{
  "abstract": "We present two complementary machine-readable catalogs for quasar (QSO) candidate selection in the Kilo-Degree Survey (KiDS) DR5. The KiDS-only catalog covers the full survey source sample and provides optical/near-infrared-based classifications together with QSO photometric redshift estimates derived without Wide-field Infrared Survey Explorer (WISE) information. The KiDS–AllWISE catalog is restricted to sources with AllWISE counterparts and provides WISE-assisted classifications and photometric redshifts. Separate stacking ensembles are trained to distinguish galaxies, stars, and QSOs. On a stratified held-out test set, the WISE-assisted classifier achieves an overall accuracy of 98.34%, a macroaveraged area under the receiver operating characteristic curve (ROC AUC) of 99.78%, and a QSO F1-score of 97.09%. Spatially disjoint R.A.-block validation yields QSO F1-scores of 95.87%–96.31%, indicating only modest spatial degradation. Feature-ablation tests show that W1/W2-based mid-infrared information provides the largest gain in QSO recovery, whereas Bayesian photometric redshift (BPZ)-derived quantities have only a minor effect. For QSO photometric redshift estimation, we train a LightGBM–XGBoost ensemble on Sloan Digital Sky Survey (SDSS) DR16 Quasar Catalog (DR16Q) quasars with valid optical and WISE photometry. On 94,057 held-out quasars, the WISE-assisted model achieves σ NMAD = 0.0633 and a catastrophic-outlier fraction of η = 12.0%; excluding WISE features degrades these values to σ NMAD = 0.0948 and η = 26.8%. Both catalogs provide probability-ranked QSO candidates and thresholded p Q samples for different follow-up priorities. As the candidates are not spectroscopically confirmed, statistical applications require independent calibration of the corresponding selection functions.",
  "abstract_shown": true,
  "affiliations": [
    "Chinese Academy of Sciences",
    "University of Southern California"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Sen Pang",
      "orcid": "0009-0003-5307-6391"
    },
    {
      "affs": [
        0
      ],
      "name": "Yanxia Zhang",
      "orcid": "0000-0002-6610-5265"
    },
    {
      "affs": [
        0
      ],
      "name": "Jinghang Shi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dxwzf2q1b2w0f.bib",
  "cited_by": 0,
  "doi": "10.3847/1538-4365/aea41d",
  "fulltext_url": null,
  "id": "dxwzf2q1b2w0f",
  "issns": [
    "0067-0049",
    "1538-4365"
  ],
  "landing_url": "https://iopscience.iop.org/article/10.3847/1538-4365/aea41d",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3103|Galaxies: Formation, Evolution, Phenomena"
    ]
  ],
  "openalex": "W7222913549",
  "pdf_url": "https://iopscience.iop.org/article/10.3847/1538-4365/aea41d/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": "American Astronomical Society",
  "ris": "https://research.draconitic.com/paper/dxwzf2q1b2w0f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:15Z",
      "native_id": "10.3847/1538-4365/aea41d",
      "source": "crossref",
      "url": "https://iopscience.iop.org/article/10.3847/1538-4365/aea41d"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "openalex:3103|Galaxies: Formation, Evolution, Phenomena",
      "subfield": "galaxies"
    }
  ],
  "title": "A Machine Learning Quasar Candidate Catalog with Photometric Redshifts from KiDS DR5 and AllWISE",
  "title_text": "A Machine Learning Quasar Candidate Catalog with Photometric Redshifts from KiDS DR5 and AllWISE",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:15Z",
  "url": "https://research.draconitic.com/paper/dxwzf2q1b2w0f",
  "venue": "The Astrophysical Journal Supplement Series",
  "versions": []
}