期刊
ASTRONOMY & ASTROPHYSICS
卷 596, 期 -, 页码 -出版社
EDP SCIENCES S A
DOI: 10.1051/0004-6361/201629165
关键词
methods: data analysis; methods: numerical; astronomical databases: miscellaneous; galaxies: statistics; large-scale structure of Universe
资金
- National Aeronautics and Space Administration
- UK Science and Technology Facilities Council
- Alfred P. Sloan Foundation
- National Science Foundation
- University of Arizona
- Brazilian Participation Group
- Brookhaven National Laboratory
- Carnegie Mellon University
- University of Florida
- French Participation Group
- German Participation Group
- Harvard University
- Instituto de Astrofisica de Canarias
- Michigan State/Notre Dame/JINA Participation Group
- Johns Hopkins University
- Lawrence Berkeley National Laboratory
- Max Planck Institute for Astrophysics
- Max Planck Institute for Extraterrestrial Physics
- New Mexico State University
- New York University
- Ohio State University
- Pennsylvania State University
- University of Portsmouth
- Princeton University
- Spanish Participation Group
- University of Tokyo
- University of Utah
- Vanderbilt University
- University of Virginia
- University of Washington
- Yale University
- Polish National Science Center [UMO-2012/07/D/ST9/02785]
- National Science Centre [UMO-2012/07/B/ST9/04425, UMO-2013/09/D/ST9/04030]
- Polish-Swiss Astro Project
- European Associated Laboratory Astrophysics Poland-France HECOLS
- Netherlands Organization for Scientific Research, NWO [614.001.451]
- European Research Council [279396]
- Swiss Contribution to the enlarged European Union
- US Department of Energy Office of Science
Context. The two currently largest all-sky photometric datasets, WISE and SuperCOSMOS, have been recently cross-matched to construct a novel photometric redshift catalogue on 70% of the sky. Galaxies were separated from stars and quasars through colour cuts, which may leave imperfections because different source types may overlap in colour space. Aims. The aim of the present work is to identify galaxies in the WISE x SuperCOSMOS catalogue through an alternative approach of machine learning. This allows us to define more complex separations in the multi-colour space than is possible with simple colour cuts, and should provide a more reliable source classification. Methods. For the automatised classification we used the support vector machines (SVM) learning algorithm and employed SDSS spectroscopic sources that we cross-matched with WISE x SuperCOSMOS to construct the training and verification set. We performed a number of tests to examine the behaviour of the classifier (completeness, purity, and accuracy) as a function of source apparent magnitude and Galactic latitude. We then applied the classifier to the full-sky data and analysed the resulting catalogue of candidate galaxies. We also compared the resulting dataset with the one obtained through colour cuts. Results. The tests indicate very high accuracy, completeness, and purity (>95%) of the classifier at the bright end; this deteriorates for the faintest sources, but still retains acceptable levels of similar to 85%. No significant variation in the classification quality with Galactic latitude is observed. When we applied the classifier to all-sky WISE x SuperCOSMOS data, we found 15 million galaxies after masking problematic areas. The resulting sample is purer than the one produced by applying colour cuts, at the price of a lower completeness across the sky. Conclusions. The automatic classification is a successful alternative approach to colour cuts for defining a reliable galaxy sample. The identifications we obtained are included in the public release of the WISE x SuperCOSMOS galaxy catalogue.
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