Research
During my Ph.D. at Pontificia Universidad Católica de Chile I have been working on spectro-photometric analysis of white dwarf atmospheres with machine learning. Motivated by the large scale surveys upcoming in the following years, and under the supervision of Dr. Claudia Aguilera-Gómez, I have been working to develop an efficient machine learning based pipeline to estimate stellar parameters for white dwarfs, based on their photometric and/or spectroscopic observations.
This idea was born during the last year of my Bachelors, where I tested mass-radius relations of white dwarfs by predicting stellar parameters for UV spectroscopy of a small sample of stars, while working in Dr. Odette Toloza's group, at Universidad Técnica Federico Santa María.
Previously, I had the chance to work with Dr. Matthias Schreiber, on a one-semester long project that focused on modeling cataclysmic variable evolution using MESA to test a magnetic braking prescription (Ortúzar-Garzón et al. (2024)).
Publications
- Suggested magnetic braking prescription derived from field complexity fails to reproduce the cataclysmic variable orbital period gap, Ortúzar-Garzón, V., et al.: A&A, 690, L1 (2024)
Curriculum Vitae
You can access my full CV from the research page here: Download my resume.