Star-galaxy separation in the AKARI NEP Deep Field
  Context: It is crucial to develop a method for classifying objects detected
in deep surveys at infrared wavelengths. We specifically need a method to
separate galaxies from stars using only the infrared information to study the
properties of galaxies, e.g., to estimate the angular correlation function,
without introducing any additional bias. Aims. We aim to separate stars and
galaxies in the data from the AKARI North Ecliptic Pole (NEP) Deep survey
collected in nine AKARI / IRC bands from 2 to 24 {\mu}m that cover the near-
and mid-infrared wavelengths (hereafter NIR and MIR). We plan to estimate the
correlation function for NIR and MIR galaxies from a sample selected according
to our criteria in future research. Methods: We used support vector machines
(SVM) to study the distribution of stars and galaxies in the AKARIs multicolor
space. We defined the training samples of these objects by calculating their
infrared stellarity parameter (sgc). We created the most efficient classifier
and then tested it on the whole sample. We confirmed the developed separation
with auxiliary optical data obtained by the Subaru telescope and by creating
Euclidean normalized number count plots. Results: We obtain a 90% accuracy in
pinpointing galaxies and 98% accuracy for stars in infrared multicolor space
with the infrared SVM classifier. The source counts and comparison with the
optical data (with a consistency of 65% for selecting stars and 96% for
galaxies) confirm that our star/galaxy separation methods are reliable.
Conclusions: The infrared classifier derived with the SVM method based on
infrared sgc- selected training samples proves to be very efficient and
accurate in selecting stars and galaxies in deep surveys at infrared
wavelengths carried out without any previous target object selection.
