The miniJPAS survey quasar selection : I. Mock catalogues for classification
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/268506 |
Resumo: | 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. |
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Silva, Carolina Queiroz de AbreuChies-Santos, Ana LeonorVázquez Ramió, Héctor2023-12-16T03:26:29Z20230035-8711http://hdl.handle.net/10183/268506001175336In 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.application/pdfengMonthly notices of the royal astronomical society. Oxford. Vol. 520, no. 3 (Apr. 2023), p. 3476–3493Catalogos astronomicosFotometria astronômicaQuasarsMethods : Data analysisTechniques : PhotometricCataloguesSurveysQuasars : GeneralThe miniJPAS survey quasar selection : I. Mock catalogues for classificationEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001175336.pdf.txt001175336.pdf.txtExtracted Texttext/plain89048http://www.lume.ufrgs.br/bitstream/10183/268506/2/001175336.pdf.txtf0208b438d58ceac852b3f3c6d164a45MD52ORIGINAL001175336.pdfTexto completo (inglês)application/pdf3986383http://www.lume.ufrgs.br/bitstream/10183/268506/1/001175336.pdf5220c65c800c9401260cad486ac8e969MD5110183/2685062023-12-17 04:22:51.591028oai:www.lume.ufrgs.br:10183/268506Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2023-12-17T06:22:51Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.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 Silva, Carolina Queiroz de Abreu Catalogos astronomicos Fotometria astronômica Quasars Methods : Data analysis Techniques : Photometric Catalogues Surveys Quasars : General |
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 |
author |
Silva, Carolina Queiroz de Abreu |
author_facet |
Silva, Carolina Queiroz de Abreu Chies-Santos, Ana Leonor Vázquez Ramió, Héctor |
author_role |
author |
author2 |
Chies-Santos, Ana Leonor Vázquez Ramió, Héctor |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Silva, Carolina Queiroz de Abreu Chies-Santos, Ana Leonor Vázquez Ramió, Héctor |
dc.subject.por.fl_str_mv |
Catalogos astronomicos Fotometria astronômica Quasars |
topic |
Catalogos astronomicos Fotometria astronômica Quasars Methods : Data analysis Techniques : Photometric Catalogues Surveys Quasars : General |
dc.subject.eng.fl_str_mv |
Methods : Data analysis Techniques : Photometric Catalogues Surveys Quasars : General |
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.accessioned.fl_str_mv |
2023-12-16T03:26:29Z |
dc.date.issued.fl_str_mv |
2023 |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
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0035-8711 |
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001175336 |
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eng |
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dc.relation.ispartof.pt_BR.fl_str_mv |
Monthly notices of the royal astronomical society. Oxford. Vol. 520, no. 3 (Apr. 2023), p. 3476–3493 |
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openAccess |
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