STUDYING STELLAR POPULATIONS WITH MACHINE LEARNING

G. BAUME; E. GULARTE; M.J. RODRIGUEZ; C. FEINSTEIN

Córdoba

Congreso; 10th Friends of Friends Meeting; 2021

Observatorio de Córdoba

Stellar populations in NGC 1313 galaxy were studied using multi-band photometric data obtained with the Hubble Space Telescope. Machine learning techniques were applied to recognize both the stellar populations and the groups of stars in the youngest population. In both cases, different clustering algorithms were used and their efficiency was evaluated. Additionally, we characterized the spatial distribution of each population. It was possible to identify the youngest populations with a hierarchical structure and the most evolved ones with a homogeneous distribution, except for very large-scale fluctuations.