"Artificial Intelligence Assisted Inversion of Supernova Observations"

par Lifan Wang (Texas A&M University)

lundi 9 janv. 2023, 14:00 → 15:00 Europe/Paris

Salle des séminaires (LPNHE)

We present a data-driven method based on neural networks to analyze the observational data and theoretical models of Type Ia supernova (SN Ia). The method allows for accurate reconstruction of the spectral sequence of an SN Ia from soon after the explosion to about a month past the optical maximum based on a single observed spectrum around maximum light. The precision of the spectral reconstruction increases moderately with more spectral time coverages; the significant benefit of multiple epoch data at around optical maximum is only evident for observations separated by more than a week. Neural networks are also constructed to model inversely map observational data to the ejecta structure of SNe Ia. The method shows great power in extracting the physical properties of SNe Ia using the observed data. It suggests that the most critical information of an SN Ia can be derived from a single spectrum around the optical maximum. The algorithm we have developed is important for the planning of spectroscopic follow-up observations of future SN surveys with the LSST/Rubin and the WFIRST/Roman telescopes.