24 citations · 26 across the 3 of their papers we have counts for
5 papers
Artificial Intelligence Assisted Inversion (AIAI): Quantifying the Spectral Features of Ni of Type Ia Supernovae
Xingzhuo Chen, Lifan Wang, Lei Hu +1
Following our previous study of Artificial Intelligence Assisted Inversion (AIAI) of supernova analyses (Chen et al. 2020), we train a set of deep neural networks based on the one-…
Using Physics Informed Neural Networks for Supernova Radiative Transfer Simulation
Xingzhuo Chen, David J. Jeffery, Ming Zhong +3
We use physics informed neural networks (PINNs) to solve the radiative transfer equation and calculate a synthetic spectrum for a Type Ia supernova (SN~Ia) SN 2011fe. The calculati…
Spectroscopic Studies of Type Ia Supernovae Using LSTM Neural Networks
Lei Hu, Xingzhuo Chen, Lifan Wang
We present a data-driven method based on long short-term memory (LSTM) neural networks to analyze spectral time series of Type Ia supernovae (SNe Ia). The dataset includes 3091 spe…
Artificial Intelligence Assisted Inversion (AIAI) of Synthetic Type Ia Supernova Spectra
Xingzhuo Chen, Lei Hu, Lifan Wang
We generate 100,000 model spectra of Type Ia Supernovae (SNIa) to form a spectral library for the purpose of building an Artificial Intelligence Assisted Inversion (AIAI) al…
A New Method to Classify Type IIP/IIL Supernovae Based on their Spectra
Xingzhuo Chen, Shihao Kou, Xuewen Liu
Type IIP and type IIL supernovae (SNe) are defined on their light curves, but the spectrum criteria in distinguishing these two type SNe remains unclear. We propose a new classific…