Surrogate Modeling for Computationally Expensive Simulations of
  Supernovae in High-Resolution Galaxy Simulations
Keiya Hirashima Kana Moriwaki Michiko S. Fujii Yutaka Hirai Takayuki R. Saitoh Junichiro Makino Shirley Ho
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.