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An Exploratory Analysis of New Large Gaia-informed Quasar Samples in SDSS-V

T0 review · 3 major / 6 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read Two color-and-astrometry recipes pick out quasars with more than 96 percent purity.

desk verdict Solid, transparent survey validation with a genuinely useful southern quasar catalog; the purity claims are credible but the exact counts rest on an extrapolation that needs quantification. read the letter →

arxiv 2607.27329 v1 pith:UBUN2A37 submitted 2026-07-29 astro-ph.GA

Shir Aviram , Benny Trakhtenbrot , Tom Dwelly , Scott F. Anderson , Sean Morrison , Michael Eracleous , Yue Shen , Mara Salvato
show 17 more authors
Donald P. Schneider Roberto J. Assef Catarina Aydar Franz E. Bauer W.N. Brandt Joel R. Brownstein Johannes Buchner Jeremy Darling Jose G. Fernandez-Trincado Patrick B. Hall Dong-Woo Kim Anton M. Koekemoer Stephanie LaMassa Andrea Merloni Claudio Ricci Qian Yang Grisha Zeltyn
This is my paper · ORCID
classification astro-ph.GA
keywords quasarsactivegalacticnucleisupermassiveblackholesquasarselectionGaiaastrometryWISEmid-infraredcolorsSDSS-Vspectroscopysouthernskysurveys
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 argues that large, high-purity quasar samples can be built from just two all-sky catalogs—Gaia's optical photometry and astrometry plus WISE mid-infrared colors—without X-ray or radio preselection. It reports that two such selection methods, run as non-core programs inside SDSS-V, delivered over 250,000 spectroscopically confirmed quasars (reaching z~5), of which about 151,972 are newly confirmed, most in the southern sky that past surveys undersampled. The measured quasar purity exceeds 96% for both methods, with conservative lower limits above 82%, and remains high even at low Galactic latitudes and faint magnitudes. If the result holds, future surveys can cheaply assemble wide-area quasar samples for black-hole demographics, time-domain studies, and southern-hemisphere cosmology.

What carries the argument

The load-bearing machinery is the pair of candidate-selection algorithms. GUA (Gaia-unWISE AGN) uses a random forest trained on previously confirmed quasars to score every Gaia-DR2/unWISE matched source, combining Gaia G/BP/RP photometry and proper motion with WISE W1-W2 mid-IR colors, and keeps sources with probability >0.8. Skewt-QSO uses parametric skew-t distributions in the multi-band color space of DES optical + near-IR + WISE data, requiring P_QSO > P_star and P_QSO > P_galaxy, and likewise rejects proper-motion stars. Both feed SDSS-V's fiber-fed spectroscopic survey, where overlapping target cartons and a visual-inspection subsample are used to measure purity. What the machinery doe

What would settle it

Visually inspect a few thousand randomly chosen 'good' spectra that the authors did not examine; if the fraction of misclassified stars/galaxies exceeds the ~1.4% found in the inspected subsample — or if a substantial share of the uninspected 'bad' spectra are not quasars — the >96% purity and 151,972 new-quasar totals are overestimates. Alternatively, check whether quasar fraction varies by redshift or S/N among uninspected spectra; the claimed purity assumes it does not.

Watch

Extended reading notes

Core claim

The central empirical claim is that the GUA and Skewt-QSO candidate selections, implemented in SDSS-V as non-core spectroscopic programs, achieve exceptionally high quasar purity: 96.2% for GUA and 97.4% for Skewt-QSO in a homogenized southern test area, with good-only lower limits of 82.0% and 88.9%. In doing so, they produced 151,972 newly spectroscopically confirmed quasars (76,840 of them selected only by these programs), more than half in the southern celestial hemisphere, with redshifts continuously spanning ~0.001 to ~5.3. The authors also show, through spectral decomposition, that the resulting quasars span Lbol ~1e44-1e48 erg/s, MBH ~1e6-1e10 Msun, and Eddington ratios ~0.01-1, and

Load-bearing premise

The purity and new-quasar counts treat the BOSS pipeline's classifications as ground truth for the ~200,000 spectra that were never visually inspected; the checked 23,986 spectra may not represent the contamination rate in the rest, especially for BAL or reddened quasars with wrong redshifts.

Editorial extensions

If this is right

  • The southern-hemisphere quasar census expands dramatically: more than half of the newly confirmed quasars lie at δ<0, where only ~13% of previously known quasars resided.
  • Quasar samples with >90% purity can be selected from public all-sky photometry and astrometry, so future surveys (optical or time-domain) can target quasars without a prior X-ray/radio catalog.
  • The selection remains pure at low Galactic latitudes (≳80% down to |b|≲5° for GUA), opening the Galactic plane region to quasar demographics.
  • Because the samples probe redder optical/IR colors than legacy SDSS color selection, they recover a substantial fraction (≈26%) of R−W1>4 mildly obscured quasars, while the redshift distribution shows no gap in the troublesome z≈2.1–2.8 range.
  • The spectral-property distributions (Lbol, MBH, L/LEdd) of these quasars match previous SDSS catalogs once selection differences are accounted for, so the larger sky coverage does not introduce a new population of accreting black holes.

Reading between the lines

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

  • If purity holds at fainter magnitudes, extending these recipes with deeper optical surveys and the improved proper motions of later Gaia releases would yield a deeper all-sky quasar catalog; the paper's own magnitude trend suggests the faint end (G≳20) is where contamination begins.
  • The paper demonstrates purity, not completeness: because both training sets were built from SDSS color-selected quasars, heavily reddened or broad-absorption-line quasars are likely underrepresented; a testable prediction is that a dedicated red/obscured quasar search would find a larger fraction among the W1−W2<0.8 tail of the sample.
  • Cross-matching the new southern quasars with eROSITA X-ray data would quantify how many X-ray-faint or X-ray-undetected quasars these color methods recover, directly measuring the added value beyond X-ray preselection.
  • The ~50,000 'bad' spectra retained as quasars add statistical weight but their redshifts are unreliable; redshift-corrected versions of these spectra could refine the high-z tail and the BAL population.
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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

3 major / 6 minor

Summary. The paper presents an exploratory analysis of two SDSS-V ancillary quasar programs, GUA and ASQOSS/Skewt-QSO, which use Gaia+WISE and DES-based photometric/astrometric selection rather than X-ray/radio preselection. The authors construct a sample of 259,700 spectroscopically classified quasars from SDSS-V/DR20, report 151,972 newly confirmed quasars, and measure homogenized selection purities of 96.2% (GUA) and 97.4% (Skewt-QSO), with conservative good-only lower limits of 82.0% and 88.9%. They also derive Lbol, MBH, and L/LEdd for ~208k objects using PyQSOFit and compare the resulting distributions with SDSS/DR7Q and DR16Q, concluding that the physical properties are broadly consistent once selection differences are accounted for.

Significance. If the purity and sample-size claims hold, the paper provides strong empirical evidence that inexpensive Gaia+WISE or DES-based selection can deliver large, high-purity quasar samples over wide sky areas, including the southern hemisphere and low Galactic latitudes. This is of high practical value for current and future wide-field surveys. The manuscript is transparent in its use of public DR20 data, explicitly reports conservative good-only purity lower limits, cross-matches against independent compilations (Milliquas/v8, DESI/DR1), and describes the training-set limitations of the parent selection methods. These are genuine strengths. The main risk is that the headline numbers rest on an unquantified extrapolation from a modest visual-inspection subset to the full sample, and on an area-specific homogenization procedure.

major comments (3)
  1. [Sec. 3.1, Fig. 9, Table 1] The headline purity and new-quasar numbers rest on an extrapolation that is not quantified. Visual inspection covered 23,986 pipeline-QSO spectra with ZWARNING=0 and S/N>2, but the 'good' sample contains 213,528 objects and the 'bad' sample adds 50,672 objects; the contamination rate measured in the inspected subset is applied to the uninspected majority. For the bad sample, the paper states only that ~83% of visually inspected bad pipeline-QSOs show broad lines, without giving the number inspected, the selection rule, or the fate of the remaining ~17%. Since bad spectra are included in the numerator of hp and in the 151,972 new-quasar count, a moderate overcount among bad spectra would directly inflate both headline numbers. The paper itself notes in Sec. 3.4 that non-extreme spectra were not visually inspected. Please report the inspection N and selection strategy, propagate the result
  2. [Appendix A, Eqs. (A1)-(A2); Sec. 4.2] The homogenized purity hp is calculated in a single high-Galactic-latitude southern test area (2h<RA<5h20m, -60<Dec<-20), using target-pool fractions fA...fD from that area, and then quoted as 96.2%/97.4% for GUA/Skewt-QSO. The all-sky claim relies on separate, non-homogenized GUA curves in Fig. 10 rather than on the headline hp. Because the weights in Eqs. (A1)-(A2) are area-specific, the quoted values are not all-sky purities. Please either restrict the abstract and conclusions to the test area or provide an error budget for the spatial extrapolation, e.g., by repeating the hp calculation in other regions where the targeting paths overlap.
  3. [Sec. 4.4, Figs. 18-19] The conclusion that the new samples are consistent with SDSS/DR16Q and DR7Q in Lbol, MBH, and L/LEdd is weaker than stated because the comparison uses the same PyQSOFit configuration, the same single-epoch scaling relations, and the same bolometric corrections used in the reference catalog (Secs. 3.2-3.3). The AD test formally rejects identity (P_AD << 10^-10); the visual agreement is partly inherited from the shared measurement recipe. Please state explicitly that the test only probes selection-induced differences conditional on identical measurement assumptions, or validate on a subset with an independent pipeline.
minor comments (6)
  1. [Sec. 3.1] The 'good' sample definition is given as ZWARNING=0, ZERR>0, relative redshift error <5%, z>0, but the visual-inspection subset described in the same section additionally requires S/N>2. Clarify whether S/N is part of the sample selection or only of the verification subset.
  2. [Table 1] The column headers N_tot, N_GUA, N_Skewt_QSO, etc. are difficult to parse, and the relationship between rows (e.g., 275,287 total observed vs. 259,700 quasars-only) is not immediately transparent. Expand the table note or use a cleaner layout so the numbers can be followed without the main text.
  3. [Figures 3 and 4] The bottom axis is labeled 'Rest Wavelength' in both figures, but the plotted x-axis is observed wavelength in each panel. Re-label to avoid confusion.
  4. [Sec. 4.3.1] The 'red quasar' sub-sample is defined with 'g−1>1' where presumably 'g−r>1' is intended; fix the typo.
  5. [Sec. 4.2] The text states hp = 96.1% for GUA in one sentence, while Fig. 9 and elsewhere quote 96.2%. Make the values consistent throughout.
  6. [Sec. 4.1 vs. Sec. 5] The main text reports 151,972 newly confirmed quasars, while the conclusions cite about 76,000 newly identified sources. The distinction between the total new-quasar count and the 'GUA+Skewt QSO sole-selected' subset should be stated clearly in both places to avoid apparent inconsistency.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: purity and new-quasar counts are empirically measured against observed spectra and external catalogs; shared spectral-fitting calibration is transparent and not a fitted prediction.

full rationale

The paper's central claims—GUA/Skewt-QSO purity of 96.2%/97.4%, conservative good-only lower limits, and 151,972 newly confirmed quasars—are measured from BOSS spectra and cross-matched against the external Milliquas/v8+DESI/DR1 catalogs. The selection methods (Shu et al. 2019; Yang & Shen 2023) are prior, independent works; this paper tests their empirical yield in SDSS-V rather than deriving the yield from the selection criteria by construction. The homogenized purity calculation (Eqs. A1/A2) combines target fractions and observed quasar fractions in disjoint subsets; it is a measurement, not a self-referential definition. The physical-property comparison to DR16Q does adopt the same PyQSOFit configuration and the same bolometric corrections and virial calibrations as Wu & Shen (2022), which makes the Lbol/MBH/L_Edd consistency partly inherited from shared methodology. However, the paper presents this as a transparent comparison using standard, code-reproduced calibrations, not as a first-principles prediction; the FWHM and luminosity measurements come from the new SDSS-V spectra, and no fitted parameter is renamed as a prediction. The extrapolation from visually inspected subsets to uninspected spectra is a statistical-robustness concern, not circularity. No equation reduces to its inputs and no load-bearing argument rests on an unverified self-citation, so the analysis finds no significant circularity.

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

No physically invented entities. The central empirical claims (purity, new-quasar counts) depend mainly on survey data and pipeline products; the derived physical-property distributions depend on adopted literature calibrations and on the completeness of prior catalogs used as reference.

free parameters (4)
  • Bolometric corrections fbol (L5100, L3000, L1350) = 9.26, 5.15, 3.81
    Adopted from Richards et al. (2006) via Wu & Shen (2022); converts monochromatic luminosities to Lbol. Not fitted in this paper.
  • MBH single-epoch scaling coefficients (a,b) for Hbeta, MgII, CIV = (0.91,0.50), (0.74,0.62), (0.66,0.53)
    Adopted from Vestergaard & Peterson (2006)/Shen et al. (2011); used in Eq. 1. Not fitted.
  • Halpha MBH scaling with +0.125 dex rescaling = 6.43 + 0.55 log L + 2.06 log FWHM; +0.125 dex
    Greene & Ho (2005) relation rescaled per Mejia-Restrepo et al. (2022); Eq. 2.
  • Broad-line FWHM cap = 15000 km/s
    Imposed in PyQSOFit to reject spuriously broad fits; affects high-MBH tail.
assumptions (6)
  • domain assumption BOSS pipeline template classifications and ZWARNING flags are a reliable proxy for spectral identity/redshift
    Section 3.1; verified only on 23,986 visually inspected spectra; un-inspected majority assumed similar.
  • domain assumption PyQSOFit continuum+line decomposition is an adequate model for all quasars in the sample
    Section 3.2; includes power-law+polynomial continuum, FeII template, Gaussian lines; absorption/IGM only partially handled.
  • domain assumption Reverberation-mapping-based single-epoch mass prescriptions (Eq. 1-2) are valid
    Section 3.3; standard but with ~0.3 dex systematic uncertainty; CIV especially uncertain.
  • domain assumption Mean SED bolometric corrections from Richards et al. (2006) are universal
    Section 3.3; known 0.2 dex systematic scatter.
  • domain assumption Candidate-training sets of S19 and YS23 are representative of the all-sky quasar population
    Section 2.1.3; paper concedes bias against reddened/obscured quasars.
  • domain assumption Milliquas/v8 union DESI/DR1 is a complete reference for previously-known quasars
    Section 4.1; used to define 'new' quasars via 3'' match.

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Pith. "Pith review of An Exploratory Analysis of New Large Gaia-informed Quasar Samples in SDSS-V." pith.science (2026). https://pith.science/paper/UBUN2A37

@misc{pith2026260727329,
  author       = {Pith},
  title        = {Pith review of: An Exploratory Analysis of New Large Gaia-informed Quasar Samples in SDSS-V},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UBUN2A37}},
  note         = {Machine review of arXiv:2607.27329}
}
abstract

Quasars are luminous objects that provide insights into the physics and evolution of supermassive black holes (SMBHs) and their accretion flows, galaxy evolution, and even cosmology. In this study, we present an exploratory study based on the ongoing fifth generation of the Sloan Digital Sky Survey (SDSS-V) and its unique dual-hemisphere, wide-field, and multi-object spectroscopic capabilities, with the aim of creating a comprehensive, all-sky quasar sample. The targets were selected through two novel methods, GUA and Skewt-QSO, that rely primarily on data from WISE and Gaia, aiming to address gaps in previous large quasar samples. Our sample includes over 250,000 spectroscopically confirmed quasars reaching z~5, with tens of thousands of newly identified quasars in the southern hemisphere. The selection methods are highly pure, with well over 80% of the spectra collected being genuine quasars; the main contaminants are M-type stars. The detailed spectral decomposition procedure we employed shows that the quasars in the sample span a wide range of luminosities (Lbol~$10^{44}-10^{48} erg s^{-1}$), SMBH masses (MBH~$10^6-10^{10}$ Msun), and accretion rates (L/LEdd~0.01-1). The distributions of these properties are consistent with those of previous quasar catalogs, which are based on past generations of SDSS, once we account for potential selection biases related to the various survey depths. Our findings confirm that novel selection methods based on optical+IR colors and/or astrometry can yield a large, high-purity quasar sample over wide sky areas, including in cases where more nuanced multi-band photometry and/or multi-wavelength data in the X-ray or radio is not available. This SDSS-V sample, which will continue to grow, establishes a robust reference for future southern (time-domain) surveys, while enhancing and complementing our understanding of quasar demographics and SMBH evolution.

Figures

Figures reproduced from arXiv: 2607.27329 by the authors.

Figure 1
Figure 1. Parent sample construction. From top to bottom, the flowchart illustrates how quasar candidates are selected through the two main selection methods (based on S19 and YS23). The two techniques populate various, often non-unique SDSS-V target pools (“cartons”), which are used for actual MOS survey observations; and ultimately are made available for analysis as part of SDSS-V/DR20. We highlight in green the two final s… view at source ↗
Figure 2
Figure 2. Examples of the three basic categories of SDSS￾V spectra in our parent sample. From top to bottom: (1) a spectrum of a quasar that passed all the basic quality checks, including object classification and redshift assignment, and is therefore considered a good spectrum; (2) a spectrum of a quasar where the BOSS pipeline has issued a warning, but is still worth considering for efficiency analysis (i.e., part of the ba… view at source ↗
Figure 3
Figure 3. presents four examples from the bad sam￾ple, each of which falls into this category for a different reason. While some spectra are indeed unsuitable for de￾tailed spectral measurements, others still maintain rel￾atively reliable quality. The top panel shows a quasar with no ZWARNING but with ZERR = −1, implying that the best fit is at the lowest or highest redshift tested for a template. Despite this result, it can … view at source ↗
Figures from the paper (21 more)
Figure 4
Figure 4. Figure 4: Examples of various types of celestial objects in the GUA- and Skewt QSO-based samples. All four spectra are included in the good sample, as they are not affected by any severe issues (as indicate, e.g.,by the ZWARNING and ZERR). Stars and galaxies with reliable spectr…
Figure 5
Figure 5. Figure 5: An example of a successful spectral fit using PyQSOFit. The top panel shows the observed spectrum after host subtraction (black), the total best-fit model (red), and its various components: host-galaxy (purple), quasar continuum (orange), quasar and host templates (pin…
Figure 6
Figure 6. Figure 6: A source misclassified as a high-redshift quasar (z = 4.95) by the BOSS pipeline, which we identified in our visual inspection of PyQSOFit measurements yielding extreme quasar properties (in this case, extremely high Lbol). The legend description is similar to [PITH_F…
Figure 7
Figure 7. Figure 7: All-sky maps of the quasars observed within the GUA and Skewt QSO samples (including both good and bad samples), compared with previously-known quasars (gray; from Milliquas/v8 and DESI/DR1). Left: the GUA (blue) and Skewt QSO (red) samples. Right: the union of the two…
Figure 8
Figure 8. Figure 8: The differential (top) and cumulative (bottom) distributions of redshift for the quasars in our two sam￾ples (GUA and Skewt QSO), compared to the SDSS/DR7Q (Schneider et al. 2010) and SDSS/DR16Q (Lyke et al. 2020) reference quasar catalogs. GUA Skewt-QSO 0 20 40 60 80 …
Figure 9
Figure 9. Figure 9: The purity (efficiency) of the quasar selection among the GUA and Skewt QSO samples. The two bar charts show the hp of GUA and Skewt QSO samples. No￾tably, both of the methods achieved very high purity, over 96%. The black dashed lines indicate the corresponding lower …
Figure 10
Figure 10. Figure 10: The quasar selection purity of the GUA and Skewt QSO samples as a function of source brightness (left) and Galactic latitude (right). In both panels, the dashed lines with upward triangles show the corresponding lower limits, obtained by counting only good quasars as …
Figure 11
Figure 11. Figure 11: The distributions of our quasar samples in op￾tical color-color space, specifically r − i vs. g − r (left) and i − z vs. r − i (right). In both panels, black contours repre￾sent quasars from the legacy SDSS/DR7Q (Schneider et al. 2010), with enclosed regions correspon…
Figure 12
Figure 12. Figure 12: The mid-infrared W1 − W2 vs. W2 diagnostic diagram for various sources from our quasar samples. The blue, red and black contours represent the distributions for GUA, Skewt QSO and DR7Q, respectively (percentiles iden￾tical to those in [PITH_FULL_IMAGE:figures/full_fi…
Figure 13
Figure 13. Figure 13: The distribution of the R − W1 MIR-optical color for our quasars compared to LaMassa et al. (2024)’s AGN sample. We show the GUA (blue) and Skewt QSO (red) samples, compared with the sample of broad-line AGNs from LaMassa et al. (2024) (gray) and DR7Q (black). The dar…
Figure 14
Figure 14. Figure 14: Preliminary search for Hot DOGs using mid-infrared diagnostics, following the “W1W2 drop-outs” method from Assef et al. (2015, black dashed lines). The magnitudes are given in the Vega system. The contours represent the distributions of the quasars in our sample that …
Figure 16
Figure 16. Figure 16: Similar to [PITH_FULL_IMAGE:figures/full_fig_p025_16.png]
Figure 15
Figure 15. Figure 15: The distributions of our quasars in the Lbol − z (top) and log MBH − z (bottom) planes. In both panels, the contours (defined as in [PITH_FULL_IMAGE:figures/full_fig_p025_15.png]
Figure 17
Figure 17. Figure 17: Distributions of key parameters for our two main samples, GUA and Skewt QSO, and two comparison samples (SDSS/DR7Q and DR16Q; see legend). The upper panels show the probability density functions (PDFs) of, from left to right: G-band magnitudes, luminosities (Lbol), bl…
Figure 18
Figure 18. Figure 18: Results of the z − Lbol matching procedure, comparing GUA sample (as the test sample) and the SDSS/DR16Q sample (as the reference). The panels are identical to those of [PITH_FULL_IMAGE:figures/full_fig_p026_18.png]
Figure 19
Figure 19. Figure 19: Similar to [PITH_FULL_IMAGE:figures/full_fig_p027_19.png]
Figure 20
Figure 20. Figure 20: Upset diagrams illustrating the overlap between the GUA, Skewt QSO, and other main other higher priority BHM selection methods, grouped by the kind of prior knowledge they hold about the AGN nature of the sources. The top, middle and bottom panels show the overlap amo…
Figure 21
Figure 21. Figure 21: Venn diagrams presenting the selection and sample overlap considered in this work. Left: a conceptual illustration of the the seven disjoint subsets considered in our analysis of newly detected quasars (Section 4.1 and of selection efficiency (purity; this Appendix an…
Figure 22
Figure 22. Figure 22: An example of the matching method, between GUA and DR16Q. Each bin in the log Lbol − z space represents the number of quasars in the test sample (top) and the reference sample (bottom). The ratio between the two samples is displayed within each bin, where bins with ra…
Figure 23
Figure 23. Figure 23: Similar to [PITH_FULL_IMAGE:figures/full_fig_p037_23.png]
Figure 24
Figure 24. Figure 24: Similar to [PITH_FULL_IMAGE:figures/full_fig_p037_24.png]

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Works this paper leans on

115 extracted references · 8 canonical work pages

  1. [1]

    N., Adelman-McCarthy, J

    Abazajian, K. N., Adelman-McCarthy, J. K., Ag¨ ueros, M. A., et al. 2009, ApJS, 182, 543, doi: 10.1088/0067-0049/182/2/543

    doi:10.1088/0067-0049/182/2/543 2009

  2. [2]

    Abbott, T. M. C., Abdalla, F. B., Allam, S., et al. 2018, ApJS, 239, 18, doi: 10.3847/1538-4365/aae9f0

    doi:10.3847/1538-4365/aae9f0 2018

  3. [3]

    Abbott, T. M. C., Adam´ ow, M., Aguena, M., et al. 2021, ApJS, 255, 20, doi: 10.3847/1538-4365/ac00b3

    doi:10.3847/1538-4365/ac00b3 2021

  4. [4]

    2020, ApJS, 249, 3, doi: 10.3847/1538-4365/ab929e

    Ahumada, R., Allende Prieto, C., Almeida, A., et al. 2020, ApJS, 249, 3, doi: 10.3847/1538-4365/ab929e

    doi:10.3847/1538-4365/ab929e 2020

  5. [5]

    M., & Hickox, R

    Alexander, D. M., & Hickox, R. C. 2012, NewAR, 56, 93, doi: 10.1016/j.newar.2011.11.003

    doi:10.1016/j.newar.2011.11.003 2012

  6. [6]

    F., Argudo-Fern´ andez, M., et al

    Almeida, A., Anderson, S. F., Argudo-Fern´ andez, M., et al. 2023, ApJS, 267, 44, doi: 10.3847/1538-4365/acda98

    doi:10.3847/1538-4365/acda98 2023

  7. [7]

    T., Weigel, A

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

    doi:10.3847/1538-4365/ac5b64 2022

  8. [8]

    J., Stern, D., Noirot, G., et al

    Assef, R. J., Stern, D., Noirot, G., et al. 2018, ApJS, 234, 23, doi: 10.3847/1538-4365/aaa00a

    doi:10.3847/1538-4365/aaa00a 2018

Show all 115 references
  1. [9]

    J., Stern, D., Kochanek, C

    Assef, R. J., Stern, D., Kochanek, C. S., et al. 2013, ApJ, 772, 26, doi: 10.1088/0004-637X/772/1/26

    2013 doi

  2. [10]

    J., Eisenhardt, P

    Assef, R. J., Eisenhardt, P. R. M., Stern, D., et al. 2015, ApJ, 804, 27, doi: 10.1088/0004-637X/804/1/27 Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., et al. 2022, ApJ, 935, 167, doi: 10.3847/1538-4357/ac7c74

    2015 doi

  3. [11]

    2025, A&A, 698, A132, doi: 10.1051/0004-6361/202554372

    Aydar, C., Merloni, A., Dwelly, T., et al. 2025, A&A, 698, A132, doi: 10.1051/0004-6361/202554372

    2025 doi

  4. [12]

    McMahon, R. G. 2015, MNRAS, 447, 3368, doi: 10.1093/mnras/stu2649

    2015 doi

  5. [14]

    C., Batiste, M., Seals, J., et al

    Bentz, M. C., Batiste, M., Seals, J., et al. 2016, ApJ, 831, 2, doi: 10.3847/0004-637X/831/1/2

    2016 doi

  6. [15]

    2016, MNRAS, 463, 4041, doi: 10.1093/mnras/stw2207

    Bertemes, C., Trakhtenbrot, B., Schawinski, K., Done, C., & Elvis, M. 2016, MNRAS, 463, 4041, doi: 10.1093/mnras/stw2207

    2016 doi

  7. [16]

    R., Carlberg, J

    Blanton, M. R., Carlberg, J. K., Dwelly, T., et al. 2026, AJ, 171, 37, doi: 10.3847/1538-3881/ae196d

    2026 doi

  8. [17]

    S., Schlegel, D

    Bolton, A. S., Schlegel, D. J., Aubourg, ´E., et al. 2012, AJ, 144, 144, doi: 10.1088/0004-6256/144/5/144

    2012 doi

  9. [18]

    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

  10. [19]

    S., & Vaughan, Jr., A

    Bowen, I. S., & Vaughan, Jr., A. H. 1973, ApOpt, 12, 1430, doi: 10.1364/AO.12.001430

    1973 doi

  11. [20]

    A., Clayton, G

    Cardelli, J. A., Clayton, G. C., & Mathis, J. S. 1989, ApJ, 345, 245, doi: 10.1086/167900

    1989 doi

  12. [21]

    2023, ApJ, 953, 142, doi: 10.3847/1538-4357/ace1e5

    Cho, H., Woo, J.-H., Wang, S., et al. 2023, ApJ, 953, 142, doi: 10.3847/1538-4357/ace1e5

    2023 doi

  13. [22]

    2001, MNRAS, 328, 1039, doi: 10.1046/j.1365-8711.2001.04902.x

    Colless, M., Dalton, G., Maddox, S., et al. 2001, MNRAS, 328, 1039, doi: 10.1046/j.1365-8711.2001.04902.x

    2001

  14. [23]

    2020, The Open Journal of Astrophysics, 3, 13, doi: 10.21105/astro.2008.08404

    Comparat, J., Eckert, D., Finoguenov, A., et al. 2020, The Open Journal of Astrophysics, 3, 13, doi: 10.21105/astro.2008.08404

    2020 arXiv

  15. [25]

    M., Richards, G

    Croom, S. M., Richards, G. T., Shanks, T., et al. 2009, MNRAS, 392, 19, doi: 10.1111/j.1365-2966.2008.14052.x

    2009

  16. [26]

    M., Wright, E

    Cutri, R. M., Wright, E. L., Conrow, T., et al. 2013, Explanatory Supplement to the AllWISE Data Release

    2013

  17. [27]

    Products, Explanatory Supplement to the AllWISE Data Release Products, by R. M. Cutri et al. Dalla Bont` a, E., Peterson, B. M., Grier, C. J., et al. 2025, A&A, 696, A48, doi: 10.1051/0004-6361/202452746 de Jong, R. S., Agertz, O., Berbel, A. A., et al. 2019, The Messenger, 17...

    2025 doi

  18. [28]

    Denney, K. D. 2012, ApJ, 759, 44, doi: 10.1088/0004-637X/759/1/44 DESI Collaboration, Abdul Karim, M., Adame, A. G., et al. 2026, AJ, 171, 285, doi: 10.3847/1538-3881/ae4c43

    2012 doi

  19. [29]

    J., Lang, D., et al

    Dey, A., Schlegel, D. J., Lang, D., et al. 2019, AJ, 157, 168, doi: 10.3847/1538-3881/ab089d

    2019 doi

  20. [30]

    P., Bellstedt, S., Robotham, A

    Driver, S. P., Bellstedt, S., Robotham, A. S. G., et al. 2022, MNRAS, 513, 439, doi: 10.1093/mnras/stac472

    2022 doi

  21. [31]

    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

    2020 doi

  22. [32]

    2017, MNRAS, 469, 1065, doi: 10.1093/mnras/stx864

    Dwelly, T., Salvato, M., Merloni, A., et al. 2017, MNRAS, 469, 1065, doi: 10.1093/mnras/stx864

    2017 doi

  23. [33]

    A., et al

    Dye, S., Lawrence, A., Read, M. A., et al. 2018, MNRAS, 473, 5113, doi: 10.1093/mnras/stx2622

    2018 doi

  24. [34]

    2013, The Messenger, 154, 32

    Edge, A., Sutherland, W., Kuijken, K., et al. 2013, The Messenger, 154, 32

    2013

  25. [35]

    Eisenhardt, P. R. M., Wu, J., Tsai, C.-W., et al. 2012, ApJ, 755, 173, doi: 10.1088/0004-637X/755/2/173

    2012 doi

  26. [36]

    N., Evans, J

    Evans, I. N., Evans, J. D., Mart ´ ınez-Galarza, J. R., et al. 2024, ApJS, 274, 22, doi: 10.3847/1538-4365/ad6319

    2024 doi

  27. [37]

    Fan, X., Ba˜ nados, E., & Simcoe, R. A. 2023, ARA&A, 61, 373, doi: 10.1146/annurev-astro-052920-102455

    2023 doi

  28. [38]

    A., Alexander, D

    Fawcett, V. A., Alexander, D. M., Rosario, D. J., et al. 2020, MNRAS, 494, 4802, doi: 10.1093/mnras/staa954

    2020 doi

  29. [39]

    A., Alexander, D

    Fawcett, V. A., Alexander, D. M., Brodzeller, A., et al. 2023, MNRAS, 525, 5575, doi: 10.1093/mnras/stad2603 New Large Quasar Samples in SDSS-V39

    2023 doi

  30. [40]

    J., Olsen, K., Economou, F., et al

    Fitzpatrick, M. J., Olsen, K., Economou, F., et al. 2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9149, Observatory Operations: Strategies, Processes, and Systems V, ed. A. B. Peck, C. R. Benn, & R. L. Seaman, 91491T, doi: 10.1117/1...

    2014 doi

  31. [41]

    Flesch, E. W. 2019, arXiv e-prints, arXiv:1912.05614, doi: 10.48550/arXiv.1912.05614 —. 2023, The Open Journal of Astrophysics, 6, 49, doi: 10.21105/astro.2308.01505

    2019 doi arXiv

  32. [42]

    2021, ApJS, 254, 6, doi: 10.3847/1538-4365/abe85e Gaia Collaboration, Brown, A

    Fu, Y., Wu, X.-B., Yang, Q., et al. 2021, ApJS, 254, 6, doi: 10.3847/1538-4365/abe85e Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2016, A&A, 595, A2, doi: 10.1051/0004-6361/201629512 Gaia Collaboration, Babusiaux, C., van Leeuwen, F., et al. 2018, A&A, 616, A10,...

    2021 doi

  33. [43]

    R., Jiang, L., Brandt, W

    Gibson, R. R., Jiang, L., Brandt, W. N., et al. 2009, ApJ, 692, 758, doi: 10.1088/0004-637X/692/1/758

    2009 doi

  34. [44]

    2024, MNRAS, 528, 711, doi: 10.1093/mnras/stae042

    Lacy, M. 2024, MNRAS, 528, 711, doi: 10.1093/mnras/stae042

    2024 doi

  35. [45]

    2015, ApJ, 806, 218, doi: 10.1088/0004-637X/806/2/218

    Glikman, E., Simmons, B., Mailly, M., et al. 2015, ApJ, 806, 218, doi: 10.1088/0004-637X/806/2/218

    2015 doi

  36. [46]

    2012, ApJ, 757, 51, doi: 10.1088/0004-637X/757/1/51

    Glikman, E., Urrutia, T., Lacy, M., et al. 2012, ApJ, 757, 51, doi: 10.1088/0004-637X/757/1/51

    2012 doi

  37. [47]

    E., & Ho, L

    Greene, J. E., & Ho, L. C. 2005, ApJ, 630, 122, doi: 10.1086/431897

    2005 doi

  38. [48]

    E., Siegmund, W

    Gunn, J. E., Siegmund, W. A., Mannery, E. J., et al. 2006, AJ, 131, 2332, doi: 10.1086/500975

    2006 doi

  39. [49]

    2018, PyQSOFit: Python code to fit the spectrum of quasars, Astrophysics Source Code Library, record ascl:1809.008

    Guo, H., Shen, Y., & Wang, S. 2018, PyQSOFit: Python code to fit the spectrum of quasars, Astrophysics Source Code Library, record ascl:1809.008

    2018

  40. [50]

    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

    2024 doi

  41. [51]

    L., Ross, N., et al

    Hamann, F., Zakamska, N. L., Ross, N., et al. 2017, MNRAS, 464, 3431, doi: 10.1093/mnras/stw2387

    2017 doi

  42. [52]

    2025, ApJS, 278, 6, doi: 10.3847/1538-4365/adba52

    Huo, Z.-Y., Fu, Y., Huang, Y., et al. 2025, ApJS, 278, 6, doi: 10.3847/1538-4365/adba52

    2025 doi

  43. [53]

    H., Saunders, W., Colless, M., et al

    Jones, D. H., Saunders, W., Colless, M., et al. 2004, MNRAS, 355, 747, doi: 10.1111/j.1365-2966.2004.08353.x

    2004

  44. [54]

    M., et al

    Juneau, S., Canning, R., Alexander, D. M., et al. 2025, AJ, 169, 157, doi: 10.3847/1538-3881/adabc9

    2025 doi

  45. [55]

    A., Rix, H.-W., Aerts, C., et al

    Kollmeier, J. A., Rix, H.-W., Aerts, C., et al. 2026, AJ, 171, 52, doi: 10.3847/1538-3881/ae0576

    2026 doi

  46. [56]

    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

  47. [57]

    M., et al

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

    2024 doi

  48. [58]

    M., Civano, F., Brusa, M., et al

    LaMassa, S. M., Civano, F., Brusa, M., et al. 2016, ApJ, 818, 88, doi: 10.3847/0004-637X/818/1/88

    2016 doi

  49. [59]

    2014, AJ, 147, 108, doi: 10.1088/0004-6256/147/5/108

    Lang, D. 2014, AJ, 147, 108, doi: 10.1088/0004-6256/147/5/108

    2014 doi

  50. [60]

    J., Almaini, O., et al

    Lawrence, A., Warren, S. J., Almaini, O., et al. 2007, MNRAS, 379, 1599, doi: 10.1111/j.1365-2966.2007.12040.x

    2007

  51. [61]

    2014, IEEE Transactions on Vehicular Communications, 20, 1983, doi: 10.1109/TVCG.2014.2346248

    Pfister, H. 2014, IEEE Transactions on Vehicular Communications, 20, 1983, doi: 10.1109/TVCG.2014.2346248

    2014

  52. [62]

    2023, ApJ, 958, 162, doi: 10.3847/1538-4357/ace25b

    Li, G., Tsai, C.-W., Stern, D., et al. 2023, ApJ, 958, 162, doi: 10.3847/1538-4357/ace25b

    2023 doi

  53. [63]

    2025, ApJ, 981, 104, doi: 10.3847/1538-4357/adabe3

    Li, G., Wu, J., Tsai, C.-W., et al. 2025, ApJ, 981, 104, doi: 10.3847/1538-4357/adabe3

    2025 doi

  54. [64]

    2018, A&A, 616, A2, doi: 10.1051/0004-6361/201832727

    Lindegren, L., Hern´ andez, J., Bombrun, A., et al. 2018, A&A, 616, A2, doi: 10.1051/0004-6361/201832727

    2018 doi

  55. [65]

    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

  56. [67]

    G., Banerji, M., Gonzalez, E., et al

    McMahon, R. G., Banerji, M., Gonzalez, E., et al. 2013, The Messenger, 154, 35

    2013

  57. [68]

    M., Lang, D., Schlafly, E

    Meisner, A. M., Lang, D., Schlafly, E. F., & Schlegel, D. J. 2019, PASP, 131, 124504, doi: 10.1088/1538-3873/ab3df4 Mej ´ ıa-Restrepo, J. E., Trakhtenbrot, B., Lira, P., Netzer, H., & Capellupo, D. M. 2016, MNRAS, 460, 187, doi: 10.1093/mnras/stw568 Mej ´ ıa-Restrepo, J. E., T...

    2019 doi

  58. [69]

    D., Palanque-Delabrouille, N., Prakash, A., et al

    Myers, A. D., Palanque-Delabrouille, N., Prakash, A., et al. 2015, ApJS, 221, 27, doi: 10.1088/0067-0049/221/2/27

    2015 doi

  59. [70]

    2024, ApJL, 960, L1, doi: 10.3847/2041-8213/ad0e76

    Natarajan, P., Pacucci, F., Ricarte, A., et al. 2024, ApJL, 960, L1, doi: 10.3847/2041-8213/ad0e76

    2024 doi

  60. [71]

    2019, MNRAS, 488, 5185, doi: 10.1093/mnras/stz2016

    Netzer, H. 2019, MNRAS, 488, 5185, doi: 10.1093/mnras/stz2016

    2019 doi

  61. [72]

    Nikutta, R., Fitzpatrick, M., Scott, A., & Weaver, B. A. 2020, Astronomy and Computing, 33, 100411, doi: 10.1016/j.ascom.2020.100411

    2020

  62. [73]

    B., & Gunn, J

    Oke, J. B., & Gunn, J. E. 1983, ApJ, 266, 713, doi: 10.1086/160817

    1983 doi

  63. [74]

    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 Pˆ aris, I., Petitjean, P., Aubourg,´E., et al. 2014, A&A, 563, A54, doi: 10.1051/0004-6361/201322691 40A viram et al. Pˆ aris, I., Petitjean, P., Ross, N. P., et al. 2017,...

    2017 doi

  64. [75]

    Peterson, B. M. 2014, SSRv, 183, 253, doi: 10.1007/s11214-013-9987-4

    2014 doi

  65. [76]

    W., Derwent, M

    Pogge, R. W., Derwent, M. A., O’Brien, T. P., et al. 2020, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 11447, Ground-based and Airborne Instrumentation for Astronomy VIII, ed. C. J

    2020

  66. [77]

    Evans, J. J. Bryant, & K. Motohara, 1144781, doi: 10.1117/12.2561113

    doi

  67. [78]

    2020, MNRAS, 492, 4058, doi: 10.1093/mnras/staa078

    Pognan, Q., Trakhtenbrot, B., Sbarrato, T., Schawinski, K., & Bertemes, C. 2020, MNRAS, 492, 4058, doi: 10.1093/mnras/staa078

    2020 doi

  68. [79]

    2021, A&A, 647, A1, doi: 10.1051/0004-6361/202039313

    Predehl, P., Andritschke, R., Arefiev, V., et al. 2021, A&A, 647, A1, doi: 10.1051/0004-6361/202039313

    2021 doi

  69. [80]

    2023, Nature Astronomy, 7, 1282, doi: 10.1038/s41550-023-02108-4

    Ricci, C., & Trakhtenbrot, B. 2023, Nature Astronomy, 7, 1282, doi: 10.1038/s41550-023-02108-4

    2023 doi

  70. [81]

    E., Treister, E., et al

    Ricci, C., Bauer, F. E., Treister, E., et al. 2017, MNRAS, 468, 1273, doi: 10.1093/mnras/stx173

    2017 doi

  71. [82]

    T., Fan, X., Newberg, H

    Richards, G. T., Fan, X., Newberg, H. J., et al. 2002, AJ, 123, 2945, doi: 10.1086/340187

    2002 doi

  72. [83]

    T., Hall, P

    Richards, G. T., Hall, P. B., Vanden Berk, D. E., et al. 2003, AJ, 126, 1131, doi: 10.1086/377014

    2003 doi

  73. [84]

    T., Lacy, M., Storrie-Lombardi, L

    Richards, G. T., Lacy, M., Storrie-Lombardi, L. J., et al. 2006, ApJS, 166, 470, doi: 10.1086/506525

    2006 doi

  74. [85]

    P., Myers, A

    Ross, N. P., Myers, A. D., Sheldon, E. S., et al. 2012, ApJS, 199, 3, doi: 10.1088/0067-0049/199/1/3

    2012 doi

  75. [86]

    P., Hamann, F., Zakamska, N

    Ross, N. P., Hamann, F., Zakamska, N. L., et al. 2015, MNRAS, 453, 3932, doi: 10.1093/mnras/stv1710

    2015 doi

  76. [88]

    2022, A&A, 661, A3, doi: 10.1051/0004-6361/202141631

    Salvato, M., Wolf, J., Dwelly, T., et al. 2022, A&A, 661, A3, doi: 10.1051/0004-6361/202141631

    2022 doi

  77. [89]

    F., & Finkbeiner, D

    Schlafly, E. F., & Finkbeiner, D. P. 2011, ApJ, 737, 103, doi: 10.1088/0004-637X/737/2/103

    2011 doi

  78. [90]

    F., Meisner, A

    Schlafly, E. F., Meisner, A. M., & Green, G. M. 2019, ApJS, 240, 30, doi: 10.3847/1538-4365/aafbea

    2019 doi

  79. [91]

    J., Finkbeiner, D

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

    1998 doi

  80. [92]

    P., Richards, G

    Schneider, D. P., Richards, G. T., Hall, P. B., et al. 2010, AJ, 139, 2360, doi: 10.1088/0004-6256/139/6/2360

    2010 doi

  81. [93]

    2010, A&A, 516, A87, doi: 10.1051/0004-6361/201014193

    Schulze, A., & Wisotzki, L. 2010, A&A, 516, A87, doi: 10.1051/0004-6361/201014193

    2010 doi

  82. [94]

    2015, MNRAS, 447, 2085, doi: 10.1093/mnras/stu2549 SDSS Collaboration, Aghakhanloo, M., Aird, J., et al

    Schulze, A., Bongiorno, A., Gavignaud, I., et al. 2015, MNRAS, 447, 2085, doi: 10.1093/mnras/stu2549 SDSS Collaboration, Aghakhanloo, M., Aird, J., et al. 2026, ApJS, 285, 9, doi: 10.3847/1538-4365/ae4697

    2015 doi

  83. [95]

    I., & Sunyaev, R

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

    1973

  84. [96]

    2013, Bulletin of the Astronomical Society of India, 41, 61, doi: 10.48550/arXiv.1302.2643

    Shen, Y. 2013, Bulletin of the Astronomical Society of India, 41, 61, doi: 10.48550/arXiv.1302.2643

    2013 doi arXiv

  85. [97]

    2012, ApJ, 753, 125, doi: 10.1088/0004-637X/753/2/125

    Shen, Y., & Liu, X. 2012, ApJ, 753, 125, doi: 10.1088/0004-637X/753/2/125

    2012 doi

  86. [98]

    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

  87. [99]

    B., Horne, K., et al

    Shen, Y., Hall, P. B., Horne, K., et al. 2019, ApJS, 241, 34, doi: 10.3847/1538-4365/ab074f

    2019 doi

  88. [100]

    E., Evans, N

    Shu, Y., Koposov, S. E., Evans, N. W., et al. 2019, MNRAS, 489, 4741, doi: 10.1093/mnras/stz2487

    2019 doi

  89. [101]

    F., Cutri, R

    Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163, doi: 10.1086/498708

    2006 doi

  90. [102]

    A., Gunn, J

    Smee, S. A., Gunn, J. E., Uomoto, A., et al. 2013, AJ, 146, 32, doi: 10.1088/0004-6256/146/2/32

    2013 doi

  91. [103]

    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

    2012 doi

  92. [104]

    W., Rix, H.-W., et al

    Storey-Fisher, K., Hogg, D. W., Rix, H.-W., et al. 2024, ApJ, 964, 69, doi: 10.3847/1538-4357/ad1328

    2024 doi

  93. [105]

    2012, MNRAS, 427, 3081, doi: 10.1111/j.1365-2966.2012.22056.x

    Trakhtenbrot, B., & Netzer, H. 2012, MNRAS, 427, 3081, doi: 10.1111/j.1365-2966.2012.22056.x

    2012

  94. [106]

    H., White, R

    Urrutia, T., Becker, R. H., White, R. L., et al. 2009, ApJ, 698, 1095, doi: 10.1088/0004-637X/698/2/1095

    2009 doi

  95. [107]

    Fabian, A. C. 2009, MNRAS, 399, 1553, doi: 10.1111/j.1365-2966.2009.15371.x

    2009

  96. [108]

    Vestergaard, M., & Peterson, B. M. 2006, ApJ, 641, 689, doi: 10.1086/500572

    2006 doi

  97. [109]

    N., Stern, D., et al

    Vito, F., Brandt, W. N., Stern, D., et al. 2018, MNRAS, 474, 4528, doi: 10.1093/mnras/stx3120

    2018 doi

  98. [110]

    2021, ApJL, 907, L1, doi: 10.3847/2041-8213/abd8c6

    Wang, F., Yang, J., Fan, X., et al. 2021, ApJL, 907, L1, doi: 10.3847/2041-8213/abd8c6

    2021 doi

  99. [111]

    2024, ApJS, 273, 21, doi: 10.3847/1538-4365/ad58df

    Werk, J., Tchernyshyov, K., Bish, H., et al. 2024, ApJS, 273, 21, doi: 10.3847/1538-4365/ad58df

    2024 doi

  100. [112]

    J., Morris, S

    Weymann, R. J., Morris, S. L., Foltz, C. B., & Hewett, P. C. 1991, ApJ, 373, 23, doi: 10.1086/170020

    1991 doi

  101. [113]

    2022, ApJS, 263, 42, doi: 10.3847/1538-4365/ac9ead

    Wu, Q., & Shen, Y. 2022, ApJS, 263, 42, doi: 10.3847/1538-4365/ac9ead

    2022 doi

  102. [114]

    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

  103. [115]

    L., et al

    Yao, S., Wu, X.-B., Ai, Y. L., et al. 2019, ApJS, 240, 6, doi: 10.3847/1538-4365/aaef88

    2019 doi

  104. [116]

    W., Connolly, A

    Yip, C. W., Connolly, A. J., Szalay, A. S., et al. 2004a, AJ, 128, 585, doi: 10.1086/422429

    doi

  105. [117]

    W., Connolly, A

    Yip, C. W., Connolly, A. J., Vanden Berk, D. E., et al. 2004b, AJ, 128, 2603, doi: 10.1086/425626

    doi

  106. [118]

    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

    2000 doi

  107. [119]

    2024, ApJ, 966, 85, doi: 10.3847/1538-4357/ad2f30

    Zeltyn, G., Trakhtenbrot, B., Eracleous, M., et al. 2024, ApJ, 966, 85, doi: 10.3847/1538-4357/ad2f30

    2024 doi

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