Identification of RR lyrae stars in multiband, sparsely sampled data from the dark energy survey using template fitting and random forest classification
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/197165 |
Resumo: | Many studies have shown that RR Lyrae variable stars (RRL) are powerful stellar tracers of Galactic halo structure and satellite galaxies. The Dark Energy Survey (DES), with its deep and wide coverage (g ~ 23.5 mag in a single exposure; over 5000 deg2) provides a rich opportunity to search for substructures out to the edge of the Milky Way halo. However, the sparse and unevenly sampled multiband light curves from the DES wide-field survey (a median of four observations in each of grizY over the first three years) pose a challenge for traditional techniques used to detect RRL. We present an empirically motivated and computationally efficient template-fitting method to identify these variable stars using three years of DES data. When tested on DES light curves of previously classified objects in SDSS stripe 82, our algorithm recovers 89% of RRL periods to within 1% of their true value with 85% purity and 76% completeness. Using this method, we identify 5783 RRL candidates, ~28% of which are previously undiscovered. This method will be useful for identifying RRL in other sparse multiband data sets |
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Stringer, Katelyn M.Santiago, Basilio XavierDES Collaboration2019-07-19T02:38:48Z20190004-6256http://hdl.handle.net/10183/197165001097294Many studies have shown that RR Lyrae variable stars (RRL) are powerful stellar tracers of Galactic halo structure and satellite galaxies. The Dark Energy Survey (DES), with its deep and wide coverage (g ~ 23.5 mag in a single exposure; over 5000 deg2) provides a rich opportunity to search for substructures out to the edge of the Milky Way halo. However, the sparse and unevenly sampled multiband light curves from the DES wide-field survey (a median of four observations in each of grizY over the first three years) pose a challenge for traditional techniques used to detect RRL. We present an empirically motivated and computationally efficient template-fitting method to identify these variable stars using three years of DES data. When tested on DES light curves of previously classified objects in SDSS stripe 82, our algorithm recovers 89% of RRL periods to within 1% of their true value with 85% purity and 76% completeness. Using this method, we identify 5783 RRL candidates, ~28% of which are previously undiscovered. This method will be useful for identifying RRL in other sparse multiband data setsapplication/pdfengThe astronomical journal. Bristol. Vol. 158, no. 1 (July 2019), 16, 26 p.Catalogos astronomicosAglomerados estelares e associacoesEstrelas variaveisEnergia escuracatalogs–galaxyhalo–galaxystructure–methodsstatistical–starsvariables: RR LyraeIdentification of RR lyrae stars in multiband, sparsely sampled data from the dark energy survey using template fitting and random forest 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:UFRGSTEXT001097294.pdf.txt001097294.pdf.txtExtracted Texttext/plain124728http://www.lume.ufrgs.br/bitstream/10183/197165/2/001097294.pdf.txtdc3edba507a76dabd15b5b703a349d07MD52ORIGINAL001097294.pdfTexto completo (inglês)application/pdf3930279http://www.lume.ufrgs.br/bitstream/10183/197165/1/001097294.pdfcb823e87b565fc4455ccde7a341a4a7fMD5110183/1971652023-07-02 03:42:06.651514oai:www.lume.ufrgs.br:10183/197165Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2023-07-02T06:42:06Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Identification of RR lyrae stars in multiband, sparsely sampled data from the dark energy survey using template fitting and random forest classification |
title |
Identification of RR lyrae stars in multiband, sparsely sampled data from the dark energy survey using template fitting and random forest classification |
spellingShingle |
Identification of RR lyrae stars in multiband, sparsely sampled data from the dark energy survey using template fitting and random forest classification Stringer, Katelyn M. Catalogos astronomicos Aglomerados estelares e associacoes Estrelas variaveis Energia escura catalogs–galaxy halo–galaxy structure–methods statistical–stars variables: RR Lyrae |
title_short |
Identification of RR lyrae stars in multiband, sparsely sampled data from the dark energy survey using template fitting and random forest classification |
title_full |
Identification of RR lyrae stars in multiband, sparsely sampled data from the dark energy survey using template fitting and random forest classification |
title_fullStr |
Identification of RR lyrae stars in multiband, sparsely sampled data from the dark energy survey using template fitting and random forest classification |
title_full_unstemmed |
Identification of RR lyrae stars in multiband, sparsely sampled data from the dark energy survey using template fitting and random forest classification |
title_sort |
Identification of RR lyrae stars in multiband, sparsely sampled data from the dark energy survey using template fitting and random forest classification |
author |
Stringer, Katelyn M. |
author_facet |
Stringer, Katelyn M. Santiago, Basilio Xavier DES Collaboration |
author_role |
author |
author2 |
Santiago, Basilio Xavier DES Collaboration |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Stringer, Katelyn M. Santiago, Basilio Xavier DES Collaboration |
dc.subject.por.fl_str_mv |
Catalogos astronomicos Aglomerados estelares e associacoes Estrelas variaveis Energia escura |
topic |
Catalogos astronomicos Aglomerados estelares e associacoes Estrelas variaveis Energia escura catalogs–galaxy halo–galaxy structure–methods statistical–stars variables: RR Lyrae |
dc.subject.eng.fl_str_mv |
catalogs–galaxy halo–galaxy structure–methods statistical–stars variables: RR Lyrae |
description |
Many studies have shown that RR Lyrae variable stars (RRL) are powerful stellar tracers of Galactic halo structure and satellite galaxies. The Dark Energy Survey (DES), with its deep and wide coverage (g ~ 23.5 mag in a single exposure; over 5000 deg2) provides a rich opportunity to search for substructures out to the edge of the Milky Way halo. However, the sparse and unevenly sampled multiband light curves from the DES wide-field survey (a median of four observations in each of grizY over the first three years) pose a challenge for traditional techniques used to detect RRL. We present an empirically motivated and computationally efficient template-fitting method to identify these variable stars using three years of DES data. When tested on DES light curves of previously classified objects in SDSS stripe 82, our algorithm recovers 89% of RRL periods to within 1% of their true value with 85% purity and 76% completeness. Using this method, we identify 5783 RRL candidates, ~28% of which are previously undiscovered. This method will be useful for identifying RRL in other sparse multiband data sets |
publishDate |
2019 |
dc.date.accessioned.fl_str_mv |
2019-07-19T02:38:48Z |
dc.date.issued.fl_str_mv |
2019 |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/197165 |
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0004-6256 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001097294 |
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0004-6256 001097294 |
url |
http://hdl.handle.net/10183/197165 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
The astronomical journal. Bristol. Vol. 158, no. 1 (July 2019), 16, 26 p. |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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