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arxiv: 2512.23060 · v2 · submitted 2025-12-28 · 🌌 astro-ph.GA

Recognition: no theorem link

Finding Quasars behind the Galactic Plane. IV. Candidate Selection from Chandra with Random Forest

Xu Zhang , Yanli Ai , Yanxia Zhang , Yuming Fu , Xue-Bing Wu , Zhiying Huo , Wenfeng Wen , Jiayuan Zhou
show 3 more authors
Dexuan Kong Linfeng Zeng Heng Wang
Authors on Pith no claims yet

Pith reviewed 2026-05-16 18:53 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords quasarsGalactic planeChandra Source CatalogRandom ForestX-ray selectionGaia proper motionsphotometric redshiftsGalactic Plane Quasars
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The pith

A Random Forest classifier on Chandra X-ray sources with Gaia and CatWISE data selects 7570 quasar candidates including 1060 at low Galactic latitudes.

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

The paper shows how to find quasars at low Galactic latitudes where dust extinction and source crowding have left samples incomplete. Training a Random Forest on confirmed quasars and stars from DESI, SDSS, and LAMOST, then applying it to unclassified Chandra sources while rejecting stars via Gaia proper motions, produces a much larger candidate list that reaches fainter fluxes. This matters because background quasars can serve as reference points for astrometry and as lights shining through the Milky Way to reveal its gas and dust. The selected objects also show harder X-ray spectra, matching expectations for extra absorption along plane sightlines. Pilot spectra already confirm two candidates as quasars at redshifts 1.26 and 1.13.

Core claim

Applying this framework to previously unclassified CSC sources, we identify 7570 quasar candidates, including 1060 Galactic Plane Quasar (GPQ) candidates at |b|<20°, of which 551 are high-confidence candidates. Relative to the previously known GPQ sample, our selected GPQs reach lower optical and X-ray fluxes. Both the GPQ candidates and known GPQs display harder X-ray spectra than the all-sky quasar sample. Pilot spectroscopy confirms two high-confidence GPQ candidates as quasars at spectroscopic redshifts of z=1.2582 and z=1.1313.

What carries the argument

Random Forest classifier trained on spectroscopically confirmed quasars and stellar objects, using combined X-ray, optical, and mid-infrared photometry together with Gaia proper-motion cuts to suppress stellar contaminants.

If this is right

  • The new GPQ candidates reach fainter optical and X-ray fluxes than earlier samples, extending the reach to lower-luminosity objects.
  • Both candidates and known GPQs exhibit harder X-ray spectra than the all-sky quasar population, consistent with line-of-sight absorption.
  • The 7570-candidate list supplies targets for spectroscopic campaigns that can refine astrometric reference frames.
  • Absorption features in confirmed GPQs can be used to map Milky Way interstellar and circumgalactic gas.
  • Photometric redshifts generated by the regression model allow preliminary statistical work on the population before spectra are obtained.

Where Pith is reading between the lines

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

  • The same Random Forest pipeline could be run on larger X-ray catalogs to produce an all-sky obscured-quasar sample.
  • High-confidence GPQ candidates offer a way to trace the three-dimensional distribution of dust and gas in the inner Milky Way.
  • The photometric redshift estimates could support clustering or luminosity-function studies of plane quasars without waiting for complete spectroscopy.

Load-bearing premise

The training sets drawn from DESI, SDSS, and LAMOST are representative of the actual quasar and stellar populations found behind the Galactic plane despite higher extinction and crowding.

What would settle it

A spectroscopic campaign on the 551 high-confidence GPQ candidates that returns a true-quasar fraction well below 50 percent would show the classifier does not reliably isolate quasars.

Figures

Figures reproduced from arXiv: 2512.23060 by Dexuan Kong, Heng Wang, Jiayuan Zhou, Linfeng Zeng, Wenfeng Wen, Xue-Bing Wu, Xu Zhang, Yanli Ai, Yanxia Zhang, Yuming Fu, Zhiying Huo.

Figure 1
Figure 1. Figure 1: Normalized confusion matrix of the RF classifier computed on the validation set. The matrix is color-coded by the number of sources in each cell. Diagonal entries show the fraction of correctly classified objects (i.e., recall or com￾pleteness) for each class, while off-diagonal entries indicate the misclassification rates. is further supported by an overall F1−score of 0.9868, indicating consistently high… view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of RF-based photometric redshifts for the quasar candidates. The red histogram shows the ful￾l-sky candidate sample, and the blue histogram shows can￾didates at low Galactic latitude (|b| < 20◦ ). The ordinate shows source counts (logarithmic scale). model does not introduce significant redshift-dependent biases in the low-latitude regime. These photometric redshifts provide a useful statistic… view at source ↗
Figure 4
Figure 4. Figure 4: Distributions of log(fPM0), the probability den￾sity at zero proper motion derived from Gaia astrometry (see Eq. 8), for spectroscopically confirmed quasars (green), spectroscopically confirmed stars (yellow), and the GPQ can￾didates (white). The vertical dashed line marks the adopted threshold log(fPM0) = −4 used to suppress stellar contami￾nants; note that fPM0 is a probability density (not a proba￾bilit… view at source ↗
Figure 5
Figure 5. Figure 5: Spatial distribution of GPQ candidates (|b| < 20◦ ) in Galactic coordinates (Mollweide projection). Points are color-coded by the RF-derived quasar membership probability, PQSO (color bar). 0.5 0.0 0.5 HRhs 0.00 0.02 0.04 0.06 0.08 0.10 0.12 N n o r m aliz e d QSOs in Training Set QSOs in Training Set (|b|<20°) QSO Candidates QSO Candidates (|b|<20°) [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Normalized histograms of the HRhs (hard – soft energy band hardness ratio, see table 3) for quasars. Filled histograms representing all-sky/galactic plane (|b| < 20◦ ) training set samples, and step outlines for selected al￾l-sky/galactic plane candidates. sources. To further suppress stellar contamination, we apply a probabilistic zero–proper-motion criterion based on Gaia astrometry. In addition, we esti… view at source ↗
Figure 7
Figure 7. Figure 7: Normalized histograms of G, W1, log(fx) (logarithm of X-ray flux in 0.5-7keV) and redshift (here, the redshifts of the QSO candidates are estimated photometrically via RF.) for GPQs and GPQ candidates. Filled histograms representing training set samples, and step outlines for selected candidates. GPQ candidates have counterparts in existing catalogs, a significant fraction does not. These additional can￾di… view at source ↗
Figure 8
Figure 8. Figure 8: Spectra of the two identified GPQs from our pilot follow-up program with NGPS of Hale Telescope. We show flux￾and wavelength-calibrated one-dimensional optical spectra of 2CXO J031404.4+403900 (upper) and 2CXO J031712.7+405301 (lower). The redshift inferred from identified emission lines is given in the upper-right corner of each panel. Strong emission features are labeled, and the spectra are displayed in… view at source ↗
read the original abstract

Quasar samples remain severely incomplete at low Galactic latitudes because of strong extinction and source confusion. We conduct a systematic search for quasars behind the Galactic plane using X-ray sources from the Chandra Source Catalog (CSC 2.1), combined with optical data from Gaia DR3 and mid-infrared data from CatWISE2020. Using spectroscopically confirmed quasars and stellar-type objects from data sets including DESI, SDSS, and LAMOST, we apply a Random Forest classifier to identify quasar candidates, with stellar contaminants suppressed using Gaia proper-motion constraints. Photometric redshifts are estimated for the candidates using a Random Forest regression model. Applying this framework to previously unclassified CSC sources, we identify 7570 quasar candidates, including 1060 Galactic Plane Quasar (GPQ) candidates at |b|<20{\deg}, of which 551 are high-confidence candidates. Relative to the previously known GPQ sample, our selected GPQs reach lower optical and X-ray fluxes, improving sensitivity to low-flux GPQs. In addition, both the GPQ candidates and known GPQs display harder X-ray spectra than the all-sky quasar sample, consistent with increased absorption through the Galactic plane. Pilot spectroscopy confirms two high-confidence GPQ candidates as quasars at spectroscopic redshifts of z=1.2582 and z=1.1313, and further spectroscopic follow-up of the GPQ sample is underway. This work substantially improves the census of GPQs and provides a valuable target sample for future spectroscopic follow-up, enabling the use of GPQs to refine the reference frames for astrometry and probe the Milky Way interstellar and circumgalactic media with the absorption features of GPQs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript applies a Random Forest classifier, trained on spectroscopically confirmed quasars and stars from DESI, SDSS, and LAMOST, to Chandra Source Catalog X-ray sources combined with Gaia DR3 and CatWISE2020 photometry. Gaia proper-motion cuts are used to suppress stellar contaminants. The work reports 7570 quasar candidates overall, including 1060 Galactic Plane Quasar (GPQ) candidates at |b|<20° (551 high-confidence), with photometric redshifts estimated via Random Forest regression. Pilot spectroscopy confirms two high-confidence GPQ candidates at z=1.2582 and z=1.1313; the GPQ sample shows harder X-ray spectra than the all-sky quasar population, attributed to Galactic absorption. The method aims to improve the census of low-latitude quasars for future follow-up.

Significance. If the photometric selection proves reliable, the catalog substantially enlarges the known GPQ sample at fainter fluxes, supporting studies of Milky Way interstellar and circumgalactic media via absorption lines and improved astrometric reference frames. Strengths include the use of established multi-wavelength features, proper-motion filtering, and the two pilot redshifts; the result would be a valuable target list pending broader spectroscopic validation.

major comments (3)
  1. [Methods (classifier training and feature selection)] The Random Forest is trained primarily on high-latitude (|b|>20°) spectroscopic samples from DESI/SDSS/LAMOST and applied directly to |b|<20° CSC sources (abstract and methods description of classifier training). No explicit test or adjustment for differential extinction shifts in Gaia optical and CatWISE mid-IR colors is reported, which could move the target feature distributions outside the training domain and affect the reliability of the 1060 GPQ candidates and the 551 high-confidence subset.
  2. [Results (X-ray spectral properties)] The claim that GPQ candidates exhibit harder X-ray spectra than the all-sky sample (abstract and results section) is presented as evidence of increased absorption, but the manuscript does not quantify whether this hardness difference persists after controlling for the X-ray flux limit, the RF probability threshold, or selection biases in the CSC sample.
  3. [Validation and pilot spectroscopy] Only two pilot spectroscopic confirmations are provided for the high-confidence GPQ candidates. While these support the method, the absence of a quantified false-positive rate, cross-validation on a low-latitude hold-out set, or larger follow-up statistics leaves the purity of the 551 high-confidence objects untested at the scale needed to support the central claim of 1060 reliable GPQ candidates.
minor comments (2)
  1. [Abstract] The definition of 'high-confidence' (probability threshold or other criteria) for the 551 GPQ candidates is not stated in the abstract and should be added for clarity.
  2. [Figures] Feature-importance plots or color-color diagrams comparing training and target distributions would help readers assess the domain-shift concern raised above.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for the careful and constructive review of our manuscript. We address each major comment point by point below, indicating where revisions will be made to improve the analysis and presentation.

read point-by-point responses

  1. Referee: [Methods (classifier training and feature selection)] The Random Forest is trained primarily on high-latitude (|b|>20°) spectroscopic samples from DESI/SDSS/LAMOST and applied directly to |b|<20° CSC sources (abstract and methods description of classifier training). No explicit test or adjustment for differential extinction shifts in Gaia optical and CatWISE mid-IR colors is reported, which could move the target feature distributions outside the training domain and affect the reliability of the 1060 GPQ candidates and the 551 high-confidence subset.

    Authors: We thank the referee for highlighting this potential issue. Our feature set combines X-ray properties, Gaia proper motions, and photometry selected for robustness against moderate extinction, and the proper-motion filter already removes many stellar contaminants. Nevertheless, we acknowledge that differential extinction could induce domain shift. In the revised manuscript we will add a dedicated subsection comparing feature distributions (particularly Gaia BP-RP and CatWISE W1-W2) between the high-latitude training set and the low-latitude target sample, together with a test using synthetic catalogs that apply realistic Galactic-plane extinction. This will quantify any degradation in classifier performance and support the reliability of the reported GPQ candidates. revision: yes

  2. Referee: [Results (X-ray spectral properties)] The claim that GPQ candidates exhibit harder X-ray spectra than the all-sky sample (abstract and results section) is presented as evidence of increased absorption, but the manuscript does not quantify whether this hardness difference persists after controlling for the X-ray flux limit, the RF probability threshold, or selection biases in the CSC sample.

    Authors: We agree that a controlled comparison is required to isolate the absorption effect. In the revised results section we will present hardness-ratio distributions for GPQ candidates and the all-sky quasar sample in bins of matched X-ray flux and at fixed RF probability thresholds. We will also discuss possible CSC selection biases. These additions will demonstrate whether the observed hardness difference remains statistically significant after accounting for the factors raised by the referee. revision: yes

  3. Referee: [Validation and pilot spectroscopy] Only two pilot spectroscopic confirmations are provided for the high-confidence GPQ candidates. While these support the method, the absence of a quantified false-positive rate, cross-validation on a low-latitude hold-out set, or larger follow-up statistics leaves the purity of the 551 high-confidence objects untested at the scale needed to support the central claim of 1060 reliable GPQ candidates.

    Authors: The two pilot spectra at z=1.2582 and z=1.1313 provide direct confirmation that the classifier can identify genuine quasars at low latitude. We already report cross-validation metrics (accuracy, precision, recall) from the high-latitude training set and define the high-confidence subset via an RF probability threshold. A large independent low-latitude spectroscopic hold-out set does not currently exist. We will revise the discussion to emphasize that the 1060 objects remain candidates, to state the training-set performance metrics explicitly, and to highlight that dedicated follow-up spectroscopy is ongoing. These changes will better contextualize the current validation level. revision: partial

standing simulated objections not resolved
  • Absence of a sufficiently large low-latitude spectroscopic sample for direct hold-out cross-validation or empirical false-positive rate estimation at |b|<20°.

Circularity Check

0 steps flagged

No circularity: classifier trained on independent spectroscopic catalogs and applied to new CSC sources

full rationale

The paper trains a Random Forest classifier on spectroscopically confirmed quasars and stars drawn from DESI, SDSS, and LAMOST catalogs, then applies the model to previously unclassified Chandra CSC sources. No equation or parameter is fitted to the target CSC data and then re-used as a 'prediction' on the same data. The training sets are external to the prediction set, and the reported candidates (7570 total, 1060 GPQ) are outputs of this forward application rather than re-statements of fitted inputs. Pilot spectroscopic redshifts serve as external validation, not internal closure. No self-citation chain, uniqueness theorem, or ansatz smuggling is invoked to force the result. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central claim depends on the representativeness of spectroscopic training data and the discriminative power of the chosen multi-wavelength features under Galactic extinction.

free parameters (2)
  • Random Forest hyperparameters
    Number of trees, maximum depth, and feature sampling chosen during training on the spectroscopic sample.
  • Classification probability threshold
    Decision threshold tuned to select high-confidence GPQ candidates from the unclassified CSC sources.
axioms (1)
  • domain assumption Spectroscopic training sets from DESI, SDSS, and LAMOST accurately represent the feature distributions of quasars and stars in the target Galactic plane fields.
    Invoked when the classifier trained on these surveys is applied to previously unclassified Chandra sources.

pith-pipeline@v0.9.0 · 5650 in / 1302 out tokens · 46453 ms · 2026-05-16T18:53:39.778051+00:00 · methodology

discussion (0)

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