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.
The AKARI All-Sky Survey provided the first bright point source catalog detected at 90um. Starting from this catalog, we selected galaxies by matching AKARI sources with those in the IRAS PSCz. Next, we have measured total GALEX FUV and NUV flux dens
ities. Then, we have matched this sample with SDSS and 2MASS galaxies. By this procedure, we obtained the final sample which consists of 607 galaxies. If we sort the sample with respect to 90um, their average SED shows a coherent trend: the more luminous at 90um, the redder the global SED becomes. The M_r--NUV-r color-magnitude relation of our sample does not show bimodality, and the distribution is centered on the green valley between the blue cloud and red sequence seen in optical surveys. We have established formulae to convert FIR luminosity from AKARI bands to the total infrared (IR) luminosity L_TIR. With these formulae, we calculated the star formation directly visible with FUV and hidden by dust. The luminosity related to star formation activity (L_SF) is dominated by L_TIR even if we take into account the far-infrared (FIR) emission from dust heated by old stars. At high star formation rate (SFR) (> 20 Msun yr^-1), the fraction of directly visible SFR, SFR_FUV, decreases. We also estimated the FUV attenuation A_FUV from FUV-to-total IR (TIR) luminosity ratio. We also examined the L_TIR/L_FUV-UV slope (FUV- NUV) relation. The majority of the sample has L_TIR/L_FUV ratios 5 to 10 times lower than expected from the local starburst relation, while some LIRGs and all the ULIRGs of this sample have higher L_TIR/L_FUV ratios. We found that the attenuation indicator L_TIR/L_FUV is correlated to the stellar mass of galaxies, M*, but there is no correlation with specific SFR (SSFR), SFR/M*, and dust attenuation L_TIR/L_FUV. (abridged)
