Host: Dong Lai
Location: Online
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Meeting ID: 777306620 (no password）
Abstract:
Type Ia Supernovae (SNe Ia) have been utilized as distance indicators in the modern cosmology researches, owing to the empirical relationships between the peak luminosities and the luminosity decline rate. However, the full details of the underlying explosion mechanism remains unclear despite the commonly agreed thermonuclear explosion picture. In this talk, I will introduce a deep-learning method to estimate the ejecta structure of type Ia supernovae through radiative transfer simulations. I will also introduce a new radiative transfer simulation program for accurate type Ia supernova spectropolarimetry modelling.
Biography:
Dr. Xingzhuo Chen is the postdoctoral research associate in Texas A&M University Institute of Data Science. His research interest focuses on deep-learning application on astrophysical simulations, and explosion mechanism of type Ia supernovae. He has earned B.Sc in Physics from Sichuan University, and has earned PhD in Astronomy from Texas A&M University.