2026-04-17T03:07:49Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/3512442025-07-17T15:38:05Zcom_2072_1033col_2072_452951

urn:hdl:2117/351244 Machine learning classification of the Gaia 3rd data release based on a random forest algorithm Lopez Moreno, Sergio Àrees temàtiques de la UPC::Física::Astronomia i astrofísica White dwarf stars Stars Machine Learning Random Forest Algorithm Gaia Space Mission Simulation Classification Estels A new Gaia data release, EDR3, has been available since the end of last year containing a complete catalogue of nearly 2 billion stars. This huge wealth of information provided by this magnificent space astronomical mission needs to be analyzed and studied in detail. In particular, we have fixated on a specific type of star, named white dwarf, and we have studied to which population of the Galaxy they belong. This knowledge can help us to understand how our Galaxy was formed and also help to discover large scale Galactic events which are matter of debate. This project is focused on obtaining the most adequate possible classification of the white dwarf population among the three main components of our Galaxy (thin and thick disk and halo) by means of artificial intelligence techniques, and more specifically, machine learning. The algorithm chosen for our purpose has been the random forest algorithm, which, as demonstrated in previous works, has already produced very positive results when applied to the second Gaia data release. We reproduce the results previously obtained for the white dwarf 100 pc Gaia DR2 sample, and extend the analysis to the new EDR3 up to 500 pc. The number of white dwarf finally classified by our algorithm have increased from 10,000 in DR2 up to 80,000 in EDR3. Thus, we have managed to identify nearly 300 halo white dwarf candidates. 2021-09-13 Master thesis http://creativecommons.org/licenses/by-nc-sa/3.0/es/ Open Access Universitat Politècnica de Catalunya