Machine-learning identification of galaxies in the WISExSuperCOSMOS
  all-sky catalogue
  The two currently largest all-sky photometric datasets, WISE and SuperCOSMOS,
were cross-matched by Bilicki et al. (2016) (B16) to construct a novel
photometric redshift catalogue on 70% of the sky. Galaxies were therein
separated from stars and quasars through colour cuts, which may leave
imperfections because of mixing different source types which overlap in colour
space. The aim of the present work is to identify galaxies in the
WISExSuperCOSMOS catalogue through an alternative approach of machine learning.
This allows us to define more complex separations in the multi-colour space
than possible with simple colour cuts, and should provide more reliable source
classification. For the automatised classification we use the support vector
machines learning algorithm, employing SDSS spectroscopic sources cross-matched
with WISExSuperCOSMOS as the training and verification set. We perform 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 apply the classifier to the full-sky data and analyse the resulting
catalogue of candidate galaxies. We also compare thus produced dataset with the
one presented in B16. The tests indicate very high accuracy, completeness and
purity (>95%) of the classifier at the bright end, deteriorating for the
faintest sources, but still retaining acceptable levels of 85%. No significant
variation of classification quality with Galactic latitude is observed.
Application of the classifier to all-sky WISExSuperCOSMOS data gives 15 million
galaxies after masking problematic areas. The resulting sample is purer than
the one in B16, at a price of lower completeness over the sky. The automatic
classification gives a successful alternative approach to defining a reliable
galaxy sample as compared to colour cuts.
