Towards automatic classification of all WISE sources

Abstract: The WISE satellite has detected hundreds of millions sources over the entire sky. Classifying them reliably is however a challenging task due to degeneracies in WISE multicolour space and low levels of detection in its two longest-wavelength bandpasses. Here we aim at obtaining comprehensive and reliable star, galaxy and quasar catalogues based on automatic source classification in full-sky WISE data. This means that the final classification will employ only parameters available from WISE itself, in particular those reliably measured for a majority of sources. For the automatic classification we applied the support vector machines (SVM) algorithm, which requires a training sample with relevant classes already identified, and we chose to use the SDSS spectroscopic dataset for that purpose. By calibrating the classifier on the test data drawn from SDSS, we first established that a polynomial kernel is preferred over a radial one for this particular dataset. Next, using three classification parameters (W1 magnitude, W1-W2 colour, and a differential aperture magnitude) we obtained very good classification efficiency in all the tests. At the bright end, the completeness for stars and galaxies reaches ~95%, deteriorating to ~80% at W1=16 mag, while for quasars it stays at a level of ~95% independently of magnitude. Similar numbers are obtained for purity. Application of the classifier to full-sky WISE data, flux-limited to 16 mag (Vega) in the 3.4 {\mu}m channel, and appropriate a posteriori cleaning allowed us to obtain reliably-looking catalogues of star and galaxy candidates. However, the sources flagged by the classifier as `quasars' are in fact dominated by dusty galaxies but also exhibit contamination from sources located mainly at low ecliptic latitudes, consistent with Solar System objects. [abridged]