{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/catalog-of-quasars-from-the-kilo-degree","title":"Catalog of quasars from the Kilo-Degree Survey Data Release 3","arxiv_id":"1812.03084","date":"2018-12-07","proceeding":null,"authors":["S. Nakoneczny","M. Bilicki","A. Solarz","A. Pollo","N. Maddox","C. Spiniello","M. Brescia","N. R. Napolitano"],"abstract":"We present a catalog of quasars selected from broad-band photometric ugri\ndata of the Kilo-Degree Survey Data Release 3 (KiDS DR3). The QSOs are\nidentified by the random forest (RF) supervised machine learning model, trained\non SDSS DR14 spectroscopic data. We first cleaned the input KiDS data from\nentries with excessively noisy, missing or otherwise problematic measurements.\nApplying a feature importance analysis, we then tune the algorithm and identify\nin the KiDS multiband catalog the 17 most useful features for the\nclassification, namely magnitudes, colors, magnitude ratios, and the stellarity\nindex. We used the t-SNE algorithm to map the multi-dimensional photometric\ndata onto 2D planes and compare the coverage of the training and inference\nsets. We limited the inference set to r<22 to avoid extrapolation beyond the\nfeature space covered by training, as the SDSS spectroscopic sample is\nconsiderably shallower than KiDS. This gives 3.4 million objects in the final\ninference sample, from which the random forest identified 190,000 quasar\ncandidates. Accuracy of 97%, purity of 91%, and completeness of 87%, as derived\nfrom a test set extracted from SDSS and not used in the training, are confirmed\nby comparison with external spectroscopic and photometric QSO catalogs\noverlapping with the KiDS footprint. The robustness of our results is\nstrengthened by number counts of the quasar candidates in the r band, as well\nas by their mid-infrared colors available from WISE. An analysis of parallaxes\nand proper motions of our QSO candidates found also in Gaia DR2 suggests that a\nprobability cut of p(QSO)>0.8 is optimal for purity, whereas p(QSO)>0.7 is\npreferable for better completeness. Our study presents the first comprehensive\nquasar selection from deep high-quality KiDS data and will serve as the basis\nfor versatile studies of the QSO population detected by this survey.","url_abs":"http://arxiv.org/abs/1812.03084v2","url_pdf":"http://arxiv.org/pdf/1812.03084v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"catalog-of-quasars-from-the-kilo-degree","repo_url":"https://github.com/snakoneczny/kids-quasars","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"feature-importance","task_name":"Feature Importance"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}