6 citations · 10 across the 8 of their papers we have counts for
4 papers · 1 filter
Self-supervised Synthetic Pretraining for Inference of Stellar Mass Embedded in Dense Gas
Keiya Hirashima, Shingo Nozaki, Naoto Harada
Stellar mass is a fundamental quantity that determines the properties and evolution of stars. However, estimating stellar masses in star-forming regions is challenging because youn…
The First Star-by-star -body/Hydrodynamics Simulation of Our Galaxy Coupling with a Surrogate Model
Keiya Hirashima, Michiko S. Fujii, Takayuki R. Saitoh +9
A major goal of computational astrophysics is to simulate the Milky Way Galaxy with sufficient resolution down to individual stars. However, the scaling fails due to some small-sca…
Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations
Keiya Hirashima, Kana Moriwaki, Michiko S. Fujii +4
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 st…
3D-Spatiotemporal Forecasting the Expansion of Supernova Shells Using Deep Learning toward High-Resolution Galaxy Simulations
Keiya Hirashima, Kana Moriwaki, Michiko S. Fujii +3
Supernova (SN) plays an important role in galaxy formation and evolution. In high-resolution galaxy simulations using massively parallel computing, short integration timesteps for…