In recent years a major effort was dedicated to collecting large data sets of high-quality spectra of stars in the Milky Way and to improving data analysis pipelines with machine learning and artificial intelligence. This allowed us to efficiently obtain atmospheric parameters and chemical abundances for hundreds of thousand of stars. However, less effort was dedicated to improving the stellar atmospheric models these analyses depend on. The aim of this project is to improve the understanding of stellar atmospheres by using AI tools to improve the modeling of stellar atmospheres with current technologies.
Type of project: PhD thesis
Status: Ongoing
Researchers: Theo Signor, Paula Jofre, Luis Martíi, Nayat Sánchez-Pi
Funding source: INRIA-Chile.