Postdoctoral Fellowship: AAPF: Cool Stars, Hot Mess: Towards Accurate Modeling of M Dwarfs to Unveil Rocky Worlds

Ana Glidden is awarded an NSF Astronomy and Astrophysics Fellowship to carry out a program of research and education at the Smithsonian Astrophysical Observatory. This project will build the foundation necessary to assess habitability and detect biosignature gases with the next generation of extremely large telescopes. The project will also offer interactive presentations on current research with local middle and high school students as well as provide mentorship on applying to graduate school to undergraduate researchers at the institution. Glidden will quantitatively establish the performance requirements for M dwarf stellar models to reach terrestrial atmospheres and create the first semi-empirical grid that comprehensively incorporates 3D stellar surface magnetic features. This will be accomplished by using machine learning to optimize the grid information content, while minimizing computational costs. The investigator will work with colleagues at the institutional AstroAI group to explore feature importance methods (e.g., random forests or gradient-boosted trees) and supervised deep learning methods. Finally, Glidden will develop a deep-learning Bayesian retrieval framework to simultaneously constrain stellar surface inhomogeneities and planetary atmospheres. This project will produce open-source tools that will serve as the community baseline for interpreting terrestrial exoplanet data observed with the upcoming Giant Magellan Telescope (GMT). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2602456 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Ana Glidden | Institution: Glidden, Ana, Cambridge, MA | Award Amount: $330,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2602456 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2602456.html