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REVIEW 4 major objections 5 minor 141 references

Photometric Selection of type 1 Quasars in the XMM-LSS Field with Machine Learning and the Disk-Corona Connection

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A gradient-boosted classifier selects 1,591 type 1 quasars in XMM-LSS at 99.7% reliability and confirms the disk-corona luminosity relation.

desk verdict A useful new quasar sample and a reproduced α_OX–L2500 relation, but the blind-test performance claims are partly extrapolated to the faint end and the abstract overstates the reliability. read the letter →

arxiv 2412.06923 v1 pith:QYRYNLF5 submitted 2024-12-09 astro-ph.GA

Jian Huang , Bin Luo , W. N. Brandt , Ying Chen , Qingling Ni , Yongquan Xue , Zijian Zhang This is my paper · ORCID
classification astro-ph.GA
keywords type1quasarsphotometricselectionmachinelearningXGBoostXMM-LSSfieldalpha_OX-L2500relationdisk-coronaconnectionredshifts
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 claims that a machine-learning classifier can pick type 1 quasars out of multi-band photometry alone in the 5.3 square degree XMM-LSS field, with enough purity and completeness to build a sample fit for studying how the accretion disk and corona are connected. The authors train two XGBoost classifiers on SDSS spectra and apply them to 18 colors and 5 morphology features, selecting 1,591 quasars. On a blind-test sample drawn outside the field, the selection reports 99.7% reliability (344 of 345) and 87.5% completeness (344 of 393). Using the X-ray data for 1,016 radio-quiet, non-absorbed quasars, the paper derives the $\alpha_{\rm OX}$--$L_{2500}$ relation $\alpha_{\rm OX} = (-0.156\pm0.007) \log L_{2500} + (3.175\pm0.211)$ with dispersion 0.159, in agreement with earlier work. A sympathetic reader cares because photometric selection of this kind can deliver large quasar samples that reach lower luminosities than spectroscopic surveys, which helps test the physics of the disk-corona connection.

What carries the argument

The load-bearing machinery is a two-stage XGBoost classifier: the first stage separates stars from extragalactic objects, and the second separates quasars from galaxies, using 18 colors (from GALEX UV through optical to Spitzer IRAC) plus 5 morphology parameters built from PSF-minus-Kron magnitudes. XGBoost, a gradient-boosted decision tree ensemble, supplies built-in handling of missing photometry and regularization to limit overfitting; the thresholds $p_{\rm extragalactic} \ge 0.98$ and $p_{\rm quasar} \ge 0.95$ are chosen on the blind-test sample to favor high reliability. For the disk-corona relation, the paper defines $\alpha_{\rm OX}$ as the logarithmic ratio of 2500 Angstrom and 2 keV flux densities and uses the ASURV survival-analysis package with the EM algorithm to fit the linear relation while treating X-ray nondetections as upper limits.

What would settle it

Obtain spectra of roughly 100 randomly selected XGBoost-selected quasars in the XMM-LSS field with $22 < i < 23$ and no existing SDSS spectra; the paper's blind test finds no drop in reliability with magnitude, so a confirmation rate below about 90% would directly contradict the claim that the selection works to $i\approx23$.

Watch

Extended reading notes

Core claim

The central discovery claim is that photometric quasar selection by XGBoost transfers from the training field to unseen data and yields a sample large enough to measure the optical-to-X-ray connection with competitive accuracy. Specifically, the paper reports a blind-test reliability of 0.997 and completeness of 0.875 for type 1 quasars, with no significant dependence on i-band magnitude down to $i\approx23$, and it selects 1,591 quasars inside the XMM-LSS field whose sky density exceeds that of SDSS spectroscopy. For the subsample of 1,016 radio-quiet quasars without signs of X-ray absorption, the fitted relation $\alpha_{\rm OX} = (-0.156\pm0.007) \log L_{2500} + (3.175\pm0.211)$, with dispersion 0.159, agrees with the relations of Just et al. (2007) and Lusso & Risaliti (2016). The paper also finds that the slope steepens in the high-luminosity, high-redshift regime, consistent with an evolving disk-corona connection.

Load-bearing premise

The blind-test sample, drawn from SDSS spectroscopy outside the XMM-LSS field and with much sparser CFHTLS $u^*$-band and VIDEO coverage than the parent sample inside the field, is representative enough that the measured reliability and completeness transfer to the actual in-field selection.

Editorial extensions

If this is right

  • The same pipeline can be applied to the other two XMM-SERVS fields, W-CDF-S and ELAIS-S1, roughly doubling the available quasar sample for disk-corona studies.
  • The selected sample extends the low-luminosity end of $\alpha_{\rm OX}$ studies by about 0.3 dex compared with previous optically selected X-ray samples, reaching a 90% log $L_{2500}$ range lower bound of 28.87.
  • The $\alpha_{\rm OX}$--$L_{2500}$ slope is unchanged when restricting to 832 quasars with little UV reddening or to 741 X-ray-detected quasars, indicating the relation is not driven by dust, host-galaxy contamination, or X-ray-absorbed sources.
  • The XGBoost photometric redshifts (outlier fraction $\approx17\%$, $\sigma_{\rm NMAD} \approx 0.07$) outperform the Le PHARE SED fit on the same data, suggesting machine-learning photo-$z$s are a viable alternative for featureless quasar SEDs.
  • The slope steepens from $-0.156$ to about $-0.19$ in the $1.7<z<2.7$, high-luminosity subset, supporting the idea that the $\alpha_{\rm OX}$--$L_{2500}$ relation evolves and may be tied to black hole mass and Eddington ratio.

Reading between the lines

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

  • If the pipeline is applied to the other two XMM-SERVS fields, the combined sample could reach roughly 3,000 quasars and shrink the slope uncertainty; the paper flags this as future work rather than performing it.
  • The blind-test sample has much sparser CFHTLS $u^*$-band and VIDEO coverage than the in-field parent sample, so the reported 99.7% reliability may be conservative; a direct spectroscopic check inside the field would test this transfer.
  • The 275 X-ray-undetected quasars are treated as upper limits in the survival analysis; a stacking analysis of their average X-ray emission would test whether their inclusion or exclusion shifts the slope, which the paper does not do.
  • Because the photometric-redshift outlier fraction is about 17%, roughly one in six quasars may have a redshift error of $|\Delta z|/(1+z)>0.15$, which could add unrecognized scatter to the derived $\alpha_{\rm OX}$ values.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper applies XGBoost to multiwavelength photometry (HSC, Spitzer DeepDrill, VIDEO, CFHTLS, GALEX) to select type 1 quasars in the XMM-LSS field. The authors construct a parent sample of 168,805 sources, train two binary classifiers on SDSS spectroscopically confirmed quasars, galaxies, and stars, and validate on a blind-test sample outside the XMM-LSS field, reporting quasar reliability 0.997 and completeness 0.875. They estimate photometric redshifts with XGBoost (f_outlier ≈ 17%, σ_NMAD ≈ 0.07 for the u*-covered blind-test subset) and use X-ray data from Chen et al. (2018) to derive the α_OX–L_2500 relation for 1,016 radio-quiet quasars: α_OX = (−0.156 ± 0.007) log L_2500 + (3.175 ± 0.211), with dispersion 0.159. The relation agrees with Just et al. (2007) and Lusso & Risaliti (2016); the paper tests several potential biases and finds no significant effects, and reports tentative steepening at high L_2500/z.

Significance. The main contribution is a large photometrically selected quasar sample in a deep X-ray field, with public code and data, which can support future disk-corona studies and can be extended to the other XMM-SER VS fields. The α_OX–L_2500 analysis is an independent empirical fit compared against external literature, uses censored-data survival analysis, and includes explicit tests for X-ray absorption, reddening, and variability; these are genuine strengths. The derived relation is consistent with previous work, and the low-luminosity extension relative to Lusso & Risaliti (2016) is potentially valuable. The principal caveat is that the blind-test validation sample is brighter and has different multiwavelength coverage than the in-field parent sample, so the headline reliability and completeness, especially at 22 < i < 23, should be viewed with caution.

major comments (4)
  1. [Section 3.5 and Table 2] There is a one-object inconsistency in the headline numbers. The text states that 'of the 345 selected quasars, 344 are SDSS quasars,' while Table 2 reports 344 selected quasars including 343 SDSS quasars and one galaxy. The abstract quotes a reliability of ≈99.9%, but the text and Table 3 give 0.997 (99.7%). Because the reliability and completeness figures are central to the sample-construction claim, these numbers must be reconciled before publication.
  2. [Sections 2.4, 2.5, 3.5, and Figure 2] The blind-test sample is not representative of the parent sample at the faint end. The parent sample has mean i = 21.7, while both the training and blind-test quasars have mean i = 20.7, and the blind-test contains only 393 quasars. The paper's claim that it selects 649 quasars at 22 < i < 23 (23% of the 2,784 selected quasars) and the low-luminosity end of the α_OX–L_2500 relation therefore rely on the assumption that performance does not degrade at faint magnitudes. Figure 6 shows no significant trend, but the faint bins have large Gehrels uncertainties and are based on few objects; this is an extrapolation rather than a validation. Please provide per-bin counts and uncertainties, and either additional faint-end validation (for example, using the in-field MMT/WIYN spectroscopy) or an explicit statement that faint-end performance is not directly verified.
  3. [Table 1 and Sections 2.5, 3.1] The blind-test sample has much lower CFHTLS u* and VIDEO coverage than the training sample (u*: 67.9% vs 99.3%; VIDEO: 16.8% vs 80.9%). Because XGBoost's built-in missing-value routine can learn coverage-dependent split rules, the blind test evaluates a different missingness pattern from the one present in the XMM-LSS parent sample. The statement in Section 3.5 that in-field performance 'might be better' is speculation; the opposite could also hold. The authors should test the model under the in-field missingness pattern (for example, by masking in-field training data) or temper the performance claims accordingly.
  4. [Section 4] The adopted photo-z accuracy (f_outlier ≈ 17%, σ_NMAD ≈ 0.07) is based on only 211 blind-test quasars with CFHT u* detection, whereas the full blind test gives f_outlier = 22.7% and σ_NMAD = 0.079. Since roughly 42% of the 1,591 selected quasars use photo-zs, the systematic difference between these two estimates should be propagated into the α_OX analysis, or at least discussed as a source of uncertainty in L_2500 and hence in Equation 4.
minor comments (5)
  1. [Section 5.5] The text refers to '211 high-L SDSS quasars' but then says the relation slope was derived for 'the 212 quasars'; the intended number should be stated consistently.
  2. [Abstract and Conclusion] The abstract reports reliability ≈99.9% and completeness ≈87.5%, while the Conclusion says 'approximately 99%' and 'approximately 90%'; these should be harmonized with the exact values in Section 3.5 and Table 3.
  3. [Section 5.3] The statement that the method extends the luminosity range 'to the low-luminosity end' is contradicted by the paper's own comparison: the lower limit of the 90% log L_2500 range is 28.87, which is 0.3 dex higher than the 28.56 for Just et al. (2007). The extension is relative to Lusso & Risaliti (2016) or to previous ML-selected samples and should be phrased accordingly.
  4. [Section 3.2] The sentence 'The entire training sample is used for both training and validation' is imprecise; the paper actually uses five-fold cross-validation, which should be stated explicitly.
  5. [Section 4] The comparison of XGBoost photo-zs with Le PHARE is useful, but the manuscript should state whether the Le PHARE results use the same 13-band data and the same blind-test sample selection, to make the comparison fully transparent.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the machine-learning selection is validated on held-out SDSS spectra, and the α_OX–L2500 relation is an empirical fit benchmarked against independent literature samples.

full rationale

The paper's central derivation chain is not circular. The quasar selection pipeline is trained on spectroscopically identified SDSS quasars, galaxies, and stars and evaluated on a blind-test sample constructed in the same way but excluded from training; this is a standard supervised-learning validation, not a case where a fitted parameter is renamed as a prediction. The reliability and completeness numbers are computed directly from the blind-test confusion matrix (Section 3.5, Table 3) and are not forced by the training labels. The photometric redshifts are likewise trained on a spectroscopic subsample and assessed on an independent blind-test sample, with quality metrics reported separately (Section 4). The α_OX–L2500 relation is an empirical regression of α_OX on L2500 computed from X-ray and optical photometry of the selected quasars (Equation 4). Although α_OX and L2500 both depend on f2500 by definition, the fitted slope (−0.156) is not the value that the shared f2500 term alone would impose (−0.384 if the X-ray flux were uncorrelated with optical luminosity), so the result carries independent information about the f2keV–f2500 correlation. Moreover, the paper explicitly compares Equation 4 with the relations of Just et al. (2007) and Lusso & Risaliti (2016), and also re-derives a comparison relation from Lusso et al. (2020), providing external benchmarks rather than relying on the authors' own prior results. Self-citations appear (e.g., Chen et al. 2018 for the X-ray catalog, Pu et al. 2020 for the Γeff threshold), but these supply data products and externally published criteria, and the paper tests alternative Γeff thresholds, so the central claim does not reduce to a self-citation chain. The main validity concern is the representativeness of the blind-test sample for the fainter in-field parent sample — the blind-test quasars are brighter and have less CFHTLS u* and VIDEO coverage — but this is a generalization limitation, not a circularity. The claimed completeness and reliability at faint magnitudes rest on an extrapolation, yet the α_OX–L2500 result itself is an independent empirical measurement compared with prior work. No step in the derivation is equivalent to its inputs by construction.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The central claim relies on a small number of chosen thresholds and assumptions about the reliability of public catalogs and the accuracy of photometric redshifts. No new physical entities are introduced. The most consequential free parameters are the Γ_eff thresholds used for X-ray flux conversion and absorption exclusion, since they directly affect the α_OX values and the sample composition.

free parameters (7)
  • p_extragalactic threshold = 0.98
    Chosen from reliability-completeness curves to maximize extragalactic completeness while keeping stellar contamination low.
  • p_quasar threshold = 0.95
    Chosen to balance quasar reliability and completeness on the blind-test sample.
  • i-band magnitude cut for subsample = 22.5
    Selected to maintain a high X-ray detection rate; 1132 quasars remain after this cut.
  • Gamma_eff threshold for X-ray absorption exclusion = 1.26
    Adopted from Pu et al. (2020) to exclude likely absorbed quasars; 116 quasars removed.
  • Radio-loudness threshold R = 10
    Standard criterion (Kellermann et al. 1989); quasars with R>10 classified as radio-loud and excluded.
  • Gamma_eff = 1.7 for soft-band-only detections = 1.7
    Assumed photon index to convert soft-band flux to 2 keV flux; used for 741 detections and upper limits.
  • Gamma_eff = 1.0 for hard-band-only detections = 1.0
    Arbitrary assumption, cautioned in the text; these quasars are excluded from the α_OX relation.
assumptions (5)
  • domain assumption SDSS spectroscopic classifications (quasar, galaxy, star) are correct for the training and blind-test samples.
    The entire supervised learning procedure relies on these labels being accurate. Any mislabeling would propagate into the selected sample.
  • domain assumption The X-ray source catalog and soft-band sensitivity map from Chen et al. (2018) are reliable, including the count rates, flux conversions, and limiting fluxes.
    All X-ray properties, including the censored upper limits, are derived from this catalog and its sensitivity calculations.
  • domain assumption The XGBoost photometric redshifts, with an estimated outlier fraction of about 17%, are accurate enough for the ~669 quasars without secure spectroscopic redshifts so that L2500 and hence the α_OX-L2500 relation are not significantly biased.
    Photometric redshifts are used for a large fraction of the sample; systematic biases in photo-z could distort the luminosity distribution and the correlation slope.
  • standard math The ASURV EM algorithm and the Kaplan-Meier estimator correctly handle censored X-ray data (upper limits) when deriving the regression parameters and dispersion.
    The adopted survival analysis methods are standard in astronomy for censored data, but their validity depends on the assumptions of the underlying model.
  • domain assumption Type 1 quasar spectral energy distributions have colors that are largely independent of luminosity, allowing the XGBoost model trained on SDSS-selected quasars to generalize to fainter quasars (i~23).
    This assumption is cited to Richards et al. (2006) and Krawczyk et al. (2013), and it underpins the expectation that the classifier remains reliable at fainter magnitudes.

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Cite this review

Pith. "Pith review of Photometric Selection of type 1 Quasars in the XMM-LSS Field with Machine Learning and the Disk-Corona Connection." pith.science (2026). https://pith.science/paper/QYRYNLF5

@misc{pith2026241206923,
  author       = {Pith},
  title        = {Pith review of: Photometric Selection of type 1 Quasars in the XMM-LSS Field with Machine Learning and the Disk-Corona Connection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QYRYNLF5}},
  note         = {Machine review of arXiv:2412.06923}
}
abstract

We present photometric selection of type 1 quasars in the $\approx5.3~{\rm deg}^{2}$ XMM-Large Scale Structure (XMM-LSS) survey field with machine learning. We constructed our training and \hbox{blind-test} samples using spectroscopically identified SDSS quasars, galaxies, and stars. We utilized the XGBoost machine learning method to select a total of 1\,591 quasars. We assessed the classification performance based on the blind-test sample, and the outcome was favorable, demonstrating high reliability ($\approx99.9\%$) and good completeness ($\approx87.5\%$). We used XGBoost to estimate photometric redshifts of our selected quasars. The estimated photometric redshifts span a range from 0.41 to 3.75. The outlier fraction of these photometric redshift estimates is $\approx17\%$ and the normalized median absolute deviation ($\sigma_{\rm NMAD}$) is $\approx0.07$. To study the quasar disk-corona connection, we constructed a subsample of 1\,016 quasars with HSC $i<22.5$ after excluding radio-loud and potentially X-ray-absorbed quasars. The relation between the optical-to-X-ray power-law slope parameter ($\alpha_{\rm OX}$) and the 2500 Angstrom monochromatic luminosity ($L_{2500}$) for this subsample is $\alpha_{\rm OX}=(-0.156\pm0.007)~{\rm log}~{L_{\rm 2500}}+(3.175\pm0.211)$ with a dispersion of 0.159. We found this correlation in good agreement with the correlations in previous studies. We explored several factors which may bias the $\alpha_{\rm OX}$-$L_{\rm 2500}$ relation and found that their effects are not significant. We discussed possible evolution of the $\alpha_{\rm OX}$-$L_{\rm 2500}$ relation with respect to $L_{\rm 2500}$ or redshift.

Figures

Figures reproduced from arXiv: 2412.06923 by the authors.

Figure 2
Figure 2. Distribution of the observed i-band magnitude of our parent-sample, training-sample, and blind-test-sample sources. The training and blind-test-sample sources are brighter than the parent-sample sources, which is attributed to the target selection strategies utilized in SDSS spectro￾scopic observations. side the XMM-SERVS XMM-LSS field as our training sample. Our training sample comprises 723 quasars, 2 673 galaxies… view at source ↗
Figure 3
Figure 3. (a) Reliability and completeness of predicted extragalactic objects for the five-fold cross-validation (dark blue points) and blind-test (light green points) results, plot￾ted as a function of pextragalactic. (b) The same as panel (a), but for the predicted quasars from the second classifier. The pextragalactic and pquasar values range from 0.80 to 0.99, with increments of 0.01. The vertical red dashed lines indi￾ca… view at source ↗
Figure 4
Figure 4. The color-morphology diagram of the stars (light-blue contours) and extragalactic objects (green con￾tours) selected from our parent sample. Contours represent source number densities per bin, with a bin size of 0.1 × 0.1 along each axis. For extragalactic objects, the contour levels from the outside to the inside are spaced as 5, 50, and 500 per bin, while for stars, the contour levels are spaced as 10, 100, and 10… view at source ↗
Figures from the paper (7 more)
Figure 7
Figure 7. Figure 7: The MMT spectrum of J022718.28−052613.5 at zspec = 2.573. The black and orange solid curves repre￾sent the flux and its uncertainty, respectively. We corrected the spectrum for the Galactic extinction. We smoothed the spectrum with a Gaussian kernel with a standard dev…
Figure 6
Figure 6. Figure 6 [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 8
Figure 8. Figure 8: The MMT spectra of (a) J022605.98−052241.7 and (b) J022532.17−050545.6, plotted at their photo-zs of 2.34 and 1.04, respectively. There are no apparent broad emission lines in the two spectra. We compared these two spectra to the composite quasar spectrum from Vanden B…
Figure 10
Figure 10. Figure 10: The absolute i-band magnitude at z = 2 vs. redshift for the 1 591 selected quasars in the XMM-SERVS XMM-LSS field. The quasars with spec-zs or photo-zs are represented as the blue stars or the red triangles, respectively. Miller et al. 2011; Zhu et al. 2020). We exclu…
Figure 11
Figure 11. Figure 11: The X-ray detection rate vs. observed i-band magnitude of the 1 441 selected quasars. Data are binned with a step of 1 magnitude, spanning the range from 17.5 to 22.5. The errors are 1σ statistical uncertainties. The detection rate drops as the magnitude increases [P…
Figure 12
Figure 12. Figure 12: The distribution of L2 keV vs. redshifts for the 1 016 subsample of quasars. The 3σ L2 keV upper limits of those quasars with no X-ray detection, calculated from the soft-band sensitivity map, are marked as blue arrows. To obtain the X-ray properties of our selected q…
Figure 13
Figure 13. Figure 13: The distribution of αOX vs. L2500 ˚A values for our 1 016 subsample of quasars. Those quasars with no X-ray detection are marked as arrows. Our αOX–L2500 ˚A relation is represented by the black solid line, while the αOX– L2500 ˚A correlations of Just et al. (2007) and…

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Works this paper leans on

141 extracted references · 9 canonical work pages

  1. [1]

    2018, PASJ, 70, S4, doi: 10.1093/pasj/psx066

    Aihara, H., Arimoto, N., Armstrong, R., et al. 2018, PASJ, 70, S4, doi: 10.1093/pasj/psx066

    doi:10.1093/pasj/psx066 2018

  2. [2]

    2022, PASJ, 74, 247, doi: 10.1093/pasj/psab122

    Aihara, H., AlSayyad, Y., Ando, M., et al. 2022, PASJ, 74, 247, doi: 10.1093/pasj/psab122

    doi:10.1093/pasj/psab122 2022

  3. [3]

    2019, in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2623–2631

    Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. 2019, in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2623–2631

    2019

  4. [4]

    2019, A&A, 628, A135, doi: 10.1051/0004-6361/201935874

    Arcodia, R., Merloni, A., Nandra, K., & Ponti, G. 2019, A&A, 628, A135, doi: 10.1051/0004-6361/201935874

    doi:10.1051/0004-6361/201935874 2019

  5. [5]

    1999, MNRAS, 310, 540, doi: 10.1046/j.1365-8711.1999.02978.x 17 https://github.com/choubalv-hj/AstroML

    Arnouts, S., Cristiani, S., Moscardini, L., et al. 1999, MNRAS, 310, 540, doi: 10.1046/j.1365-8711.1999.02978.x 17 https://github.com/choubalv-hj/AstroML. 18 https://doi.org/10.5281/zenodo.14321895. Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A...

    arXiv 1999

  6. [6]

    1982, ApJL, 262, L17, doi: 10.1086/183903 —

    Avni, Y., & Tananbaum, H. 1982, ApJL, 262, L17, doi: 10.1086/183903 —. 1986, ApJ, 305, 83, doi: 10.1086/164230

    doi:10.1086/183903 1982

  7. [7]

    Blundell, K. M. 2005, ApJ, 618, 108, doi: 10.1086/425859

    doi:10.1086/425859 2005

  8. [8]

    A., Ishida, E

    Beck, R., Lin, C. A., Ishida, E. E. O., et al. 2017, MNRAS, 468, 4323, doi: 10.1093/mnras/stx687

    doi:10.1093/mnras/stx687 2017

Show all 141 references
  1. [9]

    1996, A&AS, 117, 393, doi: 10.1051/aas:1996164 22

    Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393, doi: 10.1051/aas:1996164 22

    1996 doi

  2. [10]

    2017, Frontiers in Astronomy and Space Sciences, 4, 68, doi: 10.3389/fspas.2017.00068

    Bisogni, S., Risaliti, G., & Lusso, E. 2017, Frontiers in Astronomy and Space Sciences, 4, 68, doi: 10.3389/fspas.2017.00068

    2017

  3. [11]

    F., Hogg, D

    Bovy, J., Hennawi, J. F., Hogg, D. W., et al. 2011, ApJ, 729, 141, doi: 10.1088/0004-637X/729/2/141

    2011 doi

  4. [12]

    J., Almaini, O., Hartley, W

    Bradshaw, E. J., Almaini, O., Hartley, W. G., et al. 2013, MNRAS, 433, 194, doi: 10.1093/mnras/stt715

    2013 doi

  5. [13]

    B., van Dokkum, P

    Brammer, G. B., van Dokkum, P. G., & Coppi, P. 2008, ApJ, 686, 1503, doi: 10.1086/591786

    2008 doi

  6. [14]

    N., & Alexander, D

    Brandt, W. N., & Alexander, D. M. 2015, A&A Rv, 23, 1, doi: 10.1007/s00159-014-0081-z

    2015 doi

  7. [15]

    N., & Hasinger, G

    Brandt, W. N., & Hasinger, G. 2005, ARA&A, 43, 827, doi: 10.1146/annurev.astro.43.051804.102213

    2005 arXiv

  8. [16]

    N., Ni, Q., Yang, G., et al

    Brandt, W. N., Ni, Q., Yang, G., et al. 2018, arXiv e-prints, arXiv:1811.06542, doi: 10.48550/arXiv.1811.06542

    2018 doi arXiv

  9. [17]

    2010, ApJ, 716, 348, doi: 10.1088/0004-637X/716/1/348

    Brusa, M., Civano, F., Comastri, A., et al. 2010, ApJ, 716, 348, doi: 10.1088/0004-637X/716/1/348

    2010 doi

  10. [18]

    2024, A&A, 683, A34, doi: 10.1051/0004-6361/202346625

    Calderone, G., Guarneri, F., Porru, M., et al. 2024, A&A, 683, A34, doi: 10.1051/0004-6361/202346625

    2024 doi

  11. [19]

    2023, MNRAS, 518, 3123, doi: 10.1093/mnras/stac3336

    Chaini, S., Bagul, A., Deshpande, A., et al. 2023, MNRAS, 518, 3123, doi: 10.1093/mnras/stac3336

    2023 doi

  12. [20]

    Chen, C. T. J., Brandt, W. N., Luo, B., et al. 2018, MNRAS, 478, 2132, doi: 10.1093/mnras/sty1036

    2018 doi

  13. [21]

    2016, in KDD ’16, Vol

    Chen, T., & Guestrin, C. 2016, in KDD ’16, Vol. 11, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM), 785–794, doi: 10.1145/2939672.2939785

    2016

  14. [22]

    F., Liu, J., et al

    Cheng, H., Liu, B. F., Liu, J., et al. 2020, MNRAS, 495, 1158, doi: 10.1093/mnras/staa1250

    2020 doi

  15. [23]

    2018, A&A, 619, A95, doi: 10.1051/0004-6361/201833631

    Chiaraluce, E., Vagnetti, F., Tombesi, F., & Paolillo, M. 2018, A&A, 619, A95, doi: 10.1051/0004-6361/201833631

    2018 doi

  16. [24]

    L., Blanton, M

    Coil, A. L., Blanton, M. R., Burles, S. M., et al. 2011, ApJ, 741, 8, doi: 10.1088/0004-637X/741/1/8

    2011 doi

  17. [25]

    M., Fan, X., Brandt, W

    Diamond-Stanic, A. M., Fan, X., Brandt, W. N., et al. 2009, ApJ, 699, 782, doi: 10.1088/0004-637X/699/1/782

    2009 doi

  18. [26]

    Falcke, H., Sherwood, W., & Patnaik, A. R. 1996, ApJ, 471, 106, doi: 10.1086/177956

    1996 doi

  19. [27]

    L., Wang, H

    Fan, L. L., Wang, H. Y., Wang, T., et al. 2009, ApJ, 690, 1006, doi: 10.1088/0004-637X/690/1/1006

    2009 doi

  20. [28]

    Clayton, G. C. 2019, ApJ, 886, 108, doi: 10.3847/1538-4357/ab4c3a

    2019 doi

  21. [29]

    N., Zou, F., et al

    Fu, S., Brandt, W. N., Zou, F., et al. 2022, ApJ, 934, 97, doi: 10.3847/1538-4357/ac7a36

    2022 doi

  22. [30]

    2021, ApJS, 254, 6, doi: 10.3847/1538-4365/abe85e

    Fu, Y., Wu, X.-B., Yang, Q., et al. 2021, ApJS, 254, 6, doi: 10.3847/1538-4365/abe85e

    2021 doi

  23. [31]

    2024, ApJS, 271, 54, doi: 10.3847/1538-4365/ad2ae6

    Fu, Y., Wu, X.-B., Li, Y., et al. 2024, ApJS, 271, 54, doi: 10.3847/1538-4365/ad2ae6

    2024 doi

  24. [32]

    C., Brandt, W

    Gallagher, S. C., Brandt, W. N., Chartas, G., & Garmire, G. P. 2002, ApJ, 567, 37, doi: 10.1086/338485

    2002 doi

  25. [33]

    C., Brandt, W

    Gallagher, S. C., Brandt, W. N., Chartas, G., et al. 2006, ApJ, 644, 709, doi: 10.1086/503762

    2006 doi

  26. [34]

    2008, MNRAS, 386, 1417, doi: 10.1111/j.1365-2966.2008.13070.x

    Gao, D., Zhang, Y.-X., & Zhao, Y.-H. 2008, MNRAS, 386, 1417, doi: 10.1111/j.1365-2966.2008.13070.x

    2008

  27. [35]

    2014, A&A, 562, A23, doi: 10.1051/0004-6361/201322790

    Garilli, B., Guzzo, L., Scodeggio, M., et al. 2014, A&A, 562, A23, doi: 10.1051/0004-6361/201322790

    2014 doi

  28. [36]

    1986, ApJ, 303, 336, doi: 10.1086/164079

    Gehrels, N. 1986, ApJ, 303, 336, doi: 10.1086/164079

    1986 doi

  29. [37]

    R., & Brandt, W

    Gibson, R. R., & Brandt, W. N. 2012, ApJ, 746, 54, doi: 10.1088/0004-637X/746/1/54

    2012 doi

  30. [38]

    Schneider, D. P. 2009, ApJ, 696, 924, doi: 10.1088/0004-637X/696/1/924

    2009 doi

  31. [39]

    R., Brandt, W

    Gibson, R. R., Brandt, W. N., & Schneider, D. P. 2008, ApJ, 685, 773, doi: 10.1086/590403

    2008 doi

  32. [40]

    D., & Coupon, J

    Golob, A., Sawicki, M., Goulding, A. D., & Coupon, J. 2021, MNRAS, 503, 4136, doi: 10.1093/mnras/stab719

    2021 doi

  33. [41]

    D., Clayton, G

    Gordon, K. D., Clayton, G. C., Misselt, K. A., Landolt, A. U., & Wolff, M. J. 2003, ApJ, 594, 279, doi: 10.1086/376774

    2003 doi

  34. [42]

    2018, The Journal of Open Source Software, 3, 695

    Green, G. 2018, The Journal of Open Source Software, 3, 695

    2018

  35. [43]

    J., Aldcroft, T

    Green, P. J., Aldcroft, T. L., Richards, G. T., et al. 2009, ApJ, 690, 644, doi: 10.1088/0004-637X/690/1/644

    2009 doi

  36. [44]

    2022, arXiv e-prints, arXiv:2206.14908, doi: 10.48550/arXiv.2206.14908

    Greene, J., Bezanson, R., Ouchi, M., Silverman, J., & the PFS Galaxy Evolution Working Group. 2022, arXiv e-prints, arXiv:2206.14908, doi: 10.48550/arXiv.2206.14908

    2022 doi arXiv

  37. [45]

    M., & Page, K

    Grupe, D., Komossa, S., Leighly, K. M., & Page, K. L. 2010, ApJS, 187, 64, doi: 10.1088/0067-0049/187/1/64

    2010 doi

  38. [46]

    B., Gallagher, S

    Hall, P. B., Gallagher, S. C., Richards, G. T., et al. 2006, AJ, 132, 1977, doi: 10.1086/507842

    2006 doi

  39. [47]

    L., Jarvis, M

    Heywood, I., Hale, C. L., Jarvis, M. J., et al. 2020, MNRAS, 496, 3469, doi: 10.1093/mnras/staa1770

    2020 doi

  40. [48]

    J., Hale, C

    Heywood, I., Jarvis, M. J., Hale, C. L., et al. 2022, MNRAS, 509, 2150, doi: 10.1093/mnras/stab3021 HI4PI Collaboration, Ben Bekhti, N., Fl¨ oer, L., et al. 2016, A&A, 594, A116, doi: 10.1051/0004-6361/201629178

    2022 doi

  41. [49]

    2010, A&A, 523, A31, doi: 10.1051/0004-6361/201014885

    Hildebrandt, H., Arnouts, S., Capak, P., et al. 2010, A&A, 523, A31, doi: 10.1051/0004-6361/201014885

    2010 doi

  42. [50]

    N., et al

    Huang, J., Luo, B., Brandt, W. N., et al. 2023, ApJ, 950, 18, doi: 10.3847/1538-4357/accd64

    2023 doi

  43. [51]

    C., Withington, K., et al

    Hudelot, P., Cuillandre, J. C., Withington, K., et al. 2012, VizieR Online Data Catalog, II/317

    2012

  44. [52]

    Hughes, A. C. N., Bailer-Jones, C. A. L., & Jamal, S. 2022, A&A, 668, A99, doi: 10.1051/0004-6361/202244859

    2022 doi

  45. [53]

    J., et al

    Ilbert, O., Arnouts, S., McCracken, H. J., et al. 2006, A&A, 457, 841, doi: 10.1051/0004-6361:20065138

    2006 doi

  46. [54]

    Isobe, T., & Feigelson, E. D. 1990, in Bulletin of the American Astronomical Society, Vol. 22, 917–918 23

    1990

  47. [55]

    J., Bonfield, D

    Jarvis, M. J., Bonfield, D. G., Bruce, V. A., et al. 2013, MNRAS, 428, 1281, doi: 10.1093/mnras/sts118

    2013 doi

  48. [56]

    2007, ApJ, 656, 680, doi: 10.1086/510831

    Jiang, L., Fan, X., Ivezi´ c,ˇZ., et al. 2007, ApJ, 656, 680, doi: 10.1086/510831

    2007 doi

  49. [57]

    2024, MNRAS, 527, 356, doi: 10.1093/mnras/stad3193

    Jin, C., Lusso, E., Ward, M., Done, C., & Middei, R. 2024, MNRAS, 527, 356, doi: 10.1093/mnras/stad3193

    2024 doi

  50. [59]

    2019, MNRAS, 485, 4539, doi: 10.1093/mnras/stz680

    Jin, X., Zhang, Y., Zhang, J., et al. 2019, MNRAS, 485, 4539, doi: 10.1093/mnras/stz680

    2019 doi

  51. [60]

    W., Brandt, W

    Just, D. W., Brandt, W. N., Shemmer, O., et al. 2007, ApJ, 665, 1004, doi: 10.1086/519990

    2007 doi

  52. [61]

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

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

    1989 doi

  53. [62]

    M., Richards, G

    Krawczyk, C. M., Richards, G. T., Mehta, S. S., et al. 2013, ApJS, 206, 4, doi: 10.1088/0067-0049/206/1/4

    2013 doi

  54. [63]

    A., & Canizares, C

    Kriss, G. A., & Canizares, C. R. 1985, ApJ, 297, 177, doi: 10.1086/163514

    1985 doi

  55. [64]

    2018, MNRAS, 480, 1247, doi: 10.1093/mnras/sty1890

    Kubota, A., & Done, C. 2018, MNRAS, 480, 1247, doi: 10.1093/mnras/sty1890

    2018 doi

  56. [65]

    A., Farrah, D., et al

    Lacy, M., Surace, J. A., Farrah, D., et al. 2020, DeepDrill XMM-LSS 2-band Catalog, the Infrared Science Archive (IRSA), doi: 10.26131/IRSA500 —. 2021, MNRAS, 501, 892, doi: 10.1093/mnras/staa3714

    2020 doi

  57. [66]

    Laha, S., Ghosh, R., Guainazzi, M., & Markowitz, A. G. 2018, MNRAS, 480, 1522, doi: 10.1093/mnras/sty1919

    2018 doi

  58. [67]

    Laor, A., & Davis, S. W. 2011, MNRAS, 417, 681, doi: 10.1111/j.1365-2966.2011.19310.x

    2011

  59. [68]

    1992, in Astronomical Society of the Pacific Conference Series, Vol

    Lavalley, M., Isobe, T., & Feigelson, E. 1992, in Astronomical Society of the Pacific Conference Series, Vol. 25, Astronomical Data Analysis Software and Systems I, ed. D. M. Worrall, C. Biemesderfer, & J. Barnes, 245 Le F` evre, O., Cassata, P., Cucciati, O., et al. 2013, A&A...

    1992 doi

  60. [69]

    2022, MNRAS, 509, 2289, doi: 10.1093/mnras/stab3165

    Li, C., Zhang, Y., Cui, C., et al. 2022, MNRAS, 509, 2289, doi: 10.1093/mnras/stab3165

    2022 doi

  61. [70]

    N., et al

    Liu, H., Luo, B., Brandt, W. N., et al. 2021, ApJ, 910, 103, doi: 10.3847/1538-4357/abe37f —. 2022, ApJ, 930, 53, doi: 10.3847/1538-4357/ac6265 —. 2019, ApJ, 878, 79, doi: 10.3847/1538-4357/ab1d5b

    2021 doi

  62. [71]

    N., Xue, Y

    Luo, B., Brandt, W. N., Xue, Y. Q., et al. 2010, ApJS, 187, 560, doi: 10.1088/0067-0049/187/2/560

    2010 doi

  63. [72]

    N., Hall, P

    Luo, B., Brandt, W. N., Hall, P. B., et al. 2015, ApJ, 805, 122, doi: 10.1088/0004-637X/805/2/122

    2015 doi

  64. [73]

    2016, ApJ, 819, 154, doi: 10.3847/0004-637X/819/2/154 —

    Lusso, E., & Risaliti, G. 2016, ApJ, 819, 154, doi: 10.3847/0004-637X/819/2/154 —. 2017, A&A, 602, A79, doi: 10.1051/0004-6361/201630079

    2016 doi

  65. [74]

    2010, A&A, 512, A34, doi: 10.1051/0004-6361/200913298

    Lusso, E., Comastri, A., Vignali, C., et al. 2010, A&A, 512, A34, doi: 10.1051/0004-6361/200913298

    2010 doi

  66. [75]

    2020, A&A, 642, A150, doi: 10.1051/0004-6361/202038899

    Lusso, E., Risaliti, G., Nardini, E., et al. 2020, A&A, 642, A150, doi: 10.1051/0004-6361/202038899

    2020 doi

  67. [76]

    W., Higley, A

    Lyke, B. W., Higley, A. N., McLane, J. N., et al. 2020, ApJS, 250, 8, doi: 10.3847/1538-4365/aba623

    2020 doi

  68. [77]

    L., Ivezi´ c,ˇZ., Kochanek, C

    MacLeod, C. L., Ivezi´ c,ˇZ., Kochanek, C. S., et al. 2010, ApJ, 721, 1014, doi: 10.1088/0004-637X/721/2/1014

    2010 doi

  69. [78]

    2020, The Messenger, 180, 24, doi: 10.18727/0722-6691/5197

    Maiolino, R., Cirasuolo, M., Afonso, J., et al. 2020, The Messenger, 180, 24, doi: 10.18727/0722-6691/5197

    2020 doi

  70. [79]

    2016, ApJ, 817, 34, doi: 10.3847/0004-637X/817/1/34

    Marchesi, S., Civano, F., Elvis, M., et al. 2016, ApJ, 817, 34, doi: 10.3847/0004-637X/817/1/34

    2016 doi

  71. [80]

    C., Fanson, J., Schiminovich, D., et al

    Martin, D. C., Fanson, J., Schiminovich, D., et al. 2005, ApJL, 619, L1, doi: 10.1086/426387 Mart ´ ınez-Solaeche, G., Queiroz, C., Gonz´ alez Delgado, R. M., et al. 2023, A&A, 673, A103, doi: 10.1051/0004-6361/202245750

    2005 doi

  72. [81]

    Fender, R. P. 2006, Nature, 444, 730, doi: 10.1038/nature05389

    2006 doi

  73. [82]

    J., Pearce, H

    McLure, R. J., Pearce, H. J., Dunlop, J. S., et al. 2013, MNRAS, 428, 1088, doi: 10.1093/mnras/sts092

    2013 doi

  74. [83]

    P., Brandt, W

    Miller, B. P., Brandt, W. N., Schneider, D. P., et al. 2011, ApJ, 726, 20, doi: 10.1088/0004-637X/726/1/20

    2011 doi

  75. [85]

    C., et al

    Mirabal, N., Charles, E., Ferrara, E. C., et al. 2016, ApJ, 825, 69, doi: 10.3847/0004-637X/825/1/69

    2016 doi

  76. [86]

    2018, PASJ, 70, S1, doi: 10.1093/pasj/psx063

    Miyazaki, S., Komiyama, Y., Kawanomoto, S., et al. 2018, PASJ, 70, S1, doi: 10.1093/pasj/psx063

    2018 doi

  77. [87]

    G., Brammer, G

    Momcheva, I. G., Brammer, G. B., van Dokkum, P. G., et al. 2016, ApJS, 225, 27, doi: 10.3847/0067-0049/225/2/27

    2016 doi

  78. [88]

    N., Luo, B., et al

    Ni, Q., Brandt, W. N., Luo, B., et al. 2018, MNRAS, 480, 5184, doi: 10.1093/mnras/sty1989

    2018 doi

  79. [89]

    N., Yi, W., et al

    Ni, Q., Brandt, W. N., Yi, W., et al. 2020, ApJL, 889, L37, doi: 10.3847/2041-8213/ab6d78

    2020 doi

  80. [90]

    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

    2021 doi

  81. [91]

    N., Luo, B., et al

    Ni, Q., Brandt, W. N., Luo, B., et al. 2022, MNRAS, 511, 5251, doi: 10.1093/mnras/stac394

    2022 doi

  82. [92]

    2011, Journal of Machine Learning Research, 12, 2825

    Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, Journal of Machine Learning Research, 12, 2825

    2011

  83. [93]

    2012, MNRAS, 425, 2599, doi: 10.1111/j.1365-2966.2012.21191.x Planck Collaboration, Aghanim, N., Akrami, Y., et al

    Peng, N., Zhang, Y., Zhao, Y., & Wu, X.-b. 2012, MNRAS, 425, 2599, doi: 10.1111/j.1365-2966.2012.21191.x Planck Collaboration, Aghanim, N., Akrami, Y., et al. 2020, A&A, 641, A6, doi: 10.1051/0004-6361/201833910

    2012

  84. [94]

    M., Anderson, S

    Plotkin, R. M., Anderson, S. F., Brandt, W. N., et al. 2010, ApJ, 721, 562, doi: 10.1088/0004-637X/721/1/562 24

    2010 doi

  85. [95]

    N., et al

    Pu, X., Luo, B., Brandt, W. N., et al. 2020, ApJ, 900, 141, doi: 10.3847/1538-4357/abacc5

    2020 doi

  86. [96]

    L., Aird, J., Ruiz, A., & Georgakakis, A

    Rankine, A. L., Aird, J., Ruiz, A., & Georgakakis, A. 2024, MNRAS, 527, 9004, doi: 10.1093/mnras/stad3686

    2024 doi

  87. [97]

    T., Nichol, R

    Richards, G. T., Nichol, R. C., Gray, A. G., et al. 2004, ApJS, 155, 257, doi: 10.1086/425356

    2004 doi

  88. [98]

    T., Strauss, M

    Richards, G. T., Strauss, M. A., Fan, X., et al. 2006, AJ, 131, 2766, doi: 10.1086/503559

    2006 doi

  89. [99]

    R., Webb, N

    Rosen, S. R., Webb, N. A., Watson, M. G., et al. 2016, A&A, 590, A1, doi: 10.1051/0004-6361/201526416

    2016 doi

  90. [100]

    2019, Nature Astronomy, 3, 212, doi: 10.1038/s41550-018-0478-0

    Salvato, M., Ilbert, O., & Hoyle, B. 2019, Nature Astronomy, 3, 212, doi: 10.1038/s41550-018-0478-0

    2019 doi

  91. [101]

    2009, ApJ, 690, 1250, doi: 10.1088/0004-637X/690/2/1250 S´ anchez, C., Carrasco Kind, M., Lin, H., et al

    Salvato, M., Hasinger, G., Ilbert, O., et al. 2009, ApJ, 690, 1250, doi: 10.1088/0004-637X/690/2/1250 S´ anchez, C., Carrasco Kind, M., Lin, H., et al. 2014, MNRAS, 445, 1482, doi: 10.1093/mnras/stu1836

    2009 doi

  92. [102]

    2019, MNRAS, 489, 5202, doi: 10.1093/mnras/stz2522

    Sawicki, M., Arnouts, S., Huang, J., et al. 2019, MNRAS, 489, 5202, doi: 10.1093/mnras/stz2522

    2019 doi

  93. [103]

    J., Finkbeiner, D

    Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, ApJ, 500, 525, doi: 10.1086/305772

    1998 doi

  94. [104]

    Schmidt, M., & Green, R. F. 1983, ApJ, 269, 352, doi: 10.1086/161048

    1983 doi

  95. [105]

    J., Malz, A

    Schmidt, S. J., Malz, A. I., Soo, J. Y. H., et al. 2020, MNRAS, 499, 1587, doi: 10.1093/mnras/staa2799

    2020 doi

  96. [106]

    M., Lochner, M., Gris, P., et al

    Scolnic, D. M., Lochner, M., Gris, P., et al. 2018, arXiv e-prints, arXiv:1812.00516, doi: 10.48550/arXiv.1812.00516

    2018 doi arXiv

  97. [107]

    N., Anderson, S

    Shemmer, O., Brandt, W. N., Anderson, S. F., et al. 2009, ApJ, 696, 580, doi: 10.1088/0004-637X/696/1/580

    2009 doi

  98. [108]

    2008, ApJ, 682, 81, doi: 10.1086/588776

    Kaspi, S. 2008, ApJ, 682, 81, doi: 10.1086/588776

    2008 doi

  99. [109]

    T., Strauss, M

    Shen, Y., Richards, G. T., Strauss, M. A., et al. 2011, ApJS, 194, 45, doi: 10.1088/0067-0049/194/2/45

    2011 doi

  100. [110]

    2023, A&A, 676, A143, doi: 10.1051/0004-6361/202346104

    Signorini, M., Risaliti, G., Lusso, E., et al. 2023, A&A, 676, A143, doi: 10.1051/0004-6361/202346104

    2023 doi

  101. [111]

    E., Whitaker, K

    Skelton, R. E., Whitaker, K. E., Momcheva, I. G., et al. 2014, ApJS, 214, 24, doi: 10.1088/0067-0049/214/2/24

    2014 doi

  102. [112]

    T., Strateva, I., Brandt, W

    Steffen, A. T., Strateva, I., Brandt, W. N., et al. 2006, AJ, 131, 2826, doi: 10.1086/503627

    2006 doi

  103. [113]

    G., & Vignali, C

    Berk, D. G., & Vignali, C. 2005, AJ, 130, 387, doi: 10.1086/431247 Str¨ uder, L., Briel, U., Dennerl, K., et al. 2001, A&A, 365, L18, doi: 10.1051/0004-6361:20000066

    2005 doi

  104. [114]

    2019, The Messenger, 175, 58, doi: 10.18727/0722-6691/5129

    Swann, E., Sullivan, M., Carrick, J., et al. 2019, The Messenger, 175, 58, doi: 10.18727/0722-6691/5129

    2019 doi

  105. [115]

    2018, PASJ, 70, S9, doi: 10.1093/pasj/psx077

    Tanaka, M., Coupon, J., Hsieh, B.-C., et al. 2018, PASJ, 70, S9, doi: 10.1093/pasj/psx077

    2018 doi

  106. [116]

    1979, ApJL, 234, L9, doi: 10.1086/183100

    Tananbaum, H., Avni, Y., Branduardi, G., et al. 1979, ApJL, 234, L9, doi: 10.1086/183100

    1979 doi

  107. [117]

    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

    2005

  108. [118]

    N., Zhu, S., et al

    Timlin, John D., I., Brandt, W. N., Zhu, S., et al. 2020a, MNRAS, 498, 4033, doi: 10.1093/mnras/staa2661

    doi

  109. [119]

    D., Brandt, W

    Timlin, J. D., Brandt, W. N., Ni, Q., et al. 2020b, MNRAS, 492, 719, doi: 10.1093/mnras/stz3433

    doi

  110. [120]

    D., Lamer, G., et al

    Traulsen, I., Schwope, A. D., Lamer, G., et al. 2020, A&A, 641, A137, doi: 10.1051/0004-6361/202037706

    2020 doi

  111. [121]

    I., & Benn, C

    Tuccillo, D., Gonz´ alez-Serrano, J. I., & Benn, C. R. 2015, MNRAS, 449, 2818, doi: 10.1093/mnras/stv472

    2015 doi

  112. [122]

    2013, A&A, 550, A71, doi: 10.1051/0004-6361/201220443

    Vagnetti, F., Antonucci, M., & Trevese, D. 2013, A&A, 550, A71, doi: 10.1051/0004-6361/201220443

    2013 doi

  113. [123]

    2010, A&A, 519, A17, doi: 10.1051/0004-6361/201014320 Vanden Berk, D

    Vagnetti, F., Turriziani, S., Trevese, D., & Antonucci, M. 2010, A&A, 519, A17, doi: 10.1051/0004-6361/201014320 Vanden Berk, D. E., Richards, G. T., Bauer, A., et al. 2001, AJ, 122, 549, doi: 10.1086/321167

    2010 doi

  114. [124]

    N., & Schneider, D

    Vignali, C., Brandt, W. N., & Schneider, D. P. 2003, AJ, 125, 433, doi: 10.1086/345973

    2003 doi

  115. [125]

    N., Schneider, D

    Vignali, C., Brandt, W. N., Schneider, D. P., & Kaspi, S. 2005, AJ, 129, 2519, doi: 10.1086/430217

    2005 doi

  116. [126]

    Walter, R., & Fink, H. H. 1993, A&A, 274, 105

    1993

  117. [127]

    N., et al

    Wang, C., Luo, B., Brandt, W. N., et al. 2022, ApJ, 936, 95, doi: 10.3847/1538-4357/ac886e

    2022 doi

  118. [128]

    N., Luo, B., et al

    Wang, S., Brandt, W. N., Luo, B., et al. 2024, ApJ, 974, 2, doi: 10.3847/1538-4357/ad7589

    2024 doi

  119. [129]

    A., Coriat, M., Traulsen, I., et al

    Webb, N. A., Coriat, M., Traulsen, I., et al. 2020, A&A, 641, A136, doi: 10.1051/0004-6361/201937353

    2020 doi

  120. [130]

    J., Tananbaum, H., Worrall, D

    Wilkes, B. J., Tananbaum, H., Worrall, D. M., et al. 1994, ApJS, 92, 53, doi: 10.1086/191959

    1994 doi

  121. [131]

    2004, A&A, 421, 913, doi: 10.1051/0004-6361:20040525

    Wolf, C., Meisenheimer, K., Kleinheinrich, M., et al. 2004, A&A, 421, 913, doi: 10.1051/0004-6361:20040525

    2004 doi

  122. [132]

    N., Anderson, S

    Wu, J., Brandt, W. N., Anderson, S. F., et al. 2012, ApJ, 747, 10, doi: 10.1088/0004-637X/747/1/10

    2012 doi

  123. [133]

    Xue, Y. Q. 2017, NewAR, 79, 59, doi: 10.1016/j.newar.2017.09.002

    2017 doi

  124. [134]

    N., Luo, B., et al

    Yang, G., Brandt, W. N., Luo, B., et al. 2016, ApJ, 831, 145, doi: 10.3847/0004-637X/831/2/145

    2016 doi

  125. [135]

    2023, ApJS, 264, 9, doi: 10.3847/1538-4365/ac9ea8

    Yang, Q., & Shen, Y. 2023, ApJS, 264, 9, doi: 10.3847/1538-4365/ac9ea8

    2023 doi

  126. [136]

    2017, AJ, 154, 269, doi: 10.3847/1538-3881/aa943c Y` eche, C., Petitjean, P., Rich, J., et al

    Yang, Q., Wu, X.-B., Fan, X., et al. 2017, AJ, 154, 269, doi: 10.3847/1538-3881/aa943c Y` eche, C., Petitjean, P., Rich, J., et al. 2010, A&A, 523, A14, doi: 10.1051/0004-6361/200913508

    2017 doi

  127. [137]

    2010, ApJ, 708, 1388, doi: 10.1088/0004-637X/708/2/1388

    Young, M., Elvis, M., & Risaliti, G. 2010, ApJ, 708, 1388, doi: 10.1088/0004-637X/708/2/1388

    2010 doi

  128. [138]

    2011, Scientia Sinica Physica, Mechanica & Astronomica, 41, 1441, doi: 10.1360/132011-961 25

    Zhan, H. 2011, Scientia Sinica Physica, Mechanica & Astronomica, 41, 1441, doi: 10.1360/132011-961 25

    2011 doi

  129. [139]

    N., et al

    Zhang, Z., Luo, B., Brandt, W. N., et al. 2023, ApJ, 954, 159, doi: 10.3847/1538-4357/ace7c2

    2023 doi

  130. [140]

    C., Xue, Y

    Zheng, X. C., Xue, Y. Q., Brandt, W. N., et al. 2017, ApJ, 849, 127, doi: 10.3847/1538-4357/aa9378

    2017 doi

  131. [141]

    N., Zou, F., et al

    Zhu, S., Brandt, W. N., Zou, F., et al. 2023, MNRAS, 522, 3506, doi: 10.1093/mnras/stad1178

    2023 doi

  132. [142]

    F., Brandt, W

    Zhu, S. F., Brandt, W. N., Luo, B., et al. 2020, MNRAS, 496, 245, doi: 10.1093/mnras/staa1411

    2020 doi

  133. [143]

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

    Zou, F., Brandt, W. N., Chen, C.-T., et al. 2022, ApJS, 262, 15, doi: 10.3847/1538-4365/ac7bdf

    2022 doi

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