The MeSsI (merging systems identification) algorithm and catalogue

Autores
De Los Rios, Martín; Dominguez, Mariano; Paz, Dante Javier; Merchan, Manuel Enrique
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Merging galaxy systems provide observational evidence of the existence of dark matter and constraints on its properties. Therefore, statistically uniform samples of merging systems would be a powerful tool for several studies. In this paper, we present a new methodology for the identification of merging systems and the results of its application to galaxy redshift surveys.We use as a starting point amock catalogue of galaxy systems, identified using friendsof- friends algorithms, that have experienced a major merger, as indicated by its merger tree. By applying machine learning techniques in this training sample, and using several features computed from the observable properties of galaxy members, it is possible to select galaxy groups that have a high probability of having experienced a major merger. Next, we apply a mixture of Gaussian techniques on galaxy members in order to reconstruct the properties of the haloes involved in such mergers. This methodology provides a highly reliable sample of merging systems with low contamination and precisely recovered properties. We apply our techniques to samples of galaxy systems obtained from the Sloan Digital Sky Survey Data Release 7, theWide-Field Nearby Galaxy-Cluster Survey (WINGS) and the Hectospec Cluster Survey (HeCS). Our results recover previously known merging systems and provide several new candidates. We present their measured properties and discuss future analysis on current and forthcoming samples.
Fil: De Los Rios, Martín. 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: Dominguez, Mariano. 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: Paz, Dante Javier. 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: Merchan, Manuel Enrique. 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. Observatorio Astronómico de Córdoba; Argentina
Materia
DARK MATTER
GALAXIES: CLUSTERS: GENERAL
GALAXIES: KINEMATICS AND DYNAMICS
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:11336/183601
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling The MeSsI (merging systems identification) algorithm and catalogueDe Los Rios, MartínDominguez, MarianoPaz, Dante JavierMerchan, Manuel EnriqueDARK MATTERGALAXIES: CLUSTERS: GENERALGALAXIES: KINEMATICS AND DYNAMICShttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Merging galaxy systems provide observational evidence of the existence of dark matter and constraints on its properties. Therefore, statistically uniform samples of merging systems would be a powerful tool for several studies. In this paper, we present a new methodology for the identification of merging systems and the results of its application to galaxy redshift surveys.We use as a starting point amock catalogue of galaxy systems, identified using friendsof- friends algorithms, that have experienced a major merger, as indicated by its merger tree. By applying machine learning techniques in this training sample, and using several features computed from the observable properties of galaxy members, it is possible to select galaxy groups that have a high probability of having experienced a major merger. Next, we apply a mixture of Gaussian techniques on galaxy members in order to reconstruct the properties of the haloes involved in such mergers. This methodology provides a highly reliable sample of merging systems with low contamination and precisely recovered properties. We apply our techniques to samples of galaxy systems obtained from the Sloan Digital Sky Survey Data Release 7, theWide-Field Nearby Galaxy-Cluster Survey (WINGS) and the Hectospec Cluster Survey (HeCS). Our results recover previously known merging systems and provide several new candidates. We present their measured properties and discuss future analysis on current and forthcoming samples.Fil: De Los Rios, Martín. 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: Dominguez, Mariano. 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: Paz, Dante Javier. 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: Merchan, Manuel Enrique. 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. Observatorio Astronómico de Córdoba; ArgentinaWiley Blackwell Publishing, Inc2016-03-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/183601De Los Rios, Martín; Dominguez, Mariano; Paz, Dante Javier; Merchan, Manuel Enrique; The MeSsI (merging systems identification) algorithm and catalogue; Wiley Blackwell Publishing, Inc; Monthly Notices of the Royal Astronomical Society; 458; 1; 11-3-2016; 226-2320035-87111365-2966CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1093/mnras/stw215info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:01:13Zoai:ri.conicet.gov.ar:11336/183601instacron: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 10:01:13.646CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv The MeSsI (merging systems identification) algorithm and catalogue
title The MeSsI (merging systems identification) algorithm and catalogue
spellingShingle The MeSsI (merging systems identification) algorithm and catalogue
De Los Rios, Martín
DARK MATTER
GALAXIES: CLUSTERS: GENERAL
GALAXIES: KINEMATICS AND DYNAMICS
title_short The MeSsI (merging systems identification) algorithm and catalogue
title_full The MeSsI (merging systems identification) algorithm and catalogue
title_fullStr The MeSsI (merging systems identification) algorithm and catalogue
title_full_unstemmed The MeSsI (merging systems identification) algorithm and catalogue
title_sort The MeSsI (merging systems identification) algorithm and catalogue
dc.creator.none.fl_str_mv De Los Rios, Martín
Dominguez, Mariano
Paz, Dante Javier
Merchan, Manuel Enrique
author De Los Rios, Martín
author_facet De Los Rios, Martín
Dominguez, Mariano
Paz, Dante Javier
Merchan, Manuel Enrique
author_role author
author2 Dominguez, Mariano
Paz, Dante Javier
Merchan, Manuel Enrique
author2_role author
author
author
dc.subject.none.fl_str_mv DARK MATTER
GALAXIES: CLUSTERS: GENERAL
GALAXIES: KINEMATICS AND DYNAMICS
topic DARK MATTER
GALAXIES: CLUSTERS: GENERAL
GALAXIES: KINEMATICS AND DYNAMICS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Merging galaxy systems provide observational evidence of the existence of dark matter and constraints on its properties. Therefore, statistically uniform samples of merging systems would be a powerful tool for several studies. In this paper, we present a new methodology for the identification of merging systems and the results of its application to galaxy redshift surveys.We use as a starting point amock catalogue of galaxy systems, identified using friendsof- friends algorithms, that have experienced a major merger, as indicated by its merger tree. By applying machine learning techniques in this training sample, and using several features computed from the observable properties of galaxy members, it is possible to select galaxy groups that have a high probability of having experienced a major merger. Next, we apply a mixture of Gaussian techniques on galaxy members in order to reconstruct the properties of the haloes involved in such mergers. This methodology provides a highly reliable sample of merging systems with low contamination and precisely recovered properties. We apply our techniques to samples of galaxy systems obtained from the Sloan Digital Sky Survey Data Release 7, theWide-Field Nearby Galaxy-Cluster Survey (WINGS) and the Hectospec Cluster Survey (HeCS). Our results recover previously known merging systems and provide several new candidates. We present their measured properties and discuss future analysis on current and forthcoming samples.
Fil: De Los Rios, Martín. 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: Dominguez, Mariano. 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: Paz, Dante Javier. 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: Merchan, Manuel Enrique. 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. Observatorio Astronómico de Córdoba; Argentina
description Merging galaxy systems provide observational evidence of the existence of dark matter and constraints on its properties. Therefore, statistically uniform samples of merging systems would be a powerful tool for several studies. In this paper, we present a new methodology for the identification of merging systems and the results of its application to galaxy redshift surveys.We use as a starting point amock catalogue of galaxy systems, identified using friendsof- friends algorithms, that have experienced a major merger, as indicated by its merger tree. By applying machine learning techniques in this training sample, and using several features computed from the observable properties of galaxy members, it is possible to select galaxy groups that have a high probability of having experienced a major merger. Next, we apply a mixture of Gaussian techniques on galaxy members in order to reconstruct the properties of the haloes involved in such mergers. This methodology provides a highly reliable sample of merging systems with low contamination and precisely recovered properties. We apply our techniques to samples of galaxy systems obtained from the Sloan Digital Sky Survey Data Release 7, theWide-Field Nearby Galaxy-Cluster Survey (WINGS) and the Hectospec Cluster Survey (HeCS). Our results recover previously known merging systems and provide several new candidates. We present their measured properties and discuss future analysis on current and forthcoming samples.
publishDate 2016
dc.date.none.fl_str_mv 2016-03-11
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/11336/183601
De Los Rios, Martín; Dominguez, Mariano; Paz, Dante Javier; Merchan, Manuel Enrique; The MeSsI (merging systems identification) algorithm and catalogue; Wiley Blackwell Publishing, Inc; Monthly Notices of the Royal Astronomical Society; 458; 1; 11-3-2016; 226-232
0035-8711
1365-2966
CONICET Digital
CONICET
url http://hdl.handle.net/11336/183601
identifier_str_mv De Los Rios, Martín; Dominguez, Mariano; Paz, Dante Javier; Merchan, Manuel Enrique; The MeSsI (merging systems identification) algorithm and catalogue; Wiley Blackwell Publishing, Inc; Monthly Notices of the Royal Astronomical Society; 458; 1; 11-3-2016; 226-232
0035-8711
1365-2966
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1093/mnras/stw215
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
application/pdf
dc.publisher.none.fl_str_mv Wiley Blackwell Publishing, Inc
publisher.none.fl_str_mv Wiley Blackwell Publishing, Inc
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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