Detection, characterisation and use of open clusters in a Galactic context in a Big Data environment
[eng] Open clusters are groups of stars, gravitationally bound together, that were born from the same molecular cloud and, thus, share similar positions, kinematics, ages and metallicities. Traditional methods to detect open clusters rely in the visual inspection of regions of the sky to look for po...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/177858 |
| Acceso en línea: | https://hdl.handle.net/2445/177858 http://hdl.handle.net/10803/671790 |
| Access Level: | acceso abierto |
| Palabra clave: | Cúmuls d'estels Astrometria Galàxies Dades massives Clusters of stars Astrometry Galaxies Big data |
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Detection, characterisation and use of open clusters in a Galactic context in a Big Data environment |
| title |
Detection, characterisation and use of open clusters in a Galactic context in a Big Data environment |
| spellingShingle |
Detection, characterisation and use of open clusters in a Galactic context in a Big Data environment Castro Ginard, Alfred Cúmuls d'estels Astrometria Galàxies Dades massives Clusters of stars Astrometry Galaxies Big data |
| title_short |
Detection, characterisation and use of open clusters in a Galactic context in a Big Data environment |
| title_full |
Detection, characterisation and use of open clusters in a Galactic context in a Big Data environment |
| title_fullStr |
Detection, characterisation and use of open clusters in a Galactic context in a Big Data environment |
| title_full_unstemmed |
Detection, characterisation and use of open clusters in a Galactic context in a Big Data environment |
| title_sort |
Detection, characterisation and use of open clusters in a Galactic context in a Big Data environment |
| dc.creator.none.fl_str_mv |
Castro Ginard, Alfred |
| author |
Castro Ginard, Alfred |
| author_facet |
Castro Ginard, Alfred |
| author_role |
author |
| dc.contributor.none.fl_str_mv |
Luri Carrascoso, Xavier Jordi i Nebot, Carme Universitat de Barcelona. Departament de Física Quàntica i Astrofísica |
| dc.subject.none.fl_str_mv |
Cúmuls d'estels Astrometria Galàxies Dades massives Clusters of stars Astrometry Galaxies Big data |
| topic |
Cúmuls d'estels Astrometria Galàxies Dades massives Clusters of stars Astrometry Galaxies Big data |
| description |
[eng] Open clusters are groups of stars, gravitationally bound together, that were born from the same molecular cloud and, thus, share similar positions, kinematics, ages and metallicities. Traditional methods to detect open clusters rely in the visual inspection of regions of the sky to look for positional overdensities of stars, which then are checked to follow an isochrone pattern in a colour-magnitude diagram. The publication of the second Gaia data release, with more than 1.3 billion stars with parallax and proper motion measurements together with mean photometry in three broadbands, boosted the development of novel machine learning-based techniques to automatise the search for open clusters, using both the astrometric and photometric information. The characterised open clusters in the Galaxy are popular tracers of properties of the Galactic disc such as the structure and evolution of the spiral arms, or testbed for stellar evolution studies for instance, because their astrophysical parameters are estimated with greater precision than for field stars. Therefore, a good understanding of the open cluster population in the Milky Way is key for Galactic archaeology studies. Our aim for this thesis is to transform classical methodologies to detect different kinds of patterns from astronomical data, that mostly relies on visual inspection, to an automatic data mining procedure to extract meaningful information from stellar catalogues. We also aim to use the result of the application of machine learning techniques to Gaia data, in a broader Galactic context. We have developed a data mining methodology to blindly search for open clusters in the Galactic disc. First, we use a density-based clustering algorithm, DBSCAN, to search for overdensities in the five-dimensional astrometric parameter space in Gaia data. The deployment of the clustering step in a Big Data environment, at the MareNostrum supercomputer located in the Barcelona Supercomputing Center, prevents the search to be limited by computational limitations. Second, the detected overdensities are classified into mere statistical or physical overdensities using an artificial neural network trained to recognise the isochrone pattern that open cluster member stars follow in a colour-magnitude diagram. We estimate astrophysical parameters such as ages, distances and line-of-sight extinctions for the whole open cluster population using an artificial neural network trained on well-known open clusters. We use this additional information, together with radial velocities gathered from different space-based and ground-based surveys, to trace the Galactic spiral present-day structure using GaussianMixtureModels to associate the young (< 30 Myr) open clusters to their mother spiral arms. We also describe the spiral arms evolution during the last 80 Myr to provide new insights into the nature of the Milky Way spiral structure. The automatization of the open cluster detection procedure, together with its deployment in a Big Data environment, has resulted in more than 650 new open clusters detected with this methodology. The new UBC clusters (named after the University of Barcelona) represent one-third of the actual open clusters census (2017 objects with Gaia DR2 parameters), and it is the largest single contribution to the open cluster catalogue. We are able to add 264 young open clusters (< 30 Myr) to the 84 high-mass star- forming regions traditionally used to trace spiral arms, to increase the Galactocentric azimuth range where the Milky Way spiral arms are defined, and better estimate their present-day parameters. By analysing the age distribution of the open clusters across the Galactic spiral arms, and computing the spiral arms pattern speeds following the open clusters orbits from their birthplaces, we are able to disfavour classical density waves as the main mechanism for the formation of the Milky Way spiral arms, favouring a transient behaviour. This thesis has shown that the use of machine learning, with proper treatment of the computational resources, has a long journey ahead in a data-dominated future for Astronomy. |
| publishDate |
2021 |
| dc.date.none.fl_str_mv |
2021 |
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info:eu-repo/semantics/doctoralThesis info:eu-repo/semantics/publishedVersion |
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doctoralThesis |
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publishedVersion |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/2445/177858 http://hdl.handle.net/10803/671790 |
| url |
https://hdl.handle.net/2445/177858 http://hdl.handle.net/10803/671790 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.rights.none.fl_str_mv |
cc by-sa (c) Castro Ginard, Alfred, 2021 http://creativecommons.org/licenses/by-sa/3.0/es/ info:eu-repo/semantics/openAccess |
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cc by-sa (c) Castro Ginard, Alfred, 2021 http://creativecommons.org/licenses/by-sa/3.0/es/ |
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openAccess |
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application/pdf |
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Universitat de Barcelona |
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Universitat de Barcelona |
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Tesis Doctorals - Departament - Física Quàntica i Astrofísica reponame:Dipòsit Digital de la UB instname:Universidad de Barcelona |
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Universidad de Barcelona |
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Dipòsit Digital de la UB |
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Dipòsit Digital de la UB |
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1869410049888092160 |
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Detection, characterisation and use of open clusters in a Galactic context in a Big Data environmentCastro Ginard, AlfredCúmuls d'estelsAstrometriaGalàxiesDades massivesClusters of starsAstrometryGalaxiesBig data[eng] Open clusters are groups of stars, gravitationally bound together, that were born from the same molecular cloud and, thus, share similar positions, kinematics, ages and metallicities. Traditional methods to detect open clusters rely in the visual inspection of regions of the sky to look for positional overdensities of stars, which then are checked to follow an isochrone pattern in a colour-magnitude diagram. The publication of the second Gaia data release, with more than 1.3 billion stars with parallax and proper motion measurements together with mean photometry in three broadbands, boosted the development of novel machine learning-based techniques to automatise the search for open clusters, using both the astrometric and photometric information. The characterised open clusters in the Galaxy are popular tracers of properties of the Galactic disc such as the structure and evolution of the spiral arms, or testbed for stellar evolution studies for instance, because their astrophysical parameters are estimated with greater precision than for field stars. Therefore, a good understanding of the open cluster population in the Milky Way is key for Galactic archaeology studies. Our aim for this thesis is to transform classical methodologies to detect different kinds of patterns from astronomical data, that mostly relies on visual inspection, to an automatic data mining procedure to extract meaningful information from stellar catalogues. We also aim to use the result of the application of machine learning techniques to Gaia data, in a broader Galactic context. We have developed a data mining methodology to blindly search for open clusters in the Galactic disc. First, we use a density-based clustering algorithm, DBSCAN, to search for overdensities in the five-dimensional astrometric parameter space in Gaia data. The deployment of the clustering step in a Big Data environment, at the MareNostrum supercomputer located in the Barcelona Supercomputing Center, prevents the search to be limited by computational limitations. Second, the detected overdensities are classified into mere statistical or physical overdensities using an artificial neural network trained to recognise the isochrone pattern that open cluster member stars follow in a colour-magnitude diagram. We estimate astrophysical parameters such as ages, distances and line-of-sight extinctions for the whole open cluster population using an artificial neural network trained on well-known open clusters. We use this additional information, together with radial velocities gathered from different space-based and ground-based surveys, to trace the Galactic spiral present-day structure using GaussianMixtureModels to associate the young (< 30 Myr) open clusters to their mother spiral arms. We also describe the spiral arms evolution during the last 80 Myr to provide new insights into the nature of the Milky Way spiral structure. The automatization of the open cluster detection procedure, together with its deployment in a Big Data environment, has resulted in more than 650 new open clusters detected with this methodology. The new UBC clusters (named after the University of Barcelona) represent one-third of the actual open clusters census (2017 objects with Gaia DR2 parameters), and it is the largest single contribution to the open cluster catalogue. We are able to add 264 young open clusters (< 30 Myr) to the 84 high-mass star- forming regions traditionally used to trace spiral arms, to increase the Galactocentric azimuth range where the Milky Way spiral arms are defined, and better estimate their present-day parameters. By analysing the age distribution of the open clusters across the Galactic spiral arms, and computing the spiral arms pattern speeds following the open clusters orbits from their birthplaces, we are able to disfavour classical density waves as the main mechanism for the formation of the Milky Way spiral arms, favouring a transient behaviour. This thesis has shown that the use of machine learning, with proper treatment of the computational resources, has a long journey ahead in a data-dominated future for Astronomy.[cat] Els cúmuls estel·lars oberts són conjunts d'estels, lligats gravitatòriament, nascuts al mateix núvol molecular que tenen propietats similars. Aquests cúmuls són traçadors populars de la estructura del disc Galàctic, com ara els braços espirals. El segon llançament de dades de Gaia, amb més de 1300 milions d'estels, impossibilita la detecció de cúmuls a partir de mètodes tradicionals degut al gran volum del catàleg. Per això, el desenvolupament de tècniques automàtiques per aquest fi ha crescut juntament amb el volums dels catàlegs a analitzar. Hem desenvolupat una metodologia per a la cerca a cegues de cúmuls oberts al disc Galàctic. Hem utilitzat un algoritme de clustering, DBSCAN, per trobar sobredensitats en l'espai astromètric de cinc dimensions de Gaia. La implementació del mètode de clustering a un entorn de Big Data, al superordinador MareNostrum, ens permet cercar cúmuls oberts basant-nos en les seves propietats físiques. Les sobredensitats detectades s'identifiquen com a cúmuls oberts reals per mitjà d'una xarxa neuronal artificial que reconeix isòcrones en un diagrama de color-magnitud. L'automatització del procediment de detecció amb l'ús de tècniques de Big Data, ha resultat en més de 650 nous cúmuls. Aquests nous cúmul representen un terç de la població actual, i és la contribució individual més gran al catàleg. Hem pogut estimar les propietats físiques dels cúmuls com distància, edat i extinció, fent servir una xarxa neuronal artificial entrenada sobre cúmuls coneguts. Fem servir aquesta informació, juntament amb mesures de velocitat radial, per traçar l'estructura espiral actual de la nostra Galàxia associant els cúmuls oberts més joves (< 30 milions d'anys) al braç espiral on s'han format. Amb això, hem augmentat el nombre de traçadors de braços espirals, afegint 264 cúmuls joves als traçadors utilitzats tradicionalment. Això ens ha permès estimar millor els paràmetres actuals d'aquests braços. Analitzant la distribució en edat dels cúmuls dins dels braços espirals, i calculant la velocitat en la que aquests braços es mouen a partir de l'orbita dels cúmuls, hem pogut desfavorir la teoria clàssica d'ona de densitat com a mecanisme principal de formació de l'estructura espiral, trobant un comportament més transitori dels braços.Universitat de BarcelonaLuri Carrascoso, XavierJordi i Nebot, CarmeUniversitat de Barcelona. Departament de Física Quàntica i Astrofísica2021info:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/177858http://hdl.handle.net/10803/671790Tesis Doctorals - Departament - Física Quàntica i Astrofísicareponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaIngléscc by-sa (c) Castro Ginard, Alfred, 2021http://creativecommons.org/licenses/by-sa/3.0/es/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1778582026-05-27T06:46:51Z |
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15.300724 |