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    Rights statement: The final, definitive version of this article has been published in the Journal, Astronomy and Astrophysics, 645, 2021, © EDP Sciences.

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The miniJPAS survey: star-galaxy classification using machine learning

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The miniJPAS survey : star-galaxy classification using machine learning. / Baqui, P. O.; Marra, V.; Casarini, L. et al.

In: Astronomy and Astrophysics, Vol. 645, A87, 18.01.2021.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Baqui, PO, Marra, V, Casarini, L, Angulo, R, Díaz-García, LA, Hernández-Monteagudo, C, Lopes, PAA, López-Sanjuan, C, Muniesa, D, Placco, VM, Quartin, M, Queiroz, C, Sobral, D, Solano, E, Tempel, E, Varela, J, Vílchez, JM, Abramo, R, Alcaniz, J, Benitez, N, Bonoli, S, Carneiro, S, Cenarro, J, Cristóbal-Hornillos, D, Amorim, ALD, Oliveira, CMD, Dupke, R, Ederoclite, A, Delgado, RMG, Marín-Franch, A, Moles, M, Ramió, HV & Sodré, L 2021, 'The miniJPAS survey: star-galaxy classification using machine learning', Astronomy and Astrophysics, vol. 645, A87. https://doi.org/10.1051/0004-6361/202038986

APA

Baqui, P. O., Marra, V., Casarini, L., Angulo, R., Díaz-García, L. A., Hernández-Monteagudo, C., Lopes, P. A. A., López-Sanjuan, C., Muniesa, D., Placco, V. M., Quartin, M., Queiroz, C., Sobral, D., Solano, E., Tempel, E., Varela, J., Vílchez, J. M., Abramo, R., Alcaniz, J., ... Sodré, L. (2021). The miniJPAS survey: star-galaxy classification using machine learning. Astronomy and Astrophysics, 645, [A87]. https://doi.org/10.1051/0004-6361/202038986

Vancouver

Baqui PO, Marra V, Casarini L, Angulo R, Díaz-García LA, Hernández-Monteagudo C et al. The miniJPAS survey: star-galaxy classification using machine learning. Astronomy and Astrophysics. 2021 Jan 18;645:A87. doi: 10.1051/0004-6361/202038986

Author

Baqui, P. O. ; Marra, V. ; Casarini, L. et al. / The miniJPAS survey : star-galaxy classification using machine learning. In: Astronomy and Astrophysics. 2021 ; Vol. 645.

Bibtex

@article{924f19b07be7437ab9fe1a155b8375b9,
title = "The miniJPAS survey: star-galaxy classification using machine learning",
abstract = " Future astrophysical surveys such as J-PAS will produce very large datasets, which will require the deployment of accurate and efficient Machine Learning (ML) methods. In this work, we analyze the miniJPAS survey, which observed about 1 deg2 of the AEGIS field with 56 narrow-band filters and 4 ugri broad-band filters. We discuss the classification of miniJPAS sources into extended (galaxies) and point-like (e.g. stars) objects, a necessary step for the subsequent scientific analyses. We aim at developing an ML classifier that is complementary to traditional tools based on explicit modeling. In order to train and test our classifiers, we crossmatched the miniJPAS dataset with SDSS and HSC-SSP data. We trained and tested 6 different ML algorithms on the two crossmatched catalogs. As input for the ML algorithms we use the magnitudes from the 60 filters together with their errors, with and without the morphological parameters. We also use the mean PSF in the r detection band for each pointing. We find that the RF and ERT algorithms perform best in all scenarios. When analyzing the full magnitude range of 1521). We use our best classifiers, with and without morphology, in order to produce a value added catalog available at https://j-pas.org/datareleases . ",
keywords = "astro-ph.IM",
author = "Baqui, {P. O.} and V. Marra and L. Casarini and R. Angulo and D{\'i}az-Garc{\'i}a, {L. A.} and C. Hern{\'a}ndez-Monteagudo and Lopes, {P. A. A.} and C. L{\'o}pez-Sanjuan and D. Muniesa and Placco, {V. M.} and M. Quartin and C. Queiroz and D. Sobral and E. Solano and E. Tempel and J. Varela and V{\'i}lchez, {J. M.} and R. Abramo and J. Alcaniz and N. Benitez and S. Bonoli and S. Carneiro and J. Cenarro and D. Crist{\'o}bal-Hornillos and Amorim, {A. L. de} and Oliveira, {C. M. de} and R. Dupke and A. Ederoclite and Delgado, {R. M. Gonz{\'a}lez} and A. Mar{\'i}n-Franch and M. Moles and Rami{\'o}, {H. V{\'a}zquez} and L. Sodr{\'e}",
note = "The final, definitive version of this article has been published in the Journal, Astronomy and Astrophysics, 645, 2021, {\textcopyright} EDP Sciences. ",
year = "2021",
month = jan,
day = "18",
doi = "10.1051/0004-6361/202038986",
language = "English",
volume = "645",
journal = "Astronomy and Astrophysics",
issn = "1432-0746",
publisher = "EDP Sciences",

}

RIS

TY - JOUR

T1 - The miniJPAS survey

T2 - star-galaxy classification using machine learning

AU - Baqui, P. O.

AU - Marra, V.

AU - Casarini, L.

AU - Angulo, R.

AU - Díaz-García, L. A.

AU - Hernández-Monteagudo, C.

AU - Lopes, P. A. A.

AU - López-Sanjuan, C.

AU - Muniesa, D.

AU - Placco, V. M.

AU - Quartin, M.

AU - Queiroz, C.

AU - Sobral, D.

AU - Solano, E.

AU - Tempel, E.

AU - Varela, J.

AU - Vílchez, J. M.

AU - Abramo, R.

AU - Alcaniz, J.

AU - Benitez, N.

AU - Bonoli, S.

AU - Carneiro, S.

AU - Cenarro, J.

AU - Cristóbal-Hornillos, D.

AU - Amorim, A. L. de

AU - Oliveira, C. M. de

AU - Dupke, R.

AU - Ederoclite, A.

AU - Delgado, R. M. González

AU - Marín-Franch, A.

AU - Moles, M.

AU - Ramió, H. Vázquez

AU - Sodré, L.

N1 - The final, definitive version of this article has been published in the Journal, Astronomy and Astrophysics, 645, 2021, © EDP Sciences.

PY - 2021/1/18

Y1 - 2021/1/18

N2 - Future astrophysical surveys such as J-PAS will produce very large datasets, which will require the deployment of accurate and efficient Machine Learning (ML) methods. In this work, we analyze the miniJPAS survey, which observed about 1 deg2 of the AEGIS field with 56 narrow-band filters and 4 ugri broad-band filters. We discuss the classification of miniJPAS sources into extended (galaxies) and point-like (e.g. stars) objects, a necessary step for the subsequent scientific analyses. We aim at developing an ML classifier that is complementary to traditional tools based on explicit modeling. In order to train and test our classifiers, we crossmatched the miniJPAS dataset with SDSS and HSC-SSP data. We trained and tested 6 different ML algorithms on the two crossmatched catalogs. As input for the ML algorithms we use the magnitudes from the 60 filters together with their errors, with and without the morphological parameters. We also use the mean PSF in the r detection band for each pointing. We find that the RF and ERT algorithms perform best in all scenarios. When analyzing the full magnitude range of 1521). We use our best classifiers, with and without morphology, in order to produce a value added catalog available at https://j-pas.org/datareleases .

AB - Future astrophysical surveys such as J-PAS will produce very large datasets, which will require the deployment of accurate and efficient Machine Learning (ML) methods. In this work, we analyze the miniJPAS survey, which observed about 1 deg2 of the AEGIS field with 56 narrow-band filters and 4 ugri broad-band filters. We discuss the classification of miniJPAS sources into extended (galaxies) and point-like (e.g. stars) objects, a necessary step for the subsequent scientific analyses. We aim at developing an ML classifier that is complementary to traditional tools based on explicit modeling. In order to train and test our classifiers, we crossmatched the miniJPAS dataset with SDSS and HSC-SSP data. We trained and tested 6 different ML algorithms on the two crossmatched catalogs. As input for the ML algorithms we use the magnitudes from the 60 filters together with their errors, with and without the morphological parameters. We also use the mean PSF in the r detection band for each pointing. We find that the RF and ERT algorithms perform best in all scenarios. When analyzing the full magnitude range of 1521). We use our best classifiers, with and without morphology, in order to produce a value added catalog available at https://j-pas.org/datareleases .

KW - astro-ph.IM

U2 - 10.1051/0004-6361/202038986

DO - 10.1051/0004-6361/202038986

M3 - Journal article

VL - 645

JO - Astronomy and Astrophysics

JF - Astronomy and Astrophysics

SN - 1432-0746

M1 - A87

ER -