The miniJPAS survey quasar selection I: Mock catalogues for classification
| Autor: | Carolina Queiroz, L Raul Abramo, Natália V N Rodrigues, Ignasi Pérez-Ràfols, Ginés Martínez-Solaeche, Antonio Hernán-Caballero, Carlos Hernández-Monteagudo, Alejandro Lumbreras-Calle, Matthew M Pieri, Sean S Morrison, Silvia Bonoli, Jonás Chaves-Montero, Ana L Chies-Santos, L A Díaz-García, Alberto Fernandez-Soto, Rosa M González Delgado, Jailson Alcaniz, Narciso Benítez, A Javier Cenarro, Tamara Civera, Renato A Dupke, Alessandro Ederoclite, Carlos López-Sanjuan, Antonio Marín-Franch, Claudia Mendes de Oliveira, Mariano Moles, David Muniesa, Laerte Sodré, Keith Taylor, Jesús Varela, Héctor Vázquez Ramió |
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| Přispěvatelé: | Laboratoire de Physique Nucléaire et de Hautes Énergies (LPNHE (UMR_7585)), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Astrophysique de Marseille (LAM), Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2022 |
| Předmět: |
Cosmology and Nongalactic Astrophysics (astro-ph.CO)
Astrophysics::Instrumentation and Methods for Astrophysics FOS: Physical sciences Astronomy and Astrophysics Astrophysics::Cosmology and Extragalactic Astrophysics methods: data analysis Astrophysics - Astrophysics of Galaxies techniques: photometric surveys Space and Planetary Science quasars: general Astrophysics of Galaxies (astro-ph.GA) [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph] catalogues Astrophysics::Galaxy Astrophysics Astrophysics - Cosmology and Nongalactic Astrophysics |
| Zdroj: | Monthly Notices of the Royal Astronomical Society Monthly Notices of the Royal Astronomical Society, 2023, 520 (3), pp.3476-3493. ⟨10.1093/mnras/stac2962⟩ |
| ISSN: | 0035-8711 1365-2966 |
| DOI: | 10.48550/arxiv.2202.00103 |
| Popis: | In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper we develop a pipeline to compute synthetic photometry of quasars, galaxies and stars using spectra of objects targeted as quasars in the Sloan Digital Sky Survey. To match the same depths and signal-to-noise ratio distributions in all bands expected for miniJPAS point sources in the range $17.5\leq r 20 pages, 18 figures, submitted to MNRAS |
| Databáze: | OpenAIRE |
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