The miniJPAS survey quasar selection – I. Mock catalogues for classification

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 m...

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Detalles Bibliográficos
Autores: Queiroz, Carolina, Abramo, L. R., Rodrigues, Natália V. N., Pérez-Ràfols, Ignasi, Martínez-Solaeche, Ginés, Hernán-Caballero, Antonio, Hernández-Monteagudo, Carlos, Lumbreras-Calle, Alejandro, Pieri, Matthew M., Morrison, Sean S., Bonoli, Silvia, Chaves-Montero, Jonás, Chies-Santos, A. L., Díaz-García, L. A., Fernández-Soto, Alberto, González Delgado, Rosa M., Alcaniz, Jailson, Benítez, Narciso, Cenarro, A. J., Civera, Tamara, Dupke, Renato A., Ederoclite, Alessandro, López-Sanjuan, Carlos, Marín-Franch, Antonio, Mendes de Oliveira, Claudia, Moles, Mariano, Muniesa, David, Sodré, Laerte, Taylor, Keith, Varela, Jesús, Vázquez Ramió, H.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/334208
Acceso en línea:http://hdl.handle.net/10261/334208
Access Level:acceso abierto
Palabra clave:Methods: data analysis
Techniques: photometric
Catalogues
Surveys
Quasars: general
Descripción
Sumario: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 ≤ r < 24, we augment our sample of available spectra by shifting the original r-band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modelling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys. ©2022 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society.

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