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REVIEW 3 major objections 4 minor 297 references

A 375,000-source X-ray survey shows black hole growth peaked at z≈1.5 and that ~80% of accreted mass remains hidden by obscuration.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-07-31 23:34 UTC pith:Q7IMDZFJ

load-bearing objection A landmark eROSITA XLF with a solid low-z core, but the high-z bright-end claims rest on a photo-z-only sample that hasn't been validated at those redshifts. the 3 major comments →

arxiv 2607.27887 v1 pith:Q7IMDZFJ submitted 2026-07-30 astro-ph.GA

The eROSITA X-ray luminosity function of active galactic nuclei

W. Roster , J. Buchner , M. Salvato , R. Shirley , A. Merloni , T. Dwelly , J. Aird , A. Georgakakis
show 18 more authors
P. Boorman M. Brusa W. N. Brandt B. Trakhtenbrot B. Laloux P. Baldini C. Andonie C. Aydar C. Ricci S. F. Anderson P. Chakraborty R. J. Assef D. P. Schneider E. Kyritsis M. Kluge E. Bulbul K. Nandra J. Weller
This is my paper
classification astro-ph.GA
keywords eROSITAX-ray luminosity functionactive galactic nucleiblack hole accretion rate densityobscured growthluminosity-dependent evolutionphotometric redshiftssurvey selection function
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper uses the eROSITA X-ray survey, with roughly 375,000 active galactic nuclei, to measure the soft X-ray luminosity function across eight orders of magnitude in luminosity and out to redshift 6. It introduces a smoothly broken power-law model whose slope, break, and normalization all evolve continuously with redshift, and fits it with a forward-folding Poisson likelihood. The authors find that the space density of bright AGN is lower at low redshift and higher at high redshift than earlier estimates, and that moderate-luminosity AGN dominate the integrated growth. Integrating the luminosity function yields a black-hole accretion-rate density peaking at z≈1.5, and a cumulative black-hole mass density that recovers only about 14–26% of the local black-hole mass density, implying roughly 80% of growth occurred in obscured or Compton-thick phases missed by soft X-ray selection.

Core claim

The central claim is that the eROSITA sample, the largest X-ray-selected AGN sample used for a luminosity-function measurement to date, resolves luminosity-dependent evolution of the AGN population and revises the cosmic growth history. Compared with previous XLF determinations, the authors find lower space densities for moderately and very luminous AGN at low redshift, and higher abundances of the most luminous quasars at z≳5. When the XLF is integrated, the black-hole accretion-rate density peaks at z≈1.5, and the cumulative mass density at z=0 is only 14–26% of the locally inferred black-hole mass density, which the authors interpret as about 80% obscured growth that is invisible in the s

What carries the argument

The load-bearing tool is a redshift-dependent smoothly broken power-law (SBPL) luminosity function in which all parameters—normalisation, break luminosity, faint- and bright-end slopes, and knee width—evolve continuously with redshift via a smooth transition function. The fit uses a forward-folding Poisson point-process likelihood that marginalises over photometric-redshift probability distributions and applies the survey selection function tile by tile. The SBPL form matters because it lets the break luminosity rise strongly with redshift and the knee stay narrow, features that drive the inferred BHAD peak and the UV-missed fraction.

Load-bearing premise

At high redshift the eROSITA sample only constrains the bright side of the XLF, so the shape of the faint end—and everything that depends on it, including the BHAD and BHMD—rests on the assumed smoothly broken power-law parameterisation rather than on measured faint sources.

What would settle it

A deep X-ray survey reaching log L2-10 keV ≈ 42–43 at z≈3–5 (e.g., a few hundred ks Chandra-like pencil-beam field) would measure the faint-end slope directly; if the recovered faint-end slope and break position at high z disagree with the SBPL extrapolation, the inferred BHAD peak and the ~80% obscured fraction would need revision. Alternatively, a fully non-parametric reconstruction of the XLF in the same redshift range would settle whether the narrow knee and rising break luminosity are real or an artefact of the assumed form.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • The XLF's luminosity-dependent evolution means AGN 'downsizing' is directly measured from a single survey: luminous quasars peak at higher redshift than moderate-luminosity AGN.
  • If the BHAD peak at z≈1.5 holds, the bulk of black-hole mass was assembled later than in models peaking at z≈2, with consequences for feedback and galaxy evolution simulations.
  • The ~80% obscured-growth fraction implies that soft X-ray surveys alone miss most of the accretion power, so cosmic-X-ray-background and IR-based censuses must be combined to close the Soltan budget.
  • The higher abundance of rare luminous quasars at z≳5 means earlier bright-end extrapolations underpredicted the high-redshift quasar population, affecting reionization and seed-growth models.
  • The release of ~1.2 million AGN with redshifts (nearly 400,000 spectroscopic) provides a homogeneous basis for future demographic studies.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • A testable extension would be to fix the faint-end slope at high redshift using deep pencil-beam surveys and re-fit; if the high-z BHAD shift disappears, the low-luminosity leverage, not the bright-end data, drives the new peak redshift.
  • The UV-missed fraction rising to ~80% for the most luminous bin at z≳2.5 implies optical/UV quasar luminosity functions increasingly undercount the accretion power at the bright end; cross-checks with ALMA or mid-IR photometry of X-ray-selected high-z quasars could discriminate between obscuration and intrinsically weak UV emission.
  • The authors' comparison to local BHMD depends on the adopted bolometric correction (two choices yield ~14% vs ~26% recovery); if the local BHMD itself declines with better scaling relations, the implied hidden fraction shrinks.
  • The NewAthena yield forecast is a direct prediction: a deep X-ray survey should find the largest gain in faint AGN at z≳2, so the forecast can be checked with early survey pointings.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. This paper measures the rest-frame 0.2-2.3 keV (converted to 2-10 keV) AGN luminosity function using about 378,000 X-ray-selected sources from eROSITA DR2 and eFEDS. The XLF is modelled with a smoothly broken power law in which the normalisation, break luminosity, faint/bright-end slopes, and knee width all evolve continuously with redshift via a second smoothly broken power law (24 free parameters). The fit uses a forward-folding Poisson point-process likelihood with tile-dependent selection functions and full photo-z PDF marginalisation. The authors report luminosity-dependent evolution, with lower bright-end space densities at low redshift and higher abundances at the highest redshifts than earlier determinations; a UV-missed fraction that decreases with luminosity and increases with redshift in the brightest bin; a BHAD peaking at z ~ 1.5; and a cumulative BHMD at z ~ 0 that recovers only ~14-26% of local black-hole mass density estimates, interpreted as ~80% obscured growth. The eRASS DR2 AGN catalogue, with spectroscopic and photometric redshifts, is released.

Significance. If the high-redshift results survive scrutiny, this is a landmark measurement: it is the largest AGN sample used for XLF work to date, with an order of magnitude more sources than earlier studies, a transparent forward-folding framework, a public catalogue release, and a flexible new parameterisation. The paper includes useful internal checks (Page & Carrera Vmax binned estimates, a mock recovery test against an input FDPL model, and explicit restriction of plotted model curves to data-supported luminosity ranges) and is unusually candid about parameter covariance and the faint-end leverage problem. The central limitations are not in the machinery but in the calibration of photo-z PDFs at z > 4.5 and in the dependence of the headline integrated quantities on the adopted parameterisation and on the unmodelled intrinsic absorption. These are addressable with additional sensitivity analyses, so the result is potentially important but not yet fully established.

major comments (3)
  1. [Table A.1; Appendix C; Eq. (4)] The z>4.5 XLF sample contains 1,608 DR2 + 198 eFEDS sources, of which only 5 + 2 have spectroscopic redshifts. All remaining constraints come from CIRCLEZ photo-z PDFs. Appendix C reports a global outlier fraction eta = 17%, driven by faint (r > 22) counterparts — precisely the optical regime of high-redshift candidates. Eq. (4) marginalises over the photo-z PDFs, which is correct only if the PDFs are calibrated at z>4.5; no spectroscopic validation is presented in that regime. A low-redshift source whose photo-z PDF has a tail at z>4.5 would be interpreted as an extremely luminous AGN (same counts, much larger distance), directly inflating the claimed high-z bright-end excess (Sect. 4.2), the rising UV-missed fraction in the brightest bin (Sect. 4.4, Fig. 7), and the high-z contribution to BHAD/BHMD (Sect. 5). Please (i) quantify the tail probability of photo-z PDFs above z=4.5 using th
  2. [Sect. 4.2; Eqs. (12), (15), (16)] The paper acknowledges that at high redshift 'the lack of low-luminosity leverage limits our ability to constrain the faint-end slope independently' and that the inferred faint-end behaviour is sensitive to the adopted parameterisation (Sect. 4.2). This limitation propagates directly into the headline numbers. The BHAD and BHMD integrals in Eqs. (15)-(16) are evaluated over 40 < log L_2-10 < 47, and the UV-missed fraction in Eq. (12) compares the fitted XLF at luminosities where the model is partly extrapolated. Because C, L* and delta are covariant (Sect. 3.3), different SBPL parameters can produce nearly identical fits over the data-supported range while implying different faint-end extrapolations. The z ~ 1.5 BHAD peak and the 14-26% BHMD recovery are therefore not purely empirical. I request an explicit sensitivity test: recompute BHAD/BHMD and f_UVmiss with an alternative high-z fai
  3. [Sect. 3.2; Sect. 5.2] The baseline selection function and likelihood (Eqs. 3-5) assume a single unabsorbed power-law spectrum, with only Galactic absorption. The resulting XLF is a soft-band observed LF, not an intrinsic absorption-corrected LF. The central interpretation that ~80% of local black-hole mass growth is obscured (Sect. 5.2) is inferred as the residual between this soft-band integral and local BHMD estimates, but the size of the obscured correction is not self-consistently estimated within the model. Because the rest-frame bandpass hardens with redshift (Fig. F.1), the redshift dependence of the absorption bias is strong. I recommend adding a simple intrinsic NH-distribution sensitivity test — for example, re-fitting with a fixed CTN fraction or applying a column-density-dependent count-rate conversion — and quoting how the high-z bright-end space density, f_UVmiss, and BHMD change. This matters b
minor comments (4)
  1. [Table B.1] The reported posterior for alpha_a (-1.06) lies outside its stated prior range [-4.5, -1.5]. Please check whether the prior bound or the reported value is a typo; as printed, the fiducial best fit violates the prior.
  2. [Appendix E] The mock recovery test is a useful check, but it only demonstrates recovery of the Aird+15 FDPL model over the luminosity range probed by the mock data. It would be helpful to state explicitly the source counts in the z>4.5 bright-end cells of the mock and to test recovery with photo-z errors included, since this is the regime most relevant to the paper's new high-z claims.
  3. [Sect. 5.3.2] The 4MOST yield estimate mixes the released DR2 catalogue with a target catalogue based on eRASS:4. The relation between the two data releases should be clarified to avoid confusion about which selection function the forecast uses.
  4. [Fig. 1 caption] The caption states that marker size scales with the square root of the number of AGN used in literature XLF studies, and colour indicates geometric volume, but no scale bar or colour bar is shown. A legend would improve interpretability.

Circularity Check

0 steps flagged

No significant circularity: the XLF is a new maximum-likelihood fit to eROSITA data; BHAD, BHMD and the UV-missed fraction are integrals/comparisons of that fit, not independent predictions.

full rationale

The central XLF is not an output of a prior self-citation: it is a maximum-likelihood fit to the DR2+eFEDS source counts through the forward-folding Poisson likelihood of Eqs. (2)-(4), with selection functions derived from the surveys. The derived quantities—BHAD (Eq. 15), cumulative BHMD (Eq. 16), and f_UVmiss (Eq. 12)—are integrals or ratios of this fitted XLF and external literature QLFs; they are derived interpretations, not independent predictions, and that does not make the derivation circular. The SBPL shape (Eqs. 6-9) is an adopted parametrization, not a theorem; the paper explicitly acknowledges where it dominates: 'the lack of low-luminosity leverage limits our ability to constrain the faint-end slope independently. In this regime, the inferred faint-end behaviour is more sensitive to the adopted parametrisation' (Sect. 4.2), and it restricts plotted model curves to data-supported luminosity ranges. Appendix C also reports a photo-z outlier fraction of η=17% driven by faint r>22 counterparts, a data-quality/statistical risk for the high-redshift sample, but not a circularity. Self-citations (Buchner et al. 2015; Aird et al. 2015; Laloux et al. 2023) motivate the flexible SBPL and provide comparison models, but the measurement is a new fit to new data, cross-checked with binned Vmax estimates and a mock-recovery test (Appendix E). No step reduces by construction to its input: no fitted parameter is relabelled a prediction, no derived quantity is defined in terms of the claim it supports, and no load-bearing self-citation supplies the result. Hence no significant circularity.

Axiom & Free-Parameter Ledger

25 free parameters · 7 axioms · 0 invented entities

The central measurement is a forward-folding fit of a 25-parameter empirical XLF model. All 25 parameters are fitted to the data and hence are free parameters. The analysis also relies on several domain assumptions (spectral model, bolometric corrections, radiative efficiency, alpha_ox relation) that are taken from the literature and are not independently tested in this paper.

free parameters (25)
  • L_star_norm = 1.15e44 erg/s
    Break luminosity at z = L_star_zc (Table B.1)
  • L_star_a = 4.73
    Low-z power-law slope of L_star(z) (Table B.1)
  • L_star_b = 1.79
    High-z power-law slope of L_star(z) (Table B.1)
  • L_star_zc = 0.68
    Transition redshift for L_star evolution (Table B.1)
  • L_star_delta = 0.25
    Transition width for L_star evolution (Table B.1)
  • C_norm = 2.11e-5 Mpc^-3 dex^-1
    Normalisation of XLF at z = C_zc (Table B.1)
  • C_a = 6.32
    Low-z slope of C(z) (Table B.1)
  • C_b = -8.80
    High-z slope of C(z) (Table B.1)
  • C_zc = 0.99
    Transition redshift for C evolution (Table B.1)
  • C_delta = 0.46
    Transition width for C evolution (Table B.1)
  • alpha_norm = 0.60
    Faint-end slope at z = alpha_zc (Table B.1)
  • alpha_a = -1.06
    Low-z slope of alpha(z) (Table B.1)
  • alpha_b = -0.69
    High-z slope of alpha(z) (Table B.1)
  • alpha_zc = 0.24
    Transition redshift for alpha evolution (Table B.1)
  • alpha_delta = 0.24
    Transition width for alpha evolution (Table B.1)
  • beta_norm = 2.15
    Bright-end slope at z = beta_zc (Table B.1)
  • beta_a = 1.70
    Low-z slope of beta(z) (Table B.1)
  • beta_b = 0.01
    High-z slope of beta(z) (Table B.1)
  • beta_zc = 0.18
    Transition redshift for beta evolution (Table B.1)
  • beta_delta = 0.27
    Transition width for beta evolution (Table B.1)
  • delta_norm = 0.38
    Knee width at z = delta_zc (Table B.1)
  • delta_a = -0.11
    Low-z slope of delta(z) (Table B.1)
  • delta_b = -0.29
    High-z slope of delta(z) (Table B.1)
  • delta_zc = 0.26
    Transition redshift for delta evolution (Table B.1)
  • delta_delta = 0.17
    Transition width for delta evolution (Table B.1)
axioms (7)
  • ad hoc to paper The AGN XLF is described by a smoothly broken power law in luminosity with redshift-dependent parameters (Eq. 6).
    A chosen phenomenological model; the paper argues it is flexible, but it is not derived from physics.
  • ad hoc to paper Each XLF parameter evolves with redshift following a smoothly broken power law (Eq. 9).
    Similarly ad hoc; the motivation is to allow continuous evolution and saturation, but it is not unique.
  • domain assumption X-ray sources follow a power-law spectrum with photon index Γ=2, absorbed only by the Galactic column (Section 3.2).
    Simplifies count-rate to luminosity conversion; intrinsic absorption is ignored in the baseline fit.
  • standard math Cosmology H0=70, ΩM=0.3, ΩΛ=0.7, Ωk=0.
    Standard cosmological parameters used for volume calculations.
  • domain assumption Bolometric corrections from Duras et al. (2020) and Hopkins et al. (2007) are applicable to the soft X-ray selected AGN.
    Used to convert X-ray luminosity to bolometric for BHAD; choice affects normalization.
  • domain assumption Radiative efficiency ξ=0.1.
    Assumed for converting luminosity to accretion rate.
  • domain assumption The alpha_ox relation of Lusso & Risaliti (2016) is used to convert UV/optical QLFs to X-ray luminosities.
    Used in the UV-missed fraction comparison; scatter is included, but systematic offsets are possible.

pith-pipeline@v1.3.0-daily-deepseek · 37197 in / 11286 out tokens · 96839 ms · 2026-07-31T23:34:24.652812+00:00 · methodology

0 comments
read the original abstract

The X-ray luminosity function (XLF) of active galactic nuclei (AGN) provides an observational probe of the growth of supermassive black holes (SMBHs) across cosmic time. With its large survey grasp, Spectrum Roentgen Gamma (SRG)/eROSITA samples the luminosity--redshift plane with a depth--area balance complementary to pencil-beam surveys, providing the volume needed to detect rare luminous AGN, previously limited by small-number statistics. We measure the soft XLF, leveraging an eROSITA sample spanning approximately eight orders of magnitude in luminosity out to $z\simeq6$. This enables us to study luminosity-dependent evolution with improved constraints, and to infer both the SMBH accretion history and optical/UV missed AGN population. We introduce a new redshift-dependent smoothly broken power-law parameterisation in which all XLF model parameters are allowed to evolve continuously with redshift. We find lower space densities for moderately and very luminous AGN at low redshift, while the abundance is higher than previously found at the highest redshifts. Comparisons to optical/UV quasar LFs converted to rest-frame $2\!-\!10\,\mathrm{keV}$ show that the UV-missed fraction decreases with luminosity, and, in the most luminous bin, increases with redshift. Integrating the XLF yields a black-hole accretion-rate density peaking at $z \simeq 1.5$, with the corresponding cumulative black-hole mass density indicating $\sim80^{+11}_{-23}\%$ obscured growth relative to locally-inferred BH mass estimates derived from scaling relations and missed by the soft X-ray selection of our sample. With this work, we release the eROSITA DR2 AGN catalogue, including counterparts and their redshift information. We then discuss how these results can inform future spectroscopic, photometric, and X-ray survey strategies aimed at improving AGN demographic constraints.

Figures

Figures reproduced from arXiv: 2607.27887 by A. Georgakakis, A. Merloni, B. Laloux, B. Trakhtenbrot, C. Andonie, C. Aydar, C. Ricci, D. P. Schneider, E. Bulbul, E. Kyritsis, J. Aird, J. Buchner, J. Weller, K. Nandra, M. Brusa, M. Kluge, M. Salvato, P. Baldini, P. Boorman, P. Chakraborty, R. J. Assef, R. Shirley, S. F. Anderson, T. Dwelly, W. N. Brandt, W. Roster.

Figure 1
Figure 1. Figure 1: Illustrative comparison of demographic leverage for [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Rest-frame 0.2 − 2.3 keV luminosity–redshift dis￾tribution of the sources used in the XLF analysis. Purple and orange hexagons show the occupied regions of the DR2 and eFEDS samples, respectively. Dashed lines indicate the 1% effective-area limit for the two samples. The solid black curve indicates the break-luminosity evolution L⋆(z) ac￾cording to the Aird et al. (2015) soft-band selected flexi￾ble double… view at source ↗
Figure 3
Figure 3. Figure 3: eROSITA-DE western Galactic hemisphere foot [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Evolution of the best-fitting SBPL XLF parameters as a function of redshift. The coloured curves indicate the [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Each panel shows Φ from Eq. (6) as a function of rest-frame log10 (L2−10 keV) at the redshift indicated in the upper-right corner. The coloured markers show binned estimates from the DR2 and eFEDS samples computed with the Page & Carrera (2000) Vmax-style estimator corrected for the selection function. The faint violet curve, repeated in panels other than the lowest-redshift panel, shows this work’s XLF fi… view at source ↗
Figure 6
Figure 6. Figure 6: Space density evolution with redshift. The coloured [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: UV-missed fraction inferred from the comparison between optical/UV QLFs converted to rest-frame [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Cosmic BHAD from Eq. (15) assuming Duras et al. (2020) (Hopkins et al. 2007) bolometric correction and a radia￾tive efficiency of ξ = 0.1. The grey shaded (hatched) region indicates the bootstrap 2σ uncertainty. Literature estimates from X-ray (Aird et al. 2015; Ananna et al. 2019; Wolf et al. 2021; Barlow-Hall et al. 2023; Pouliasis et al. 2025) and IR-selected (Delvecchio et al. 2014) AGN samples, the sc… view at source ↗
Figure 9
Figure 9. Figure 9: Cumulative BHMD inferred by integrating the [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Expected NewAthena detections of faint AGN in [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Forecasted NewAthena AGN yield in the luminosity–redshift plane. [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗

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Works this paper leans on

297 extracted references · 1 canonical work pages · 1 internal anchor

  1. [1]

    L., Georgakakis, A., et al

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

    2015

  2. [2]

    S., et al

    Aird , J., Nandra , K., Laird , E. S., et al. 2010, , 401, 2531

    2010

  3. [3]

    2012, , 546, A98

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

    2012

  4. [4]

    T., Treister , E., Urry , C

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

    2019

  5. [5]

    T., Weigel , A

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

    2022

  6. [6]

    M., Greenwell , C., et al

    Andonie , C., Alexander , D. M., Greenwell , C., et al. 2024, , 527, L144

    2024

  7. [7]

    M., Gandhi , P., et al

    Annuar , A., Alexander , D. M., Gandhi , P., et al. 2025, , 540, 3827

    2025

  8. [8]

    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

    1996

  9. [9]

    J., Kochanek , C

    Assef , R. J., Kochanek , C. S., Ashby , M. L. N., et al. 2011, , 728, 56

    2011

  10. [10]

    2025, , 698, A132

    Aydar , C., Merloni , A., Dwelly , T., et al. 2025, , 698, A132

    2025

  11. [11]

    Barger , A. J. & Cowie , L. L. 2005, , 635, 115

    2005

  12. [12]

    Barlow-Hall , C. L. & Aird , J. 2025, arXiv e-prints, arXiv:2506.16145

    Pith/arXiv arXiv 2025

  13. [13]

    L., Delaney , J., Aird , J., et al

    Barlow-Hall , C. L., Delaney , J., Aird , J., et al. 2023, , 519, 6055

    2023

  14. [14]

    G., Cava , A., et al

    Barro , G., P \'e rez-Gonz \'a lez , P. G., Cava , A., et al. 2019, , 243, 22

    2019

  15. [15]

    2026, arXiv e-prints, arXiv:2607.03430

    Belladitta , S., Decarli , R., Ba \ n ados , E., et al. 2026, arXiv e-prints, arXiv:2607.03430

    Pith/arXiv arXiv 2026

  16. [16]

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

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

    2025

  17. [17]

    Bowen , I. S. & Vaughan , Jr., A. H. 1973, , 12, 1430

    1973

  18. [18]

    J., Shanks , T., Croom , S

    Boyle , B. J., Shanks , T., Croom , S. M., et al. 2000, , 317, 1014

    2000

  19. [19]

    Brandt , W. N. & Yang , G. 2022, in Handbook of X-ray and Gamma-ray Astrophysics, ed. C. Bambi & A. Sangangelo (Springer), 78

    2022

  20. [20]

    2022, , 661, A1

    Brunner , H., Liu , T., Lamer , G., et al. 2022, , 661, A1

    2022

  21. [21]

    & Bauer , F

    Buchner , J. & Bauer , F. E. 2017, , 465, 4348

    2017

  22. [22]

    2015, ApJ, 802, 89

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

    2015

  23. [23]

    2021, nway: Bayesian cross-matching of astronomical catalogs , Astrophysics Source Code Library, record ascl:2102.014

    Buchner , J., Salvato , M., Budavari , T., & Fotopoulou , S. 2021, nway: Bayesian cross-matching of astronomical catalogs , Astrophysics Source Code Library, record ascl:2102.014

    2021

  24. [24]

    2011, ApJ, 728, 58

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

    2011

  25. [25]

    J., & Trakhtenbrot , B

    Caplar , N., Lilly , S. J., & Trakhtenbrot , B. 2015, , 811, 148

    2015

  26. [26]

    N., Urry , C

    Cardamone , C. N., Urry , C. M., Damen , M., et al. 2008, , 680, 130

    2008

  27. [27]

    N., van Dokkum , P

    Cardamone , C. N., van Dokkum , P. G., Urry , C. M., et al. 2010, , 189, 270

    2010

  28. [28]

    Carilli , C. L. & Walter , F. 2013, , 51, 105

    2013

  29. [29]

    D., et al

    Carpenter , B., Gelman , A., Hoffman , M. D., et al. 2017, JoSS, 76, 1

    2017

  30. [30]

    G., et al

    Civano , F., Zhao , X., Boorman , P. G., et al. 2024, Frontiers in Astronomy and Space Sciences, 11, 1340719

    2024

  31. [31]

    2026, , 706, A302

    Comastri , A., Lanzuisi , G., Vito , F., et al. 2026, , 706, A302

    2026

  32. [32]

    2019, , 487, 2005

    Comparat , J., Merloni , A., Salvato , M., et al. 2019, , 487, 2005

    2019

  33. [33]

    L., Songaila , A., Hu , E

    Cowie , L. L., Songaila , A., Hu , E. M., & Cohen , J. G. 1996, , 112, 839

    1996

  34. [34]

    A., Schaye , J., Bower , R

    Crain , R. A., Schaye , J., Bower , R. G., et al. 2015, , 450, 1937

    2015

  35. [35]

    M., Richards , G

    Croom , S. M., Richards , G. T., Shanks , T., et al. 2009, , 399, 1755

    2009

  36. [36]

    M., Smith , R

    Croom , S. M., Smith , R. J., Boyle , B. J., et al. 2004, , 349, 1397

    2004

  37. [37]

    J., Stevens , A

    Croton , D. J., Stevens , A. R. H., Tonini , C., et al. 2016, , 222, 22

    2016

  38. [38]

    2025, Nature Astronomy, 9, 36

    Cruise , M., Guainazzi , M., Aird , J., et al. 2025, Nature Astronomy, 9, 36

    2025

  39. [39]

    S., Agertz , O., Berbel , A

    de Jong , R. S., Agertz , O., Berbel , A. A., et al. 2019, The Messenger, 175, 3

    2019

  40. [40]

    2010, , 402, 1927

    Degraf , C., Di Matteo , T., & Springel , V. 2010, , 402, 1927

    2010

  41. [41]

    M., Bauer , F

    Del Moro , A., Alexander , D. M., Bauer , F. E., et al. 2016, , 456, 2105

    2016

  42. [42]

    2008, , 487, 119

    Della Ceca , R., Caccianiga , A., Severgnini , P., et al. 2008, , 487, 119

    2008

  43. [43]

    2014, , 439, 2736

    Delvecchio , I., Gruppioni , C., Pozzi , F., et al. 2014, , 439, 2736

    2014

  44. [44]

    G., et al

    DESI Collaboration , Abdul-Karim , M., Adame , A. G., et al. 2025, arXiv e-prints, arXiv:2503.14745

    Pith/arXiv arXiv 2025

  45. [45]

    J., Lang , D., et al

    Dey , A., Schlegel , D. J., Lang , D., et al. 2019, The Astronomical Journal, 157, 168

    2019

  46. [46]

    2008, ApJ, 676, 33

    Di Matteo, T., Colberg, J., Springel, V., Hernquist, L., & Sijacki, D. 2008, ApJ, 676, 33

    2008

  47. [47]

    2020, , 636, A73

    Duras , F., Bongiorno , A., Ricci , F., et al. 2020, , 636, A73

    2020

  48. [48]

    J., Page , M

    Ebrero , J., Carrera , F. J., Page , M. J., et al. 2009, , 493, 55

    2009

  49. [49]

    2025, A&A, 697, A1

    Euclid Collaboration: Mellier , Y., Abdurro'uf , Acevedo Barroso , J., et al. 2025, A&A, 697, A1

    2025

  50. [50]

    2025, arXiv e-prints, arXiv:2503.15316

    Euclid Collaboration: Roster , W., Salvato , M., Buchner , J., et al. 2025, arXiv e-prints, arXiv:2503.15316

    Pith/arXiv arXiv 2025

  51. [51]

    N., Evans , J

    Evans , I. N., Evans , J. D., Mart \' nez-Galarza , J. R., et al. 2024, ApJ Supplement Series, 274, 22

    2024

  52. [52]

    A., Schneider, D

    Fan, X., Strauss, M. A., Schneider, D. P., et al. 1999, The Astronomical Journal, 118, 1

    1999

  53. [53]

    M., Benson , A

    Fanidakis , N., Baugh , C. M., Benson , A. J., et al. 2012, , 419, 2797

    2012

  54. [54]

    & Merritt , D

    Ferrarese , L. & Merritt , D. 2000, ApJ Letters, 539, L9

    2000

  55. [55]

    2009, , 693, 447

    Fiore , F., Puccetti , S., Brusa , M., et al. 2009, , 693, 447

    2009

  56. [56]

    S., & Santini , P

    Fontanot , F., De Lucia , G., Monaco , P., Somerville , R. S., & Santini , P. 2009, , 397, 1776

    2009

  57. [57]

    2016, A & A, 592, A5

    Fotopoulou , S., Pacaud , F., Paltani , S., et al. 2016, A & A, 592, A5

    2016

  58. [58]

    Gaia Collaboration: Brown , A. G. A., Vallenari , A., Prusti , T., et al. 2018, , 616, A1

    2018

  59. [59]

    P., Mather , J

    Gardner , J. P., Mather , J. C., Abbott , R., et al. 2023, , 135, 068001

    2023

  60. [60]

    P., Mather , J

    Gardner , J. P., Mather , J. C., Clampin , M., et al. 2006, , 123, 485

    2006

  61. [61]

    A., Swartz , D

    Gaskin , J. A., Swartz , D. A., Vikhlinin , A., et al. 2019, Journal of Astronomical Telescopes, Instruments, and Systems, 5, 021001

    2019

  62. [62]

    2000, ApJ Letters, 539, L13

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

    2000

  63. [63]

    2015, , 453, 1946

    Georgakakis , A., Aird , J., Buchner , J., et al. 2015, , 453, 1946

    2015

  64. [64]

    S., Aird , J., & Trichas , M

    Georgakakis , A., Nandra , K., Laird , E. S., Aird , J., & Trichas , M. 2008, , 388, 1205

    2008

  65. [65]

    2017, , 469, 3232

    Georgakakis , A., Salvato , M., Liu , Z., et al. 2017, , 469, 3232

    2017

  66. [66]

    2022, , 666, A17

    Gilli , R., Norman , C., Calura , F., et al. 2022, , 666, A17

    2022

  67. [67]

    , Comastri, A

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

    2007

  68. [68]

    E., Labbe , I., Goulding , A

    Greene , J. E., Labbe , I., Goulding , A. D., et al. 2024, , 964, 39

    2024

  69. [69]

    J., Lacey , C

    Griffin , A. J., Lacey , C. G., Gonzalez-Perez , V., & Lagos , C. d. P. 2019, arXiv e-prints, arXiv:1912.09490

    Pith/arXiv arXiv 2019

  70. [70]

    M., Miller , J

    G \"u ltekin , K., Cackett , E. M., Miller , J. M., et al. 2009, , 706, 404

    2009

  71. [71]

    E., Siegmund , W

    Gunn , J. E., Siegmund , W. A., Mannery , E. J., et al. 2006, , 131, 2332

    2006

  72. [72]

    2025, , 537, 2323

    Habouzit , M. 2025, , 537, 2323

    2025

  73. [73]

    S., Li , Y., et al

    Habouzit , M., Somerville , R. S., Li , Y., et al. 2022, , 509, 3015

    2022

  74. [74]

    2026, , 709, A37

    H \"a mmerich , S., Gokus , A., McBride , F., et al. 2026, , 709, A37

    2026

  75. [75]

    2022, , 259, 20

    Harikane , Y., Ono , Y., Ouchi , M., et al. 2022, , 259, 20

    2022

  76. [76]

    2008, A & A, 490, 905

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

    2008

  77. [77]

    2005 a , A & A, 441, 417

    Hasinger , G., Miyaji , T., & Schmidt , M. 2005 a , A & A, 441, 417

    2005

  78. [78]

    2005 b , , 441, 417

    Hasinger , G., Miyaji , T., & Schmidt , M. 2005 b , , 441, 417

    2005

  79. [79]

    2016, , 594, A116

    HI4PI Collaboration , Ben Bekhti , N., Fl \"o er , L., et al. 2016, , 594, A116

    2016

  80. [80]

    Hickox , R. C. & Alexander , D. M. 2018, , 56, 625

    2018

Showing first 80 references.