., Maiolino, R., et al. 2024, arXiv e-prints, arXiv:2408.15615, doi: 10.48550/arXiv.2408.15615

  131. [142]

    D., Satyapal, S., Laor, A., et al

    McKaig, J. D., Satyapal, S., Laor, A., et al. 2024, ApJ, 976, 130, doi: 10.3847/1538-4357/ad7a7910.1134/ S1063772908070020

  132. [143]

    2017, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol

    Meidinger, N., Barbera, M., Emberger, V., et al. 2017, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 10397, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 103970V, doi: 10.1117/12.2271844

  133. [144]

    2024, MNRAS, 532, 3036, doi: 10.1093/mnras/stae1617

    Panessa, F. 2024, MNRAS, 532, 3036, doi: 10.1093/mnras/stae1617

  134. [145]

    2013, A&A, 556, A29, doi: 10.1051/0004-6361/201220846

    Mignoli, M., Vignali, C., Gilli, R., et al. 2013, A&A, 556, A29, doi: 10.1051/0004-6361/201220846

  135. [146]

    2019, A&A, 626, A9, doi: 10.1051/0004-6361/201935062

    Mignoli, M., Feltre, A., Bongiorno, A., et al. 2019, A&A, 626, A9, doi: 10.1051/0004-6361/201935062

  136. [147]

    2007, PASJ, 59, S1, doi: 10.1093/pasj/59.sp1.S1

    Mitsuda, K., Bautz, M., Inoue, H., et al. 2007, PASJ, 59, S1, doi: 10.1093/pasj/59.sp1.S1

  137. [148]

    C., et al

    Morrissey, P., Matuszewski, M., Martin, D. C., et al. 2018, ApJ, 864, 93, doi: 10.3847/1538-4357/aad597

  138. [149]

    D., & Yaqoob, T

    Murphy, K. D., & Yaqoob, T. 2009a, MNRAS, 397, 1549, doi: 10.1111/j.1365-2966.2009.15025.x —. 2009b, MNRAS, 397, 1549, doi: 10.1111/j.1365-2966.2009.15025.x

  139. [150]

    2023, A&A, 679, A84, doi: 10.1051/0004-6361/202245555

    Musiimenta, B., Brusa, M., Liu, T., et al. 2023, A&A, 679, A84, doi: 10.1051/0004-6361/202245555

  140. [151]

    2013, arXiv e-prints, arXiv:1306.2307, doi: 10.48550/arXiv.1306.2307

    Nandra, K., Barret, D., Barcons, X., et al. 2013, arXiv e-prints, arXiv:1306.2307, doi: 10.48550/arXiv.1306.2307

  141. [152]

    2018, A&A, 614, A121, doi: 10.1051/0004-6361/201832694

    Nanni, R., Gilli, R., Vignali, C., et al. 2018, A&A, 614, A121, doi: 10.1051/0004-6361/201832694

  142. [153]

    M., S´ anchez, F

    Negus, J., Comerford, J. M., S´ anchez, F. M., et al. 2023, ApJ, 945, 127, doi: 10.3847/1538-4357/acb772

  143. [154]

    N., Chen, C.-T., et al

    Ni, Q., Brandt, W. N., Chen, C.-T., et al. 2021, ApJS, 256, 21, doi: 10.3847/1538-4365/ac0dc6

  144. [155]

    Oh, K., Sarzi, M., Schawinski, K., & Yi, S. K. 2011, ApJS, 195, 13, doi: 10.1088/0067-0049/195/2/13

  145. [156]

    B., et al

    Oh, K., Koss, M., Markwardt, C. B., et al. 2018, ApJS, 235, 4, doi: 10.3847/1538-4365/aaa7fd

  146. [157]

    J., Ueda, Y., et al

    Oh, K., Koss, M. J., Ueda, Y., et al. 2022, ApJS, 261, 4, doi: 10.3847/1538-4365/ac5b68

  147. [158]

    B., & Gunn, J

    Oke, J. B., & Gunn, J. E. 1982, PASP, 94, 586, doi: 10.1086/131027

  148. [159]

    2024, ApJ, 976, 96, doi: 10.3847/1538-4357/ad84f7

    Pacucci, F., & Narayan, R. 2024, ApJ, 976, 96, doi: 10.3847/1538-4357/ad84f7

  149. [160]

    M., Assef, R

    Padovani, P., Alexander, D. M., Assef, R. J., et al. 2017, A&A Rv, 25, 2, doi: 10.1007/s00159-017-0102-9

  150. [161]

    2017, A&A, 607, A31, doi: 10.1051/0004-6361/201629623 pandas development team, T

    Paltani, S., & Ricci, C. 2017, A&A, 607, A31, doi: 10.1051/0004-6361/201629623 pandas development team, T. 2020, pandas-dev/pandas: Pandas, latest, Zenodo, doi: 10.5281/zenodo.3509134

  151. [162]

    2002, A&A, 394, 435, doi: 10.1051/0004-6361:20021161 BASS

    Panessa, F., & Bassani, L. 2002, A&A, 394, 435, doi: 10.1051/0004-6361:20021161 BASS. XLIX. Characterization of highly luminous and obscured AGNs 41

  152. [163]

    2024a, Universe, 10, 245, doi: 10.3390/universe10060245

    Foord, A. 2024a, Universe, 10, 245, doi: 10.3390/universe10060245

  153. [164]

    J., Serafinelli, R., et al

    Peca, A., Koss, M. J., Serafinelli, R., et al. 2025, arXiv e-prints, arXiv:2505.07963. https://arxiv.org/abs/2505.07963

  154. [165]

    2021, ApJ, 906, 90, doi: 10.3847/1538-4357/abc9c7

    Peca, A., Vignali, C., Gilli, R., et al. 2021, ApJ, 906, 90, doi: 10.3847/1538-4357/abc9c7

  155. [166]

    M., et al

    Peca, A., Cappelluti, N., Urry, C. M., et al. 2023, ApJ, 943, 162, doi: 10.3847/1538-4357/acac28

  156. [167]

    2024b, ApJ, 974, 156, doi: 10.3847/1538-4357/ad6df4

    Peca, A., Cappelluti, N., LaMassa, S., et al. 2024b, ApJ, 974, 156, doi: 10.3847/1538-4357/ad6df4

  157. [168]

    2023, in American Astronomical Society Meeting Abstracts, Vol

    Pfeifle, R., Ricci, C., Boorman, P., et al. 2023, in American Astronomical Society Meeting Abstracts, Vol. 241, American Astronomical Society Meeting Abstracts, 254.04

  158. [169]

    2025, The Astrophysical Journal, 979, 170, doi: 10.3847/1538-4357/ad9c64

    Pizzetti, A., Torres-Alb` a, N., Marchesi, S., et al. 2025, The Astrophysical Journal, 979, 170, doi: 10.3847/1538-4357/ad9c64

  159. [170]

    2024, A&A, 685, A97, doi: 10.1051/0004-6361/202348479

    Pouliasis, E., Ruiz, A., Georgantopoulos, I., et al. 2024, A&A, 685, A97, doi: 10.1051/0004-6361/202348479

  160. [171]

    E., et al

    Puccetti, S., Comastri, A., Bauer, F. E., et al. 2016, A&A, 585, A157, doi: 10.1051/0004-6361/201527189

  161. [172]

    S., Chand, H., & Zhang, X.-G

    Rakshit, S., Stalin, C. S., Chand, H., & Zhang, X.-G. 2017, ApJS, 229, 39, doi: 10.3847/1538-4365/aa6971

  162. [173]

    2025, arXiv e-prints, arXiv:2507.08179

    Reiss, T., Trakhtenbrot, B., Ricci, C., et al. 2025, arXiv e-prints, arXiv:2507.08179. https://arxiv.org/abs/2507.08179

  163. [174]

    S., Miller, E

    Reynolds, C. S., Miller, E. D., Hodges-Kluck, E., et al. 2024, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 13093, Space Telescopes and Instrumentation 2024: Ultraviolet to Gamma Ray, ed. J.-W. A. den Herder, S. Nikzad, & K. Nakazawa, 13...

  164. [175]

    2023, ApJ, 945, 55, doi: 10.3847/1538-4357/acb5a6

    Ricci, C., & Paltani, S. 2023, ApJ, 945, 55, doi: 10.3847/1538-4357/acb5a6

  165. [176]

    2014a, A&A, 567, A142, doi: 10.1051/0004-6361/201322701

    Ricci, C., Ueda, Y., Ichikawa, K., et al. 2014a, A&A, 567, A142, doi: 10.1051/0004-6361/201322701

  166. [177]

    J., et al

    Ricci, C., Ueda, Y., Koss, M. J., et al. 2015, ApJL, 815, L13, doi: 10.1088/2041-8205/815/1/L13

  167. [178]

    2014b, MNRAS, 441, 3622, doi: 10.1093/mnras/stu735

    Ricci, C., Ueda, Y., Paltani, S., et al. 2014b, MNRAS, 441, 3622, doi: 10.1093/mnras/stu735

  168. [179]

    Ricci, C., Walter, R., Courvoisier, T. J. L., & Paltani, S. 2011, A&A, 532, A102, doi: 10.1051/0004-6361/201016409

  169. [180]

    J., et al

    Ricci, C., Trakhtenbrot, B., Koss, M. J., et al. 2017a, ApJS, 233, 17, doi: 10.3847/1538-4365/aa96ad —. 2017b, Nature, 549, 488, doi: 10.1038/nature23906 —. 2017c, Nature, 549, 488, doi: 10.1038/nature23906

  170. [181]

    C., Fabian, A

    Ricci, C., Ho, L. C., Fabian, A. C., et al. 2018, MNRAS, 480, 1819, doi: 10.1093/mnras/sty1879

  171. [182]

    T., Temple, M

    Ricci, C., Ananna, T. T., Temple, M. J., et al. 2022a, ApJ, 938, 67, doi: 10.3847/1538-4357/ac8e67

  172. [183]

    2023a, ApJ, 959, 27, doi: 10.3847/1538-4357/ad0733 —

    Ricci, C., Ichikawa, K., Stalevski, M., et al. 2023a, ApJ, 959, 27, doi: 10.3847/1538-4357/ad0733 —. 2023b, ApJ, 959, 27, doi: 10.3847/1538-4357/ad0733

  173. [184]

    E., et al

    Ricci, F., Treister, E., Bauer, F. E., et al. 2022b, ApJS, 261, 8, doi: 10.3847/1538-4365/ac5b67

  174. [185]

    2009, ApJL, 700, L6, doi: 10.1088/0004-637X/700/1/L6

    Risaliti, G., Young, M., & Elvis, M. 2009, ApJL, 700, L6, doi: 10.1088/0004-637X/700/1/L6

  175. [186]

    G., & Buchner, J

    Saha, T., Markowitz, A. G., & Buchner, J. 2022, MNRAS, 509, 5485, doi: 10.1093/mnras/stab3250

  176. [187]

    2022, A&A, 663, A28, doi: 10.1051/0004-6361/202142760

    Salvestrini, F., Gruppioni, C., Hatziminaoglou, E., et al. 2022, A&A, 663, A28, doi: 10.1051/0004-6361/202142760

  177. [188]

    2014, A&A, 562, A30, doi: 10.1051/0004-6361/201322835

    Santini, P., Maiolino, R., Magnelli, B., et al. 2014, A&A, 562, A30, doi: 10.1051/0004-6361/201322835

  178. [189]

    2007, MNRAS, 382, 1415, doi: 10.1111/j.1365-2966.2007.12487.x

    Schawinski, K., Thomas, D., Sarzi, M., et al. 2007, MNRAS, 382, 1415, doi: 10.1111/j.1365-2966.2007.12487.x

  179. [190]

    2025, A&A, 697, A175, doi: 10.1051/0004-6361/202348804

    Scholtz, J., Maiolino, R., D’Eugenio, F., et al. 2025, A&A, 697, A175, doi: 10.1051/0004-6361/202348804

  180. [191]

    1978, The Annals of Statistics, 6, 461 , doi: 10.1214/aos/1176344136

    Schwarz, G. 1978, The Annals of Statistics, 6, 461 , doi: 10.1214/aos/1176344136

  181. [192]

    2025, A&A, 697, A78, doi: 10.1051/0004-6361/202452435

    Sengupta, D., Torres-Alb` a, N., Pizzetti, A., et al. 2025, A&A, 697, A78, doi: 10.1051/0004-6361/202452435

  182. [193]

    2006, ApJL, 646, L29, doi: 10.1086/506911 —

    Kaspi, S. 2006, ApJL, 646, L29, doi: 10.1086/506911 —. 2008, ApJ, 682, 81, doi: 10.1086/588776

  183. [194]

    2022, ApJ, 936, 39, doi: 10.3847/1538-4357/ac82f4

    Sicilian, D., Civano, F., Cappelluti, N., Buchner, J., & Peca, A. 2022, ApJ, 936, 39, doi: 10.3847/1538-4357/ac82f4

  184. [195]

    2023, A&A, 676, A49, doi: 10.1051/0004-6361/202346364

    Signorini, M., Marchesi, S., Gilli, R., et al. 2023, A&A, 676, A49, doi: 10.1051/0004-6361/202346364

  185. [196]

    L., Koss, M., Mushotzky, R., et al

    Smith, K. L., Koss, M., Mushotzky, R., et al. 2020a, ApJ, 904, 83, doi: 10.3847/1538-4357/abc3c4

  186. [197]

    L., Mushotzky, R

    Smith, K. L., Mushotzky, R. F., Koss, M., et al. 2020b, MNRAS, 492, 4216, doi: 10.1093/mnras/stz3608

  187. [198]

    A., Siemiginowska, A., & Gierli´ nski, M

    Sobolewska, M. A., Siemiginowska, A., & Gierli´ nski, M. 2011, MNRAS, 413, 2259, doi: 10.1111/j.1365-2966.2011.18302.x

  188. [199]

    J., Benford, D

    Stern, D., Assef, R. J., Benford, D. J., et al. 2012, ApJ, 753, 30, doi: 10.1088/0004-637X/753/1/30

  189. [200]

    2019, ApJ, 877, 95, doi: 10.3847/1538-4357/ab1b20 —

    Tanimoto, A., Ueda, Y., Odaka, H., et al. 2019, ApJ, 877, 95, doi: 10.3847/1538-4357/ab1b20 —. 2020, ApJ, 897, 2, doi: 10.3847/1538-4357/ab96bc

  190. [201]

    2022, ApJS, 260, 30, doi: 10.3847/1538-4365/ac5f59 42 Peca et al

    Tanimoto, A., Ueda, Y., Odaka, H., Yamada, S., & Ricci, C. 2022, ApJS, 260, 30, doi: 10.3847/1538-4365/ac5f59 42 Peca et al

  191. [202]

    Taylor, M. B. 2005, in Astronomical Society of the Pacific Conference Series, Vol. 347, Astronomical Data Analysis Software and Systems XIV, ed. P. Shopbell, M. Britton, & R. Ebert, 29

  192. [203]

    M., Koss, M

    Tokayer, Y. M., Koss, M. J., Urry, C. M., et al. 2025, ApJ, 982, 134, doi: 10.3847/1538-4357/adb8c9

  193. [204]

    F., et al

    Tombesi, F., Tazaki, F., Mushotzky, R. F., et al. 2014, MNRAS, 443, 2154, doi: 10.1093/mnras/stu1297 Torres-Alb` a, N., Marchesi, S., Zhao, X., et al. 2023, A&A, 678, A154, doi: 10.1051/0004-6361/202345947

  194. [205]

    2023, MNRAS, 526, 1687, doi: 10.1093/mnras/stad2775

    Tortosa, A., Ricci, C., Ar´ evalo, P., et al. 2023, MNRAS, 526, 1687, doi: 10.1093/mnras/stad2775

  195. [206]

    J., et al

    Trakhtenbrot, B., Ricci, C., Koss, M. J., et al. 2017, MNRAS, 470, 800, doi: 10.1093/mnras/stx1117

  196. [207]

    M., & Simmons, B

    Treister, E., Schawinski, K., Urry, C. M., & Simmons, B. D. 2012, ApJL, 758, L39, doi: 10.1088/2041-8205/758/2/L39

  197. [208]

    Treister, E., & Urry, C. M. 2005, ApJ, 630, 115, doi: 10.1086/431892

  198. [209]

    A., Heckman, T

    Tremonti, C. A., Heckman, T. M., Kauffmann, G., et al. 2004, ApJ, 613, 898, doi: 10.1086/423264

  199. [210]

    2013, ApJ, 778, 33, doi: 10.1088/0004-637X/778/1/33

    Ueda, S., Hayashida, K., Anabuki, N., et al. 2013, ApJ, 778, 33, doi: 10.1088/0004-637X/778/1/33

  200. [211]

    Watson, M. G. 2014, ApJ, 786, 104, doi: 10.1088/0004-637X/786/2/104

  201. [212]

    2015, ApJ, 815, 1, doi: 10.1088/0004-637X/815/1/1

    Ueda, Y., Hashimoto, Y., Ichikawa, K., et al. 2015, ApJ, 815, 1, doi: 10.1088/0004-637X/815/1/1

  202. [213]

    M., & Padovani, P

    Urry, C. M., & Padovani, P. 1995, PASP, 107, 803, doi: 10.1086/133630

  203. [214]

    Veilleux, S., & Osterbrock, D. E. 1987, ApJS, 63, 295, doi: 10.1086/191166

  204. [215]

    2018, A&A, 620, A193, doi: 10.1051/0004-6361/201732495

    Vergani, D., Garilli, B., Polletta, M., et al. 2018, A&A, 620, A193, doi: 10.1051/0004-6361/201732495

  205. [216]

    2011, A&A, 536, A105, doi: 10.1051/0004-6361/201117752

    Vernet, J., Dekker, H., D’Odorico, S., et al. 2011, A&A, 536, A105, doi: 10.1051/0004-6361/201117752

  206. [217]

    2014, A&A, 571, A34, doi: 10.1051/0004-6361/201424791

    Vignali, C., Mignoli, M., Gilli, R., et al. 2014, A&A, 571, A34, doi: 10.1051/0004-6361/201424791

  207. [218]

    2015, A&A, 583, A141, doi: 10.1051/0004-6361/201525852

    Vignali, C., Iwasawa, K., Comastri, A., et al. 2015, A&A, 583, A141, doi: 10.1051/0004-6361/201525852

  208. [219]

    2022, ApJ, 941, 97, doi: 10.3847/1538-4357/ac9c07

    Vijarnwannaluk, B., Akiyama, M., Schramm, M., et al. 2022, ApJ, 941, 97, doi: 10.3847/1538-4357/ac9c07

  209. [220]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, doi: 10.1038/s41592-019-0686-2

  210. [221]

    2014, MNRAS, 445, 3557, doi: 10.1093/mnras/stu2004

    Vito, F., Gilli, R., Vignali, C., et al. 2014, MNRAS, 445, 3557, doi: 10.1093/mnras/stu2004

  211. [222]

    N., Yang, G., et al

    Vito, F., Brandt, W. N., Yang, G., et al. 2018, MNRAS, 473, 2378, doi: 10.1093/mnras/stx2486

  212. [223]

    C., Tananbaum, H

    Weisskopf, M. C., Tananbaum, H. D., Van Speybroeck, L. P., & O’Dell, S. L. 2000, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 4012, X-Ray Optics, Instruments, and Missions III, ed. J. E. Truemper & B. Aschenbach, 2–16, doi: 10.1117/12.39...

  213. [224]

    R., Gallo, L

    Wilkins, D. R., Gallo, L. C., Costantini, E., Brandt, W. N., & Blandford, R. D. 2022, MNRAS, 512, 761, doi: 10.1093/mnras/stac416

  214. [225]

    L., Eisenhardt, P

    Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868, doi: 10.1088/0004-6256/140/6/1868

  215. [226]

    I., Papovich, C., et al

    Yang, G., Caputi, K. I., Papovich, C., et al. 2023, ApJL, 950, L5, doi: 10.3847/2041-8213/acd639

  216. [227]

    2012, MNRAS, 423, 3360, doi: 10.1111/j.1365-2966.2012.21129.x

    Yaqoob, T. 2012, MNRAS, 423, 3360, doi: 10.1111/j.1365-2966.2012.21129.x

  217. [228]

    Turner, T. J. 2015, MNRAS, 454, 973, doi: 10.1093/mnras/stv2021

  218. [229]

    G., Adelman, J., Anderson, Jr., J

    York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120, 1579, doi: 10.1086/301513

  219. [230]

    A., & Zakamska, N

    Yuan, S., Strauss, M. A., & Zakamska, N. L. 2016, MNRAS, 462, 1603, doi: 10.1093/mnras/stw1747

  220. [231]

    T., et al

    Yue, M., Eilers, A.-C., Ananna, T. T., et al. 2024, ApJL, 974, L26, doi: 10.3847/2041-8213/ad7eba

  221. [232]

    L., Strauss, M

    Zakamska, N. L., Strauss, M. A., Krolik, J. H., et al. 2003, AJ, 126, 2125, doi: 10.1086/378610

  222. [233]

    2021, A&A, 650, A57, doi: 10.1051/0004-6361/202140297

    Zhao, X., Marchesi, S., Ajello, M., et al. 2021, A&A, 650, A57, doi: 10.1051/0004-6361/202140297

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.