Speaker
Mr
Guillermo Valé Arteaga
Description
Understanding the mechanisms governing star formation in galaxies is key to unravel their evolution. This work employs Bayesian statistics and data mining techniques to analyze metallicity gradients, which provide valuable insights into the processing and enrichment of gas in galaxies. A notable trend is the manifestation of the downsizing effect the in resolved properties of galaxies, where massive galaxies form stars more rapidly than their lower-mass counterparts. Additionally, the use of Machine Learning techniques will be used to identify HI holes in these galaxies, with the goal of achieving a deeper understanding on how different processes influence galactic evolution.
