arxiv: 2605.09450 · v1 · submitted 2026-05-10 · 🌌 astro-ph.GA

Recognition: no theorem link

Search for quasar pairs with Gaia astrometric data. II. Photometric redshift prediction with machine learning for the MGQPC catalogue

Jianghua Wu, Jun-Qing Xia, Liang Jing, Qihang Chen, Xingyu Zhu, Yanxia Zhang, Zhuojun Deng

Authors on Pith no claims yet

Pith reviewed 2026-05-12 04:45 UTC · model grok-4.3

classification 🌌 astro-ph.GA

keywords quasar pairsphotometric redshiftsmachine learningMGQPC catalogueGaia astrometrydual supermassive black holesgalaxy evolution

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The pith

Machine learning predicts photometric redshifts for MGQPC quasars to identify 185 consistent pair candidates, 20 of which are spectroscopically confirmed.

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

The paper builds machine-learning models to estimate photometric redshifts and full redshift probability distributions for quasars, using multi-band photometry from the SDSS and DESI Legacy Imaging Surveys as training data. These models are then applied to the MGQPC catalogue, which was assembled from Gaia astrometric selections, to flag pairs whose two members show matching photometric redshifts. The workflow reports a normalized median absolute deviation of 0.036 and a 5.6 percent outlier rate on held-out test data, and produces 185 high-probability candidates of which 20 have already been verified as genuine physical pairs by independent spectroscopy. The resulting photometric-redshift catalogue is presented as a practical resource for prioritizing follow-up observations of rare kiloparsec-scale quasar pairs.

Core claim

A CatBoost regression model for photometric-redshift point estimates combined with a FlexZBoost model for full redshift probability density functions, trained on large spectroscopically confirmed quasar samples from SDSS and DESI, is applied to the MGQPC catalogue and yields 185 high-probability quasar pair candidates selected by photometric-redshift consistency, twenty of which have been independently confirmed as physical systems by spectroscopy.

What carries the argument

The CatBoost and FlexZBoost machine-learning models trained on SDSS and DESI multi-wavelength photometry to produce quasar photometric-redshift point estimates and probability density functions.

If this is right

The MGQPC photometric-redshift catalogue supplies a ready list for targeted spectroscopic campaigns on quasar pairs and dual supermassive black holes.
185 candidates can be ranked by consistency probability for efficient allocation of follow-up telescope time.
The reported performance metrics indicate that similar training sets can be used to pre-filter projected alignments in other Gaia-selected quasar samples.

Where Pith is reading between the lines

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

The same workflow could be retrained on upcoming wide-field surveys to discover additional close quasar pairs at higher redshifts.
Confirmed physical pairs offer direct targets for studying how supermassive black holes and their host galaxies interact at kiloparsec separations.
Reducing the fraction of line-of-sight contaminants through photometric-redshift pre-selection lowers the cost of spectroscopic confirmation campaigns.

Load-bearing premise

Photometric-redshift agreement between the two members of a candidate pair reliably signals physical association rather than chance line-of-sight alignment, and the models trained on SDSS and DESI data transfer without major bias to the MGQPC quasars.

What would settle it

Spectroscopic follow-up of the 185 candidates showing that the majority have redshift differences too large for physical association, or an independent test set of quasars where the reported normalized median absolute deviation rises well above 0.036.

Figures

Figures reproduced from arXiv: 2605.09450 by Jianghua Wu, Jun-Qing Xia, Liang Jing, Qihang Chen, Xingyu Zhu, Yanxia Zhang, Zhuojun Deng.

**Figure 1.** Figure 1: Spectroscopic redshift distributions for the KSTS (black) and KSTD (grey) quasar samples used in this work. The histograms are shown as step curves, and the vertical axis indicates the number of objects in each redshift bin on a logarithmic scale. puted on disjoint prefixes (Dorogush et al. 2018; Prokhorenkova et al. 2018). As base learners, CatBoost employs symmetric (oblivious) trees in which all nodes … view at source ↗

**Figure 2.** Figure 2: Feature importance and validation RMSE for the KSTS sample. Normalised SHAP importance (left axis) of each feature is shown by bars. The dashed line represents the validation RMSE (right axis) achieved by cumulatively adding features in descending order of SHAP importance. For clarity, only the top 23 most important features are displayed. scores were normalised to sum to unity. We then ran RFE based on … view at source ↗

**Figure 3.** Figure 3: Photo-z performance for two algorithms (FlexZBoost, left; CatBoost, right) on two samples (KSTD, top; KSTS, bottom). In each big panel, the scatter plot shows zphot versus zspec. The solid line is the identity relation; dashed lines (|∆z| = 0.15) mark the outlier threshold, where ∆z = (zspec−zphot)/(1+zspec). Insets list σRMSE, σNMAD, and the outlier fraction, η. The small panels display the distribution o… view at source ↗

**Figure 5.** Figure 5: Redshift-binned photo-z performance for CatBoost (blue) and FlexZBoost (red) on the KSTD (solid lines) and KSTS (dashed lines) samples. From top to bottom: ∆z, scatter quantified by σNMAD; outlier fraction, η; and the spectroscopic redshift distribution of the two samples. All curves were computed in the same zspec bins to highlight systematic trends with redshift. 5.2. Redshift trends and comparison wit… view at source ↗

read the original abstract

The identification of physically associated kiloparsec-scale quasar pairs is important for understanding galaxy evolution, the growth of supermassive black holes, and their co-evolution with host galaxies. However, their rarity and the high contamination from stellar superpositions and projected alignments require efficient pre-selection methods. We develop a machine-learning framework to produce photometric-redshift point estimates and redshift probability density functions for quasars, with the main goal of identifying high-probability quasar pair candidates in the MGQPC catalogue. We construct two large spectroscopically confirmed quasar samples with multi-wavelength photometry, based on SDSS and DESI Legacy Imaging Surveys data. CatBoost is used for point-estimate photometric-redshift regression, and FlexZBoost is used for full redshift-PDF estimation. The workflow achieves robust performance, with a normalised median absolute deviation of 0.036 and an outlier fraction of 5.6% on the test sample. Applying the trained model to the MGQPC catalogue, we identify 185 high-probability quasar pair candidates based on photometric-redshift consistency. Among them, 20 systems have been subsequently confirmed as genuine physical pairs by independent spectroscopic observations. The resulting MGQPC photometric-redshift catalogue provides a useful resource for future spectroscopic follow-up of quasar pairs and dual supermassive black holes.

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.

Desk Editor's Note private letter to a colleague

The paper applies standard ML photo-z tools to the MGQPC quasar pair catalogue and produces a list of 185 candidates with 20 spectroscopically confirmed, but provides no estimate of line-of-sight contamination.

read the letter

The main thing here is a practical pipeline that trains CatBoost for point photo-z estimates and FlexZBoost for PDFs on SDSS and DESI spectroscopic quasar samples, then runs it on the MGQPC candidates to flag 185 systems where the two members have consistent photometric redshifts. Twenty of those have since been confirmed as physical pairs by follow-up spectroscopy. That gives a usable new catalogue and a short list for observers to chase.

Referee Report

1 major / 2 minor

Summary. The manuscript develops a machine-learning pipeline (CatBoost for point-estimate photometric redshifts and FlexZBoost for redshift PDFs) trained on spectroscopically confirmed quasar samples from SDSS and DESI Legacy Imaging Surveys. The trained models are applied to the MGQPC catalogue to select 185 quasar-pair candidates whose members have consistent photometric redshifts; 20 of these systems have subsequently been confirmed as physical pairs by independent spectroscopy. The reported test-sample performance is NMAD = 0.036 and outlier fraction 5.6%.

Significance. If the photo-z estimates transfer without major bias and the candidate list has quantified low contamination, the work supplies a practical pre-selection tool for rare kpc-scale quasar pairs and a public photometric-redshift catalogue for MGQPC. The 20 spectroscopically confirmed pairs demonstrate that true positives exist and that the workflow can be useful for follow-up studies of dual SMBHs and galaxy evolution.

major comments (1)

[Application to MGQPC catalogue] § on application to MGQPC and candidate selection (abstract and results): The selection of 185 'high-probability' quasar pair candidates rests entirely on photometric-redshift consistency between the two members. With the reported NMAD of 0.036, any finite consistency window (e.g., |Δz| < 0.1 or 3σ) will be satisfied by a non-negligible fraction of unrelated quasars drawn from the same redshift distribution. The manuscript provides no Monte-Carlo estimate of this random-alignment rate, no control sample of random pairs, and no statement of how many of the 185 were pre-selected for spectroscopic follow-up versus how many were observed. Consequently the purity of the full list cannot be assessed from the 20 confirmed pairs alone.

minor comments (2)

[Abstract] Abstract: The abstract states that performance metrics are obtained 'on the test sample' but does not report the training/validation/test split fractions or the precise definition of the outlier fraction; these details should be added for reproducibility.
[Methods] Methods: The exact numerical threshold or criterion used to declare photometric-redshift consistency between pair members (e.g., |Δz| < X or |Δz| < n × σ) is not stated explicitly; this should be given in the text or a table.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive and detailed review of our manuscript. We address the major comment below and have revised the manuscript to incorporate a quantitative assessment of random alignments, thereby improving the interpretation of the candidate list.

read point-by-point responses

Referee: [Application to MGQPC catalogue] § on application to MGQPC and candidate selection (abstract and results): The selection of 185 'high-probability' quasar pair candidates rests entirely on photometric-redshift consistency between the two members. With the reported NMAD of 0.036, any finite consistency window (e.g., |Δz| < 0.1 or 3σ) will be satisfied by a non-negligible fraction of unrelated quasars drawn from the same redshift distribution. The manuscript provides no Monte-Carlo estimate of this random-alignment rate, no control sample of random pairs, and no statement of how many of the 185 were pre-selected for spectroscopic follow-up versus how many were observed. Consequently the purity of the full list cannot be assessed from the 20 confirmed pairs alone.

Authors: We agree that a Monte Carlo estimate of the random-alignment rate is necessary to contextualize the purity of the 185 candidates. In the revised manuscript we have added such an analysis: we draw mock pairs from the photometric-redshift distribution of the full MGQPC sample, apply the identical consistency window used for the real candidates, and report the resulting expected number of chance alignments. This provides a direct estimate of contamination. We also clarify the spectroscopic follow-up status, noting that the 20 confirmed physical pairs were obtained from targeted observations of a subset of the candidates (with the exact numbers now stated), while the full list of 185 is released as a resource for additional follow-up. The 20 confirmations demonstrate that genuine pairs are recovered by the method; the added simulation allows readers to assess the overall reliability of the pre-selection. revision: yes

Circularity Check

0 steps flagged

No circularity: ML training and application are independent of target catalogue

full rationale

The paper trains CatBoost and FlexZBoost models on spectroscopically confirmed quasar samples from SDSS and DESI Legacy Surveys, reports NMAD=0.036 and outlier fraction 5.6% on a held-out test subset, then applies the fixed trained model to the separate MGQPC catalogue to obtain photo-z values and select 185 candidates by redshift consistency. The 20 spectroscopically confirmed physical pairs are from independent follow-up observations. No equation, parameter, or selection criterion is defined in terms of the MGQPC outputs themselves, and no self-citation supplies a load-bearing uniqueness theorem or ansatz that collapses the workflow to its inputs.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard machine-learning assumptions plus domain knowledge that photometric redshifts can serve as a proxy for physical association in quasar pairs.

free parameters (1)

CatBoost and FlexZBoost hyperparameters
Optimized on the spectroscopic training samples to achieve the reported NMAD and outlier fraction.

axioms (2)

domain assumption Spectroscopically confirmed SDSS and DESI quasars are representative of the MGQPC population
Required for the trained models to generalize to the target catalogue.
domain assumption Photometric-redshift consistency indicates high probability of physical association
Used to select the 185 candidates from the MGQPC list.

pith-pipeline@v0.9.0 · 5570 in / 1618 out tokens · 43192 ms · 2026-05-12T04:45:40.671845+00:00 · methodology

discussion (0)

Reference graph

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