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...
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| 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 |
| Sumario: | [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. |
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