The miniJPAS survey quasar selection

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 f 1 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 f 1 = 0.88 and f 1 = 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.

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Publication Details

Journal
Astronomy and Astrophysics
Published
2025-12-04
DOI
https://doi.org/10.1051/0004-6361/202556424
Citations
1
Primary Topic
Galaxies: Formation, Evolution, Phenomena
Type
article
Field-Weighted Citation Impact
1.15

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article

The miniJPAS survey quasar selection

J. S. Alcaniz, Renato Dupke, Vítor Rodrigues, A. Ederoclite et al.
1 citations
Astronomy and Astrophysics
Galaxies: Formation, Evolution, Phenomena
1.15
article

The miniJPAS survey quasar selection

J. S. Alcaniz, Renato Dupke, Vítor Rodrigues, A. Ederoclite, Matthew M. Pieri, Silvia Bonoli, S. Carneiro, Siddhartha Gurung-López, G. Martínez-Solaeche, L. Sodré, Ignasi Pérez-Ràfols, L. Raul Abramo, A. Marín-Franch, C. López-Sanjuán, D. Cristóbal-Hornillos, J. Varela, N. Benı́tez, R. M. González Delgado, A. Hernán-Caballero, C. Hernández–Monteagudo, H. Vázquez Ramió, Valerio Marra, Keith F. Taylor, M. Moles, Javier Cenarro, Claudia Mendes de Oliveira, Marina Burjalès-del-Amo, Maria Escolà-Gallinat, Montserrat Ferré-Abad, Mireia Isern-Vizoso
article en
1 citations

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 f 1 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 f 1 = 0.88 and f 1 = 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.

Astronomy and AstrophysicsVol. 705
Universidad de La Laguna (ES), Universitat de València (ES), Universidade de São Paulo (BR), Institute for High Energy Physics (ES), Aix-Marseille Université (FR), Trieste Astronomical Observatory (IT), California Earthquake Authority (US), Valongo Observatory (BR), Centro de Estudios de Física del Cosmos de Aragón (ES), Instituto de Astrofísica de Canarias (ES), Institute for Fundamental Physics of the Universe (IT), Instituto de Geofísica y Astronomía (CU), Astronomical Observatory (RS), Instituto de Astrofísica de Andalucía (ES), Donostia International Physics Center (ES), Universidade Federal do Espírito Santo (BR), Universitat Politècnica de Catalunya (ES)
Ministerio de Ciencia, Innovación y Universidades, European Commission, Agence Nationale de la Recherche, Fundação de Amparo à Pesquisa do Estado de São Paulo, Centre National d’Etudes Spatiales, Ministerio de Economía y Competitividad, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Centre National de la Recherche Scientifique, Financiadora de Estudos e Projetos, Ministerio de Ciencia e Innovación, Fundação de Amparo à Pesquisa e Inovação do Espírito Santo, European Regional Development Fund, Agencia Estatal de Investigación, Institut National de Physique Nucléaire et de Physique des Particules
Openalex Percentile: Top 14%
Galaxies: Formation, Evolution, Phenomena
1.15
DOIOpen Access PDFOpenAlex
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