Speaker
Description
Understanding the mechanisms that govern star formation in galaxies is essential to uncover 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 in the resolved properties of galaxies, whereby massive galaxies form stars more rapidly than their lower-mass counterparts.
In addition, Machine Learning techniques will be used to identify HI holes in these galaxies, with the aim of achieving a deeper understanding of how different processes influence galactic evolution. For this, a u-net architecture is employed to quickly identify holes in the atomic gas of a sample of galaxies. Using these, we will further analyze the physics surrounding these structures.