3D-Spatiotemporal Forecasting the Expansion of Supernova Shells Using Deep Learning toward High-Resolution Galaxy Simulations

TL;DR

This paper introduces a deep learning model called 3D-MIM that predicts the expansion of supernova shells in galaxy simulations, aiming to improve the efficiency of high-resolution, computationally intensive astrophysical models.

Contribution

The paper presents a novel deep learning approach for predicting supernova shell expansion, facilitating more efficient galaxy simulations with higher resolution.

Findings

The model accurately reproduces anisotropic shell shapes.

It predicts shell radius effectively in both turbulent and uniform media.

The approach enables forecasting of SN-affected regions in simulations.

Abstract

Supernova (SN) plays an important role in galaxy formation and evolution. In high-resolution galaxy simulations using massively parallel computing, short integration timesteps for SNe are serious bottlenecks. This is an urgent issue that needs to be resolved for future higher-resolution galaxy simulations. One possible solution would be to use the Hamiltonian splitting method, in which regions requiring short timesteps are integrated separately from the entire system. To apply this method to the particles affected by SNe in a smoothed-particle hydrodynamics simulation, we need to detect the shape of the shell on and within which such SN-affected particles reside during the subsequent global step in advance. In this paper, we develop a deep learning model, 3D-MIM, to predict a shell expansion after a SN explosion. Trained on turbulent cloud simulations with particle mass $m_{\rm gas}=1$M$_\odot$, the model accurately reproduces the anisotropic shell shape, where densities decrease by over 10 per cent by the explosion. We also demonstrate that the model properly predicts the shell radius in the uniform medium beyond the training dataset of inhomogeneous turbulent clouds. We conclude that our model enables the forecast of the shell and its interior where SN-affected particles will be present.