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. Tallada; L. Tortorelli

doi:10.1093/mnras/sty3129

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

Institute of Cosmology & Gravitation

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	529-539
Number of pages	11
Journal	Monthly Notices of the Royal Astronomical Society
Volume	483
Issue number	1
Early online date	18 Nov 2018
DOIs	https://doi.org/10.1093/mnras/sty3129
Publication status	Published - Feb 2019

Keywords

Methods: data analysis
Techniques: photometric

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10.1093/mnras/sty3129

Data availability statement for 'The PAU survey: star-galaxy classification with multi narrow-band data'.
Amara, A. (Creator), Oxford University Press, 18 Nov 2018
Dataset

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Cabayol, L., Sevilla-Noarbe, I., Fernández, E., Carretero, J., Eriksen, M., Serrano, S., Alarcón, A., Amara, A., Casas, R., Castander, F. J., De Vicente, J., Folger, M., García-Bellido, J., Gaztanaga, E., Hoekstra, H., Miquel, R., Padilla, C., Sánchez, E., Stothert, L., ... Tortorelli, L. (2019). The PAU survey: star-galaxy classification with multi narrow-band data. Monthly Notices of the Royal Astronomical Society, 483(1), 529-539. https://doi.org/10.1093/mnras/sty3129

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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 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.",

keywords = "Methods: data analysis, Techniques: photometric",

author = "L. Cabayol and I. Sevilla-Noarbe and E. Fern{\'a}ndez and J. Carretero and M. Eriksen and S. Serrano and A. Alarc{\'o}n and A. Amara and R. Casas and Castander, {F. J.} and {De Vicente}, J. and M. Folger and J. Garc{\'i}a-Bellido and E. Gaztanaga and H. Hoekstra and R. Miquel and C. Padilla and E. S{\'a}nchez and L. Stothert and P. Tallada and L. Tortorelli",

year = "2019",

month = feb,

doi = "10.1093/mnras/sty3129",

language = "English",

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Cabayol, L, Sevilla-Noarbe, I, Fernández, E, Carretero, J, Eriksen, M, Serrano, S, Alarcón, A, Amara, A, Casas, R, Castander, FJ, De Vicente, J, Folger, M, García-Bellido, J, Gaztanaga, E, Hoekstra, H, Miquel, R, Padilla, C, Sánchez, E, Stothert, L, Tallada, P & Tortorelli, L 2019, 'The PAU survey: star-galaxy classification with multi narrow-band data', Monthly Notices of the Royal Astronomical Society, vol. 483, no. 1, pp. 529-539. https://doi.org/10.1093/mnras/sty3129

TY - JOUR

T1 - The PAU survey

T2 - 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 - 2019/2

Y1 - 2019/2

N2 - 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 - 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

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U2 - 10.1093/mnras/sty3129

DO - 10.1093/mnras/sty3129

M3 - Article

AN - SCOPUS:85065828323

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

IS - 1

ER -

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

Abstract

Keywords

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Datasets

Data availability statement for 'The PAU survey: star-galaxy classification with multi narrow-band data'.

Cite this