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

L. Cabayol*, I. Sevilla-Noarbe, E. Fernández, J. Carretero, M. Eriksen, S. Serrano, A. Alarcón, A. Amara, R. Casas, F. J. Castander, J. De Vicente, M. Folger, J. García-Bellido, E. Gaztanaga, H. Hoekstra, R. Miquel, C. Padilla, E. Sánchez, L. Stothert, P. TalladaL. Tortorelli

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)529-539
Number of pages11
JournalMonthly Notices of the Royal Astronomical Society
Issue number1
Early online date18 Nov 2018
Publication statusPublished - Feb 2019


  • Methods: data analysis
  • Techniques: photometric


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