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: Pérez Ràfols, Ignasi|||0000-0001-6979-0125, Ederoclite, A, Moles Villamate, Mariano
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/407797
Acceso en línea:https://hdl.handle.net/2117/407797
https://dx.doi.org/10.1093/mnras/stac2962
Access Level:acceso abierto
Palabra clave:Àrees temàtiques de la UPC::Física::Astronomia i astrofísica
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spelling The miniJPAS survey quasar selection – I. Mock catalogues for classificationPérez Ràfols, Ignasi|||0000-0001-6979-0125Ederoclite, AMoles Villamate, MarianoÀrees temàtiques de la UPC::Física::Astronomia i astrofísicaIn 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.Oxford University Press20232023-02-1520242024-05-10journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/407797https://dx.doi.org/10.1093/mnras/stac2962reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4077972026-05-27T15:37:01Z
dc.title.none.fl_str_mv The miniJPAS survey quasar selection – I. Mock catalogues for classification
title The miniJPAS survey quasar selection – I. Mock catalogues for classification
spellingShingle The miniJPAS survey quasar selection – I. Mock catalogues for classification
Pérez Ràfols, Ignasi|||0000-0001-6979-0125
Àrees temàtiques de la UPC::Física::Astronomia i astrofísica
title_short The miniJPAS survey quasar selection – I. Mock catalogues for classification
title_full The miniJPAS survey quasar selection – I. Mock catalogues for classification
title_fullStr The miniJPAS survey quasar selection – I. Mock catalogues for classification
title_full_unstemmed The miniJPAS survey quasar selection – I. Mock catalogues for classification
title_sort The miniJPAS survey quasar selection – I. Mock catalogues for classification
dc.creator.none.fl_str_mv Pérez Ràfols, Ignasi|||0000-0001-6979-0125
Ederoclite, A
Moles Villamate, Mariano
author Pérez Ràfols, Ignasi|||0000-0001-6979-0125
author_facet Pérez Ràfols, Ignasi|||0000-0001-6979-0125
Ederoclite, A
Moles Villamate, Mariano
author_role author
author2 Ederoclite, A
Moles Villamate, Mariano
author2_role author
author
dc.subject.none.fl_str_mv Àrees temàtiques de la UPC::Física::Astronomia i astrofísica
topic Àrees temàtiques de la UPC::Física::Astronomia i astrofísica
description 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.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-02-15
2024
2024-05-10
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/407797
https://dx.doi.org/10.1093/mnras/stac2962
url https://hdl.handle.net/2117/407797
https://dx.doi.org/10.1093/mnras/stac2962
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
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