A statistical formalism for alignment analysis

Autores
Dávila Kurbán, Federico; Lares, Marcelo; Garcia Lambas, Diego Rodolfo
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The detection of anisotropies with respect to a given direction in a vector field is a common problem in astronomy. Several methods have been proposed that rely on the distribution of the acute angles between the data and a reference direction. Different approaches use Monte Carlo methods to quantify the statistical significance of a signal, although often lacking an analytical framework. Here we present two methods to detect and quantify alignment signals and test their statistical robustness. The first method considers the deviance of the relative fraction of vector components in the plane perpendicular to a reference direction with respect to an isotropic distribution. We also derive the statistical properties and stability of the resulting estimator, and therefore does not rely on Monte Carlo simulations to assess its statistical significance. The second method is based on a fit over the residuals of the empirical cumulative distribution function with respect to that expected for a uniform distribution, using a small set of harmonic orthogonal functions, which does not rely on any binning scheme. We compare these methods with others commonly used in the literature, using Monte Carlo simulations, finding that the proposed statistics allow the detection of alignment signals with greater significance.
Fil: Dávila Kurbán, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
Fil: Lares, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina
Fil: Garcia Lambas, Diego Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina
Materia
METHODS: STATISTICAL
METHODS: NUMERICAL
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/203025
Acceder
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spelling A statistical formalism for alignment analysisDávila Kurbán, FedericoLares, MarceloGarcia Lambas, Diego RodolfoMETHODS: STATISTICALMETHODS: NUMERICALhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1The detection of anisotropies with respect to a given direction in a vector field is a common problem in astronomy. Several methods have been proposed that rely on the distribution of the acute angles between the data and a reference direction. Different approaches use Monte Carlo methods to quantify the statistical significance of a signal, although often lacking an analytical framework. Here we present two methods to detect and quantify alignment signals and test their statistical robustness. The first method considers the deviance of the relative fraction of vector components in the plane perpendicular to a reference direction with respect to an isotropic distribution. We also derive the statistical properties and stability of the resulting estimator, and therefore does not rely on Monte Carlo simulations to assess its statistical significance. The second method is based on a fit over the residuals of the empirical cumulative distribution function with respect to that expected for a uniform distribution, using a small set of harmonic orthogonal functions, which does not rely on any binning scheme. We compare these methods with others commonly used in the literature, using Monte Carlo simulations, finding that the proposed statistics allow the detection of alignment signals with greater significance.Fil: Dávila Kurbán, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Lares, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; ArgentinaFil: Garcia Lambas, Diego Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; ArgentinaCornell University2022-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/203025Dávila Kurbán, Federico; Lares, Marcelo; Garcia Lambas, Diego Rodolfo; A statistical formalism for alignment analysis; Cornell University; ArXiv.org; 2-20222331-8422CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.48550/arXiv.2202.13244info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:25:29Zoai:ri.conicet.gov.ar:11336/203025instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:25:29.877CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A statistical formalism for alignment analysis
title A statistical formalism for alignment analysis
spellingShingle A statistical formalism for alignment analysis
Dávila Kurbán, Federico
METHODS: STATISTICAL
METHODS: NUMERICAL
title_short A statistical formalism for alignment analysis
title_full A statistical formalism for alignment analysis
title_fullStr A statistical formalism for alignment analysis
title_full_unstemmed A statistical formalism for alignment analysis
title_sort A statistical formalism for alignment analysis
dc.creator.none.fl_str_mv Dávila Kurbán, Federico
Lares, Marcelo
Garcia Lambas, Diego Rodolfo
author Dávila Kurbán, Federico
author_facet Dávila Kurbán, Federico
Lares, Marcelo
Garcia Lambas, Diego Rodolfo
author_role author
author2 Lares, Marcelo
Garcia Lambas, Diego Rodolfo
author2_role author
author
dc.subject.none.fl_str_mv METHODS: STATISTICAL
METHODS: NUMERICAL
topic METHODS: STATISTICAL
METHODS: NUMERICAL
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The detection of anisotropies with respect to a given direction in a vector field is a common problem in astronomy. Several methods have been proposed that rely on the distribution of the acute angles between the data and a reference direction. Different approaches use Monte Carlo methods to quantify the statistical significance of a signal, although often lacking an analytical framework. Here we present two methods to detect and quantify alignment signals and test their statistical robustness. The first method considers the deviance of the relative fraction of vector components in the plane perpendicular to a reference direction with respect to an isotropic distribution. We also derive the statistical properties and stability of the resulting estimator, and therefore does not rely on Monte Carlo simulations to assess its statistical significance. The second method is based on a fit over the residuals of the empirical cumulative distribution function with respect to that expected for a uniform distribution, using a small set of harmonic orthogonal functions, which does not rely on any binning scheme. We compare these methods with others commonly used in the literature, using Monte Carlo simulations, finding that the proposed statistics allow the detection of alignment signals with greater significance.
Fil: Dávila Kurbán, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
Fil: Lares, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina
Fil: Garcia Lambas, Diego Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina
description The detection of anisotropies with respect to a given direction in a vector field is a common problem in astronomy. Several methods have been proposed that rely on the distribution of the acute angles between the data and a reference direction. Different approaches use Monte Carlo methods to quantify the statistical significance of a signal, although often lacking an analytical framework. Here we present two methods to detect and quantify alignment signals and test their statistical robustness. The first method considers the deviance of the relative fraction of vector components in the plane perpendicular to a reference direction with respect to an isotropic distribution. We also derive the statistical properties and stability of the resulting estimator, and therefore does not rely on Monte Carlo simulations to assess its statistical significance. The second method is based on a fit over the residuals of the empirical cumulative distribution function with respect to that expected for a uniform distribution, using a small set of harmonic orthogonal functions, which does not rely on any binning scheme. We compare these methods with others commonly used in the literature, using Monte Carlo simulations, finding that the proposed statistics allow the detection of alignment signals with greater significance.
publishDate 2022
dc.date.none.fl_str_mv 2022-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/203025
Dávila Kurbán, Federico; Lares, Marcelo; Garcia Lambas, Diego Rodolfo; A statistical formalism for alignment analysis; Cornell University; ArXiv.org; 2-2022
2331-8422
CONICET Digital
CONICET
url http://hdl.handle.net/11336/203025
identifier_str_mv Dávila Kurbán, Federico; Lares, Marcelo; Garcia Lambas, Diego Rodolfo; A statistical formalism for alignment analysis; Cornell University; ArXiv.org; 2-2022
2331-8422
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.48550/arXiv.2202.13244
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Cornell University
publisher.none.fl_str_mv Cornell University
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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