58 citations · 126 across the 14 of their papers we have counts for
4 papers · 1 filter
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…
Constraints on Cosmological Parameters with a Sample of Type Ia Supernovae from JWST
Jia Lu, Lifan Wang, Xingzhuo Chen +7
We investigate the potential of using a sample of very high-redshift () (VHZ) Type Ia supernovae (SNe~Ia) attainable by the James Webb Space Telescope (JWST)…
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…