{
  "id": "1405.5298",
  "version": "v2",
  "published": "2014-05-21T04:46:11.000Z",
  "updated": "2015-08-25T02:33:41.000Z",
  "title": "Photometric Classification of quasars from RCS-2 using Random Forest",
  "authors": [
    "D. Carrasco",
    "L. F. Barrientos",
    "K. Pichara",
    "T. Anguita",
    "D. N. A. Murphy",
    "D. G. Gilbank",
    "M. D. Gladders",
    "H. K. C. Yee",
    "B. C. Hsieh",
    "S. López"
  ],
  "comment": "Accepted for publication in A&A. 20 pages, 16 figures, 6 tables. Tables 1, 2 and 3 with the quasar candidates are only available in electronic format http://www.aanda.org/",
  "categories": [
    "astro-ph.GA",
    "astro-ph.CO"
  ],
  "abstract": "Aims. Construction of a new quasar candidate catalog from the Red-Sequence Cluster Survey 2 (RCS-2), identified solely from photometric information using an automated algorithm suitable for large surveys. The algorithm performance is tested using a well-defined SDSS spectroscopic sample of quasars and stars. Methods. The Random Forest algorithm constructs the catalog from RCS-2 point sources using SDSS spectroscopically-confirmed stars and quasars. The algorithm identifies putative quasars from broadband magnitudes (g, r, i, z) and colours. Exploiting NUV GALEX measurements for a subset of the objects, we refine the classifier by adding new information. An additional subset of the data with WISE W1 and W2 bands is also studied. Results. Upon analyzing 542,897 RCS-2 point sources, the algorithm identified 21,501 quasar candidates, with a training-set-derived precision (the fraction of true positives within the group assigned quasar status) of 89.5% and recall (the fraction of true positives relative to all sources that actually are quasars) of 88.4%. These performance metrics improve for the GALEX subset; 6,530 quasar candidates are identified from 16,898 sources, with a precision and recall respectively of 97.0% and 97.5%. Algorithm performance is further improved when WISE data are included, with precision and recall increasing to 99.3% and 99.1% respectively for 21,834 quasar candidates from 242,902 sources. We compile our final catalog (38,257) by merging these samples and removing duplicates. An observational follow up of 17 bright (r < 19) candidates with long-slit spectroscopy at DuPont telescope (LCO) yields 14 confirmed quasars. Conclusions. The results signal encouraging progress in the classification of point sources with Random Forest algorithms to search for quasars within current and future large-area photometric surveys.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2014-05-21T04:46:11.000Z",
      "abstract": "We describe the construction of a quasar catalog containing 91,842 candidates derived from analysis of imaging data with a Random Forest algorithm. Using spectroscopically-confirmed stars and quasars from the SDSS as a training set, we blindly search the RCS-2 (~750 deg^2) imaging survey. From a source catalogue of 1,863,970 RCS-2 point sources, our algorithm identifies putative quasars from broadband magnitudes (g, r, i, z) and colours. Exploiting NUV GALEX measurements available for a subset 16,898 of these objects, we refine the classifier by adding NUV-optical colours to the algorithm's search. An additional subset (comprising 13% of the source catalog) features WISE coverage; we explore the effect of including W1 and W2 bands on the performance of the algorithm. Upon analysing all RCS-2 point sources, the algorithm identified 85,085 quasar candidates, with a training-set-derived precision (the fraction of true positives within the group assigned quasar status) of 90.4% and a recall (the fraction of true positives relative to all sources that actually are quasars) of 87.3%. These performance metrics improve for the subset with GALEX data; 6,556 quasar candidates are identified with a precision and recall respectively of 96.9% and 97.3%. Algorithm performance is improved further still with the analysis of WISE data, with precision and recall further increasing to 99.3% and 99.2% respectively for 21,713 quasar candidates. Upon merging these samples and removing duplicates, we arrive our final catalog of 91,842 quasar candidates. An observational follow up of 17 bright r<19 potential quasars with long-slit spectroscopy at DuPont telescope (LCO) yields 13 confirmed quasars. Whilst this preliminary sample is small, it signals encouraging progress in the use of Random Forest algorithms to classify point sources for quasar searches within large-area photometric surveys such as the LSST.",
      "comment": "18 pages, 14 figures",
      "journal": null,
      "doi": null
    },
    {
      "version": "v2",
      "updated": "2015-08-25T02:33:41.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "quasar candidates",
      "photometric classification",
      "point sources",
      "random forest algorithm",
      "true positives"
    ],
    "publication": {
      "doi": "10.1051/0004-6361/201525752"
    },
    "note": {
      "typesetting": "TeX",
      "pages": 20,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "inspire": 1297347,
      "adsabs": "2014arXiv1405.5298C"
    }
  }
}