The miniJPAS survey quasar selection

III. Classification with artificial neural networks and hybridisation

¹ Instituto de Astrofísica de Andaluciá (CSIC), PO Box 3004, 18080 Granada, Spain
e-mail: gimarso@iaa.es
² Departamento de Astronomia, Instituto de Física, Universidade Federal do Rio Grande do Sul (UFRGS), Av. Bento Gonçalves, 9500, Porto Alegre, RS, Brazil
³ Departamento de Física Matemática, Instituto de Física, Universidade de São Paulo, Rua do Matão, 1371, CEP 05508-090, São Paulo, Brazil
⁴ Sorbonne Université, Université Paris-Diderot, CNRS/IN2P3, Laboratoire de Physique Nucléaire et de Hautes Energies, LPNHE, 4 Place Jussieu, 75252 Paris, France
⁵ Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, 08193 Bellaterra (Barcelona), Spain
⁶ Aix-Marseille Univ., CNRS, CNES, LAM, Marseille, France
⁷ Donostia International Physics Center, Paseo Manuel de Lardizabal 4, 20018, Donostia-San Sebastian (Gipuzkoa), Spain
⁸ Centro de Estudios de Física del Cosmos de Aragón (CEFCA), Plaza San Juan, 1, 44001 Teruel, Spain
⁹ Centro de Estudios de Física del Cosmos de Aragón (CEFCA), Unidad Asociada al CSIC, Plaza San Juan, 1, 44001 Teruel, Spain
¹⁰ Donostia-San Sebastian, Spain Ikerbasque, Basque Foundation for Science, 48013 Bilbao, Spain
¹¹ Department of Astronomy, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
¹² INAF, Osservatorio Astronomico di Trieste, via Tiepolo 11, 34131 Trieste, Italy
¹³ IFPU, Institute for Fundamental Physics of the Universe, via Beirut 2, 34151 Trieste, Italy
¹⁴ Observatório Nacional, Rua General José Cristino, 77, São Cristóvão, 20921-400 Rio de Janeiro, Brazil
¹⁵ Department of Astronomy, University of Michigan, 311 West Hall, 1085 South University Ave., Ann Arbor, USA
¹⁶ Department of Physics and Astronomy, University of Alabama, Box 870324, Tuscaloosa, AL, USA
¹⁷ Universidade de São Paulo, Instituto de Astronomia, Geofísica e Ciências Atmosféricas, R. do Matão 1226, 05508-090 São Paulo, Brazil
¹⁸ Instruments4, 4121 Pembury Place, La Cañada-Flintridge, CA 91011, USA

Received: 21 December 2022
Accepted: 14 March 2023

Abstract

This paper is part of large effort within the J-PAS collaboration that aims to classify point-like sources in miniJPAS, which were observed in 60 optical bands over ~1 deg² in the AEGIS field. We developed two algorithms based on artificial neural networks (ANN) to classify objects into four categories: stars, galaxies, quasars at low redshift (z < 2.1), and quasars at high redshift (z ≥ 2.1). As inputs, we used miniJPAS fluxes for one of the classifiers (ANN₁) and colours for the other (ANN₂). The ANNs were trained and tested using mock data in the first place. We studied the effect of augmenting the training set by creating hybrid objects, which combines fluxes from stars, galaxies, and quasars. Nevertheless, the augmentation processing did not improve the score of the ANN. We also evaluated the performance of the classifiers in a small subset of the SDSS DR12Q superset observed by miniJPAS. In the mock test set, the f1-score for quasars at high redshift with the ANN₁ (ANN₂) are 0.99 (0.99), 0.93 (0.92), and 0.63 (0.57) for 17 < r ≤ 20, 20 < r ≤ 22.5, and 22.5 < r ≤ 23.6, respectively, where r is the J-PAS rSDSS band. In the case of low-redshift quasars, galaxies, and stars, we reached 0.97 (0.97), 0.82 (0.79), and 0.61 (0.58); 0.94 (0.94), 0.90 (0.89), and 0.81 (0.80); and 1.0 (1.0), 0.96 (0.94), and 0.70 (0.52) in the same r bins. In the SDSS DR12Q superset miniJPAS sample, the weighted f1-score reaches 0.87 (0.88) for objects that are mostly within 20 < r ≤ 22.5. We find that the most common confusion occurs between quasars at low redshift and galaxies in mocks and miniJPAS data. We discuss the origin of this confusion, and we show examples in which these objects present features that are shared by both classes. Finally, we estimate the number of point-like sources that are quasars, galaxies, and stars in miniJPAS.