The overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populations

Detalhes bibliográficos
Autor(a) principal: Souza, Rafael da Silva de
Data de Publicação: 2015
Outros Autores: Hilbe, Joseph, Buelens, Bart, Riggs, J. D., Cameron, Ewan, Ishida, Emille Eugenia de Oliveira, Chies-Santos, Ana Leonor, Killedar, Madhura
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRGS
Texto Completo: http://hdl.handle.net/10183/140345
Resumo: In this paper, the third in a series illustrating the power of generalized linear models (GLMs) for the astronomical community, we elucidate the potential of the class of GLMs which handles count data. The size of a galaxy’s globular cluster (GC) population (NGC) is a prolonged puzzle in the astronomical literature. It falls in the category of count data analysis, yet it is usually modelled as if it were a continuous response variable. We have developed a Bayesian negative binomial regression model to study the connection between NGC and the following galaxy properties: central black hole mass, dynamical bulge mass, bulge velocity dispersion and absolute visual magnitude. The methodology introduced herein naturally accounts for heteroscedasticity, intrinsic scatter, errors in measurements in both axes (either discrete or continuous) and allows modelling the population of GCs on their natural scale as a nonnegative integer variable. Prediction intervals of 99 per cent around the trend for expected NGC comfortably envelope the data, notably including the Milky Way, which has hitherto been considered a problematic outlier. Finally, we demonstrate how random intercept models can incorporate information of each particular galaxy morphological type. Bayesian variable selection methodology allows for automatically identifying galaxy types with different productions of GCs, suggesting that on average S0 galaxies have a GC population 35 per cent smaller than other types with similar brightness.
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spelling Souza, Rafael da Silva deHilbe, JosephBuelens, BartRiggs, J. D.Cameron, EwanIshida, Emille Eugenia de OliveiraChies-Santos, Ana LeonorKilledar, Madhura2016-05-06T02:21:45Z20150035-8711http://hdl.handle.net/10183/140345000985381In this paper, the third in a series illustrating the power of generalized linear models (GLMs) for the astronomical community, we elucidate the potential of the class of GLMs which handles count data. The size of a galaxy’s globular cluster (GC) population (NGC) is a prolonged puzzle in the astronomical literature. It falls in the category of count data analysis, yet it is usually modelled as if it were a continuous response variable. We have developed a Bayesian negative binomial regression model to study the connection between NGC and the following galaxy properties: central black hole mass, dynamical bulge mass, bulge velocity dispersion and absolute visual magnitude. The methodology introduced herein naturally accounts for heteroscedasticity, intrinsic scatter, errors in measurements in both axes (either discrete or continuous) and allows modelling the population of GCs on their natural scale as a nonnegative integer variable. Prediction intervals of 99 per cent around the trend for expected NGC comfortably envelope the data, notably including the Milky Way, which has hitherto been considered a problematic outlier. Finally, we demonstrate how random intercept models can incorporate information of each particular galaxy morphological type. Bayesian variable selection methodology allows for automatically identifying galaxy types with different productions of GCs, suggesting that on average S0 galaxies have a GC population 35 per cent smaller than other types with similar brightness.application/pdfengMonthly notices of the Royal Astronomical Society. Oxford. Vol. 453, no. 2 (Oct. 2015), p. 1928-1940Aglomerados globularesEstatística aplicadaMethods: data analysisMethods: statisticalGlobular clusters: generalThe overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populationsEstrangeiroinfo: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:UFRGSORIGINAL000985381.pdf000985381.pdfTexto completo (inglês)application/pdf2128187http://www.lume.ufrgs.br/bitstream/10183/140345/1/000985381.pdf8b424c4702c652a90910db5aaf8a4053MD51TEXT000985381.pdf.txt000985381.pdf.txtExtracted Texttext/plain58535http://www.lume.ufrgs.br/bitstream/10183/140345/2/000985381.pdf.txteed38fc16356248b0192ac92f8bf0985MD52THUMBNAIL000985381.pdf.jpg000985381.pdf.jpgGenerated Thumbnailimage/jpeg2241http://www.lume.ufrgs.br/bitstream/10183/140345/3/000985381.pdf.jpg0b0c7be595e243ce5201b948ef12c503MD5310183/1403452021-09-18 04:36:14.391015oai:www.lume.ufrgs.br:10183/140345Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2021-09-18T07:36:14Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false
dc.title.pt_BR.fl_str_mv The overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populations
title The overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populations
spellingShingle The overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populations
Souza, Rafael da Silva de
Aglomerados globulares
Estatística aplicada
Methods: data analysis
Methods: statistical
Globular clusters: general
title_short The overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populations
title_full The overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populations
title_fullStr The overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populations
title_full_unstemmed The overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populations
title_sort The overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populations
author Souza, Rafael da Silva de
author_facet Souza, Rafael da Silva de
Hilbe, Joseph
Buelens, Bart
Riggs, J. D.
Cameron, Ewan
Ishida, Emille Eugenia de Oliveira
Chies-Santos, Ana Leonor
Killedar, Madhura
author_role author
author2 Hilbe, Joseph
Buelens, Bart
Riggs, J. D.
Cameron, Ewan
Ishida, Emille Eugenia de Oliveira
Chies-Santos, Ana Leonor
Killedar, Madhura
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Souza, Rafael da Silva de
Hilbe, Joseph
Buelens, Bart
Riggs, J. D.
Cameron, Ewan
Ishida, Emille Eugenia de Oliveira
Chies-Santos, Ana Leonor
Killedar, Madhura
dc.subject.por.fl_str_mv Aglomerados globulares
Estatística aplicada
topic Aglomerados globulares
Estatística aplicada
Methods: data analysis
Methods: statistical
Globular clusters: general
dc.subject.eng.fl_str_mv Methods: data analysis
Methods: statistical
Globular clusters: general
description In this paper, the third in a series illustrating the power of generalized linear models (GLMs) for the astronomical community, we elucidate the potential of the class of GLMs which handles count data. The size of a galaxy’s globular cluster (GC) population (NGC) is a prolonged puzzle in the astronomical literature. It falls in the category of count data analysis, yet it is usually modelled as if it were a continuous response variable. We have developed a Bayesian negative binomial regression model to study the connection between NGC and the following galaxy properties: central black hole mass, dynamical bulge mass, bulge velocity dispersion and absolute visual magnitude. The methodology introduced herein naturally accounts for heteroscedasticity, intrinsic scatter, errors in measurements in both axes (either discrete or continuous) and allows modelling the population of GCs on their natural scale as a nonnegative integer variable. Prediction intervals of 99 per cent around the trend for expected NGC comfortably envelope the data, notably including the Milky Way, which has hitherto been considered a problematic outlier. Finally, we demonstrate how random intercept models can incorporate information of each particular galaxy morphological type. Bayesian variable selection methodology allows for automatically identifying galaxy types with different productions of GCs, suggesting that on average S0 galaxies have a GC population 35 per cent smaller than other types with similar brightness.
publishDate 2015
dc.date.issued.fl_str_mv 2015
dc.date.accessioned.fl_str_mv 2016-05-06T02:21:45Z
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dc.relation.ispartof.pt_BR.fl_str_mv Monthly notices of the Royal Astronomical Society. Oxford. Vol. 453, no. 2 (Oct. 2015), p. 1928-1940
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