The PAU survey: star-galaxy classification with multi narrow-band data
Carretero, J.; Castander, F. J.; García-Bellido, J.; Gaztanaga, E.; Miquel, R.; Sevilla-Noarbe, I.; Hoekstra, H.; Serrano, S.; Amara, A.; Padilla, C.; Tortorelli, L.; de Vicente, J.; Fernández, E.; Cabayol, L.; Eriksen, M.; Casas, R.; Sánchez, E.; Tallada, P.; Alarcón, A.; Folger, M.; Stothert, L.
Spain, Switzerland, Netherlands, United Kingdom
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{{ per cent}} purity with a completeness of 98.8{{ 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.