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arXiv · 1806.08545

The PAU Survey: star-galaxy classification with multi narrow-band data

Laura Cabayol·Ignacio Sevilla-Noarbe·Enrique Fernández·Jorge Carretero·Martin Eriksen·Santiago Serrano·Alex Alarcón·Adam Amara·Ricard Casas·Francisco Javier Castander·Juan de Vicente·Martin Folger·Juan García-Bellido·Enrique Gaztanaga·Henk Hoekstra·Ramon Miquel·Cristobal Padilla·Eusebio Sánchez·Lee Stothert·Pau Tallada·Luca Tortorelli
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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 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.

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Laura Cabayol, Ignacio Sevilla-Noarbe, Enrique Fernández, Jorge Carretero, Martin Eriksen, Santiago Serrano, Alex Alarcón, Adam Amara, Ricard Casas, Francisco Javier Castander, Juan de Vicente, Martin Folger, Juan García-Bellido, Enrique Gaztanaga, Henk Hoekstra, Ramon Miquel, Cristobal Padilla, Eusebio Sánchez, Lee Stothert, Pau Tallada, Luca Tortorelli. 2018-06-22. The PAU Survey: star-galaxy classification with multi narrow-band data. https://doi.org/10.1093/mnras/sty3129

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