The PAU survey: star–galaxy classification with multi narrow-band data. Issue 1 (18th November 2018) - Cabayol, L; Sevilla-Noarbe, I; Fernández, E; Carretero, J; Eriksen, M; Serrano, S; Alarcón, A; Amara, A; Casas, R; Castander, F J; de Vicente, J; Folger, M; et al.
ABSTRACT: Classification of stars and galaxies is a well-known astronomical problem that has been treated using different approaches, most of them relying on morphological information. In this paper, we tackle this issue using the low-resolution spectra from narrow-band photometry, provided by the Physics of the Accelerating Universe survey. We find that, with the photometric fluxes from the 40 narrow-band filters and without including morphological information, it is possible to separate stars and galaxies to very high precision, $98.4{{\ \rm per\ cent}}$ purity with a completeness of$98.8{{\ \rm per\ cent}}$ for objects brighter than I  = 22.5. This precision is obtained with a convolutional neural network as a classification algorithm, applied to the objects' spectra. We have also applied the method to the ALHAMBRA photometric survey and we provide an updated classification for its Gold sample.