TY - JOUR
T1 - The PAU survey: Star-galaxy classification with multi narrow-band data
AU - Cabayol, L.
AU - Sevilla-Noarbe, I.
AU - Fernández, E.
AU - Carretero, J.
AU - Eriksen, M.
AU - Serrano, S.
AU - Alarcón, A.
AU - Amara, A.
AU - Casas, R.
AU - Castander, F. J.
AU - De Vicente, J.
AU - Folger, M.
AU - García-Bellido, J.
AU - Gaztanaga, E.
AU - Hoekstra, H.
AU - Miquel, R.
AU - Padilla, C.
AU - Sánchez, E.
AU - Stothert, L.
AU - Tallada, P.
AU - Tortorelli, L.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - © 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. 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.
AB - © 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. 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.
KW - Methods: data analysis
KW - Techniques: photometric
U2 - 10.1093/mnras/sty3129
DO - 10.1093/mnras/sty3129
M3 - Article
SN - 0035-8711
VL - 483
SP - 529
EP - 539
JO - Monthly Notices of the Royal Astronomical Society
JF - Monthly Notices of the Royal Astronomical Society
ER -