报告题目：Deep Learning on Type Ia Supernovae
报 告 人：陈星卓 浙江大学
报告时间：2026年9月24日（星期四）10：00
内容摘要：Type Ia supernovae play a crucial role in cosmology as standardizable candles. These observations are broadly consistent with theoretical models involving the thermonuclear explosion of a carbon-oxygen white dwarf near the Chandrasekhar mass. However, first principles simulations incorporating hydrodynamics, nucleosynthesis, and radiative transfer remain computationally expensive and cannot yet fully reproduce observed features with high precision. In this talk I will introduce our development on type Ia supernova structure estimation assisted with deep learning, and the deep learning application in 3-D radiative transfer simulations.
报告人简介：Dr. Xingzhuo Chen is a theoretical astrophysicist working in Zhejiang University. His research focuses on radiative transfer simulation on supernovae and scientific machine learning on magnetohydrodynamic simulations. He received his Ph.D in Astronomy from Texas A&M University. During his Ph.D, he studied the ejecta structure of type Ia supernovae using deep learning and radiative transfer simulations.
