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 PAUS (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% purity with a completeness of 98.8% 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.
@article{arxiv.1806.08545,
title = {The PAU Survey: star-galaxy classification with multi narrow-band data},
author = {Laura Cabayol and Ignacio Sevilla-Noarbe and Enrique Fernández and Jorge Carretero and Martin Eriksen and Santiago Serrano and Alex Alarcón and Adam Amara and Ricard Casas and Francisco Javier Castander and Juan de Vicente and Martin Folger and Juan García-Bellido and Enrique Gaztanaga and Henk Hoekstra and Ramon Miquel and Cristobal Padilla and Eusebio Sánchez and Lee Stothert and Pau Tallada and Luca Tortorelli},
journal= {arXiv preprint arXiv:1806.08545},
year = {2018}
}
Comments
13 pages, 10 figures, the catalog with the ALHAMBRA classification is available at http://cosmohub.pic.es