The miniJPAS survey quasar selection

V. Combined algorithm

¹ Departament de Física, EEBE, Universitat Politècnica de Catalunya, c/Eduard Maristany 10, 08930 Barcelona, Spain
² Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, 08193 Bellaterra Barcelona, Spain
³ Departamento de Física Matemática, Instituto de Física, Universidade de São Paulo, Rua do Matão 1371, CEP 05508-090, São Paulo, Brazil
⁴ Instituto de Astrofísica de Andalucía (CSIC), PO Box 3004, 18080 Granada, Spain
⁵ Aix Marseille Univ, CNRS, CNES, LAM, Marseille, France
⁶ Observatório Nacional, Rua General José Cristino, 77, São Cristóvão, 20921-400 Rio de Janeiro, RJ, Brazil
⁷ Donostia International Physics Center (DIPC), Manuel Lardizabal Ibilbidea, 4, San Sebastián, Spain
⁸ Centro de Estudios de Física del Cosmos de Aragón (CEFCA), Plaza San Juan, 1, 44001 Teruel, Spain
⁹ Unidad Asociada CEFCA-IAA, CEFCA, Unidad Asociada al CSIC por el IAA y el IFCA, Plaza San Juan 1, 44001 Teruel, Spain
¹⁰ Departamento de Física, Universidade Federal do Espírito Santo, 29075-910 Vitória, ES, Brazil
¹¹ INAF – Osservatorio Astronomico di Trieste, via Tiepolo 11, 34131 Trieste, Italy
¹² IFPU – Institute for Fundamental Physics of the Universe, via Beirut 2, 34151 Trieste, Italy
¹³ Observatori Astronòmic de la Universitat de València, Ed. Instituts d’Investigació, Parc Científic. C/ Catedrático José Beltrán, n2, 46980 Paterna, Valencia, Spain
¹⁴ Departament d’Astronomia i Astrofísica, Universitat de València, 46100 Burjassot, Spain
¹⁵ Instituto de Astrofísica de Canarias, C/ Vía Láctea, s/n, 38205 La Laguna, Tenerife, Spain
¹⁶ Universidad de La Laguna, Avda Francisco Sánchez, 38206 San Cristóbal de La Laguna, Tenerife, Spain
¹⁷ Departamento de Astronomia, Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, São Paulo, Brazil
¹⁸ Instruments4, 4121 Pembury Place, La Canada Flintridge, CA 91011, USA

^★ Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 15 July 2025
Accepted: 7 November 2025

Abstract

Aims. Quasar catalogues from narrow-band photometric data are used in a variety of applications, including targeting for spectroscopic follow-up, measurements of supermassive black hole masses, or baryon acoustic oscillations. Here, we present the final quasar catalogue, including redshift estimates, from the miniJPAS Data Release constructed using several flavours of machine-learning algorithms.

Methods. In this work, we use a machine learning algorithm to classify quasars, optimally combining the output of eight individual algorithms. We assess the relative importance of the different classifiers. We include results from three different redshift estimators to also provide improved photometric redshifts. We compare our final catalogue against both simulated data and real spectroscopic data. Our main comparison metric is the f₁ score, which balances the catalogue purity and completeness.

Results. We evaluate the performance of the combined algorithm using synthetic data. In this scenario, the combined algorithm out-performs the rest of the codes, reaching f₁ = 0.88 and f₁ = 0.79 for high- and low-z quasars (with z ≥ 2.1 and z < 2.1, respectively) down to magnitude r = 23.6. We further evaluate its performance against real spectroscopic data, finding different performances (some of the codes show a better performance, some a worse one, and the combined algorithm does not outperform the rest). We conclude that our simulated data are not realistic enough and that a new version of the mocks would improve the performance. Our redshift estimates on mocks suggest a typical uncertainty of σ_NMAD = 0.11, which, according to our results with real data, could be significantly smaller (as low as σ_NMAD = 0.02). We note that the data sample is still not large enough for a full statistical consideration.