Star/galaxy separation at faint magnitudes : application to a simulated Dark Energy Survey

Detalhes bibliográficos
Autor(a) principal: Soumagnac, Maayane Tamar
Data de Publicação: 2015
Outros Autores: Abdalla, Filipe B., Lahav, Ofer, Kirk, Donnacha, Sevilla Noarbe, Ignacio, Bertin, Emmanuel, Rowe, Barnaby T. P., Annis, James T., Busha, M. T., Costa, Luiz N. da, Frieman, Joshua A., Gaztañaga, Enrique, Jarvis, Michael, Lin, H., Percival, Will J., Santiago, Basilio Xavier, Sabiu, Cristiano Giovanni, Wechsler, Risa H., Wolz, Laura, Yanny, Brian
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRGS
Texto Completo: http://hdl.handle.net/10183/126984
Resumo: We address the problem of separating stars from galaxies in future large photometric surveys. We focus our analysis on simulations of the Dark Energy Survey (DES). In the first part of the paper, we derive the science requirements on star/galaxy separation, for measurement of the cosmological parameters with the gravitational weak lensing and large-scale structure probes. These requirements are dictated by the need to control both the statistical and systematic errors on the cosmological parameters, and by point spread function calibration. We formulate the requirements in terms of the completeness and purity provided by a given star/galaxy classifier. In order to achieve these requirements at faint magnitudes, we propose a new method for star/galaxy separation in the second part of the paper.We first use principal component analysis to outline the correlations between the objects parameters and extract from it the most relevant information.We then use the reduced set of parameters as input to an Artificial Neural Network. This multiparameter approach improves upon purely morphometric classifiers (such as the classifier implemented in SEXTRACTOR), especially at faint magnitudes: it increases the purity by up to 20 per cent for stars and by up to 12 per cent for galaxies, at i-magnitude fainter than 23.
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spelling Soumagnac, Maayane TamarAbdalla, Filipe B.Lahav, OferKirk, DonnachaSevilla Noarbe, IgnacioBertin, EmmanuelRowe, Barnaby T. P.Annis, James T.Busha, M. T.Costa, Luiz N. daFrieman, Joshua A.Gaztañaga, EnriqueJarvis, MichaelLin, H.Percival, Will J.Santiago, Basilio XavierSabiu, Cristiano GiovanniWechsler, Risa H.Wolz, LauraYanny, Brian2015-09-18T01:58:29Z20150035-8711http://hdl.handle.net/10183/126984000973413We address the problem of separating stars from galaxies in future large photometric surveys. We focus our analysis on simulations of the Dark Energy Survey (DES). In the first part of the paper, we derive the science requirements on star/galaxy separation, for measurement of the cosmological parameters with the gravitational weak lensing and large-scale structure probes. These requirements are dictated by the need to control both the statistical and systematic errors on the cosmological parameters, and by point spread function calibration. We formulate the requirements in terms of the completeness and purity provided by a given star/galaxy classifier. In order to achieve these requirements at faint magnitudes, we propose a new method for star/galaxy separation in the second part of the paper.We first use principal component analysis to outline the correlations between the objects parameters and extract from it the most relevant information.We then use the reduced set of parameters as input to an Artificial Neural Network. This multiparameter approach improves upon purely morphometric classifiers (such as the classifier implemented in SEXTRACTOR), especially at faint magnitudes: it increases the purity by up to 20 per cent for stars and by up to 12 per cent for galaxies, at i-magnitude fainter than 23.application/pdfengMonthly notices of the Royal Astronomical Society. Oxford. Vol. 450, no. 1 (June 2015), p. 666-680CosmologiaGaláxiasEnergia escuraMapeamentos astronômicosLentes gravitacionaisGravitational lensing: weakMethods: data analysisSurveysCosmology: observationsDark energyLargeScale structure of universeStar/galaxy separation at faint magnitudes : application to a simulated Dark Energy SurveyEstrangeiroinfo: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:UFRGSORIGINAL000973413.pdf000973413.pdfTexto completo (inglês)application/pdf1797603http://www.lume.ufrgs.br/bitstream/10183/126984/1/000973413.pdf17c000c59ab81346c382fa09e480a7f3MD51TEXT000973413.pdf.txt000973413.pdf.txtExtracted Texttext/plain77396http://www.lume.ufrgs.br/bitstream/10183/126984/2/000973413.pdf.txt66ab95c0a9baef28a8b4e638422215a9MD52THUMBNAIL000973413.pdf.jpg000973413.pdf.jpgGenerated Thumbnailimage/jpeg2117http://www.lume.ufrgs.br/bitstream/10183/126984/3/000973413.pdf.jpgdc3d6b56849f1aa7011edc0e5c5ef897MD5310183/1269842023-07-02 03:41:41.411566oai:www.lume.ufrgs.br:10183/126984Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2023-07-02T06:41:41Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false
dc.title.pt_BR.fl_str_mv Star/galaxy separation at faint magnitudes : application to a simulated Dark Energy Survey
title Star/galaxy separation at faint magnitudes : application to a simulated Dark Energy Survey
spellingShingle Star/galaxy separation at faint magnitudes : application to a simulated Dark Energy Survey
Soumagnac, Maayane Tamar
Cosmologia
Galáxias
Energia escura
Mapeamentos astronômicos
Lentes gravitacionais
Gravitational lensing: weak
Methods: data analysis
Surveys
Cosmology: observations
Dark energy
Large
Scale structure of universe
title_short Star/galaxy separation at faint magnitudes : application to a simulated Dark Energy Survey
title_full Star/galaxy separation at faint magnitudes : application to a simulated Dark Energy Survey
title_fullStr Star/galaxy separation at faint magnitudes : application to a simulated Dark Energy Survey
title_full_unstemmed Star/galaxy separation at faint magnitudes : application to a simulated Dark Energy Survey
title_sort Star/galaxy separation at faint magnitudes : application to a simulated Dark Energy Survey
author Soumagnac, Maayane Tamar
author_facet Soumagnac, Maayane Tamar
Abdalla, Filipe B.
Lahav, Ofer
Kirk, Donnacha
Sevilla Noarbe, Ignacio
Bertin, Emmanuel
Rowe, Barnaby T. P.
Annis, James T.
Busha, M. T.
Costa, Luiz N. da
Frieman, Joshua A.
Gaztañaga, Enrique
Jarvis, Michael
Lin, H.
Percival, Will J.
Santiago, Basilio Xavier
Sabiu, Cristiano Giovanni
Wechsler, Risa H.
Wolz, Laura
Yanny, Brian
author_role author
author2 Abdalla, Filipe B.
Lahav, Ofer
Kirk, Donnacha
Sevilla Noarbe, Ignacio
Bertin, Emmanuel
Rowe, Barnaby T. P.
Annis, James T.
Busha, M. T.
Costa, Luiz N. da
Frieman, Joshua A.
Gaztañaga, Enrique
Jarvis, Michael
Lin, H.
Percival, Will J.
Santiago, Basilio Xavier
Sabiu, Cristiano Giovanni
Wechsler, Risa H.
Wolz, Laura
Yanny, Brian
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Soumagnac, Maayane Tamar
Abdalla, Filipe B.
Lahav, Ofer
Kirk, Donnacha
Sevilla Noarbe, Ignacio
Bertin, Emmanuel
Rowe, Barnaby T. P.
Annis, James T.
Busha, M. T.
Costa, Luiz N. da
Frieman, Joshua A.
Gaztañaga, Enrique
Jarvis, Michael
Lin, H.
Percival, Will J.
Santiago, Basilio Xavier
Sabiu, Cristiano Giovanni
Wechsler, Risa H.
Wolz, Laura
Yanny, Brian
dc.subject.por.fl_str_mv Cosmologia
Galáxias
Energia escura
Mapeamentos astronômicos
Lentes gravitacionais
topic Cosmologia
Galáxias
Energia escura
Mapeamentos astronômicos
Lentes gravitacionais
Gravitational lensing: weak
Methods: data analysis
Surveys
Cosmology: observations
Dark energy
Large
Scale structure of universe
dc.subject.eng.fl_str_mv Gravitational lensing: weak
Methods: data analysis
Surveys
Cosmology: observations
Dark energy
Large
Scale structure of universe
description We address the problem of separating stars from galaxies in future large photometric surveys. We focus our analysis on simulations of the Dark Energy Survey (DES). In the first part of the paper, we derive the science requirements on star/galaxy separation, for measurement of the cosmological parameters with the gravitational weak lensing and large-scale structure probes. These requirements are dictated by the need to control both the statistical and systematic errors on the cosmological parameters, and by point spread function calibration. We formulate the requirements in terms of the completeness and purity provided by a given star/galaxy classifier. In order to achieve these requirements at faint magnitudes, we propose a new method for star/galaxy separation in the second part of the paper.We first use principal component analysis to outline the correlations between the objects parameters and extract from it the most relevant information.We then use the reduced set of parameters as input to an Artificial Neural Network. This multiparameter approach improves upon purely morphometric classifiers (such as the classifier implemented in SEXTRACTOR), especially at faint magnitudes: it increases the purity by up to 20 per cent for stars and by up to 12 per cent for galaxies, at i-magnitude fainter than 23.
publishDate 2015
dc.date.accessioned.fl_str_mv 2015-09-18T01:58:29Z
dc.date.issued.fl_str_mv 2015
dc.type.driver.fl_str_mv Estrangeiro
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10183/126984
dc.identifier.issn.pt_BR.fl_str_mv 0035-8711
dc.identifier.nrb.pt_BR.fl_str_mv 000973413
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url http://hdl.handle.net/10183/126984
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartof.pt_BR.fl_str_mv Monthly notices of the Royal Astronomical Society. Oxford. Vol. 450, no. 1 (June 2015), p. 666-680
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