Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations

TL;DR

This paper introduces a machine learning-based surrogate model to efficiently simulate supernova feedback in galaxy formation, significantly reducing computational costs while improving accuracy over traditional sub-grid models.

Contribution

The authors develop a novel method combining machine learning and Gibbs sampling to accurately predict supernova effects, replacing traditional sub-grid models in galaxy simulations.

Findings

Model outperforms low-resolution SN simulations in energy and momentum accuracy.

Reduces computational cost to approximately 1% of direct resolution methods.

Enables more realistic and efficient galaxy formation simulations.

Abstract

Some stars are known to explode at the end of their lives, called supernovae (SNe). The substantial amount of matter and energy that SNe release provides significant feedback to star formation and gas dynamics in a galaxy. SNe release a substantial amount of matter and energy to the interstellar medium, resulting in significant feedback to star formation and gas dynamics in a galaxy. While such feedback has a crucial role in galaxy formation and evolution, in simulations of galaxy formation, it has only been implemented using simple {\it sub-grid models} instead of numerically solving the evolution of gas elements around SNe in detail due to a lack of resolution. We develop a method combining machine learning and Gibbs sampling to predict how a supernova (SN) affects the surrounding gas. The fidelity of our model in the thermal energy and momentum distribution outperforms the low-resolution SN simulations. Our method can replace the SN sub-grid models and help properly simulate un-resolved SN feedback in galaxy formation simulations. We find that employing our new approach reduces the necessary computational cost to $\sim$ 1 percent compared to directly resolving SN feedback.