Open cluster dynamics
This doctoral thesis document is based on the results obtained in four scientific articles.
- Autores:
-
Alfonso, Jeison Estivens
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2024
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/75274
- Acceso en línea:
- https://hdl.handle.net/1992/75274
- Palabra clave:
- Open clusters
Astrometry
N-body simulations
Initial mass function
Mass segregation
Machine learning
Statistics
Física
- Rights
- embargoedAccess
- License
- Attribution 4.0 International
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Open cluster dynamics |
| title |
Open cluster dynamics |
| spellingShingle |
Open cluster dynamics Open clusters Astrometry N-body simulations Initial mass function Mass segregation Machine learning Statistics Física |
| title_short |
Open cluster dynamics |
| title_full |
Open cluster dynamics |
| title_fullStr |
Open cluster dynamics |
| title_full_unstemmed |
Open cluster dynamics |
| title_sort |
Open cluster dynamics |
| dc.creator.fl_str_mv |
Alfonso, Jeison Estivens |
| dc.contributor.advisor.none.fl_str_mv |
García Varela, José Alejandro |
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Alfonso, Jeison Estivens |
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Kelkar, Neelima Govind Ferreira, Carlos |
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Open clusters Astrometry N-body simulations Initial mass function Mass segregation Machine learning Statistics |
| topic |
Open clusters Astrometry N-body simulations Initial mass function Mass segregation Machine learning Statistics Física |
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Física |
| description |
This doctoral thesis document is based on the results obtained in four scientific articles. |
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2024 |
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2024-12-10 |
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2024-12-12 |
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2025-12-31 |
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Trabajo de grado - Doctorado |
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García Varela, José Alejandrovirtual::21835-1Alfonso, Jeison EstivensKelkar, Neelima GovindFerreira, Carlos2024-12-12T19:54:54Z2025-12-312024-12-102024-12-12https://hdl.handle.net/1992/75274instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/This doctoral thesis document is based on the results obtained in four scientific articles.Open star clusters are one of the most studied stellar systems studied due to their significance importance in Galactic dynamics and structure, and stellar evolution. About seven thousand open clusters have been found in the Milky Way, but this number is expected to increase in a few years with the upcoming catalogs, such as Gaia DR4, LSST, and JWST, among others. Open clusters are groups of stars that are bounded by their intrinsic gravity and share similar properties, such as a homogeneous distribution of chemical abundances and ages, and also a mean kinematics consistent with their center of mass. Their number of stars does not exceed a few thousand and, due to their weak gravitational field, they exhibit asymmetric shapes, unlike their counterparts, globular clusters. Since the first release of the Gaia astrometric mission, the unveiling of many astrophysical phenomena has now been put in the context of stellar populations and Galactic dynamics. Its exquisite astrometric data for nearly 2 billion stars is unprecedented in the growing understanding of the properties and kinematics of our Galaxy. Particularly, in the context of open clusters using Gaia data, efforts are focused on star evaporation, cluster dissolution, tidal tails and binary stars, topics that are relevant to many astrophysical phenomena. This PhD thesis focuses on the following topics: star membership in nearby Galactic open clusters, Initial Mass Function, mass segregation, binary stars and tidal tails. To perform the star membership, clustering algorithms were implemented, and outliers were treated by a statistical approach using the Mahalanobis distance. Once the most probable members were obtained, the cluster parameters were characterized by implementing a Bayesian methodology using color-magnitude diagrams with a stellar isochrone model. Moreover, a study of the dissolution of one nearby open clusters is discussed by comparing N-body simulations with Gaia DR3 data, which also includes an application of the convergent-point method to recover stars that follow the cluster kinematics. In addition to this, a binary study was also performed on fifteen open cluster by applying binary corrections to the Initial Mass Function through simulated-based inference. This thesis document is structured on five chapters. Below there is a description of each of them. Chapter one contains an overall introduction to open clusters and how they evolve. Chapter two is based on the application of two methods to recover stars in three nearby open clusters, which include a classical approach using proper motions and the DBSCAN algorithm. Chapter three describes the methodology to perform the HDBSCAN algorithm to astrometry and photometry in order to obtain stellar members for 370 open clusters, and the treatment of outliers using a robust statistical method. Chapter four contains a description of the tidal tails in the Pleiades open cluster and includes an analysis of contaminants. Chapter five outlines the process for estimating masses for main sequence stars in fifteen open clusters, discussing the Initial Mass Function while considering binary stars and mass segregation.DoctoradoAstronomy and Astrophysics100 páginasapplication/pdfengUniversidad de los AndesDoctorado en Ciencias - FísicaFacultad de CienciasDepartamento de FísicaAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfOpen cluster dynamicsTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttps://purl.org/redcol/resource_type/TDOpen clustersAstrometryN-body simulationsInitial mass functionMass segregationMachine learningStatisticsFísicaAlfonso, J. & García-Varela, A. 2023, A&A, 677, A163Alfonso, J., García-Varela, A., & Vieira, K. 2024, A&A, 689, A18Allen, C. & Santillan, A. 1991, RMxAA, 22, 255Allison, R. 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