Galaxy Formation
Galaxy formation research aims to understand how galaxies assemble from dark matter and gas, a process governed by complex interplay between gravity, hydrodynamics, and stellar feedback. Current efforts leverage machine learning, employing generative models like diffusion models and neural operators, and graph neural networks to efficiently simulate galaxy populations, predict properties like intrinsic alignments and dark matter distributions, and emulate computationally expensive processes such as supernova feedback and interstellar medium chemistry. These advancements enable more accurate cosmological inferences from large-scale surveys and improve our understanding of galaxy evolution across cosmic time, ultimately refining our models of the universe's structure and evolution.
Papers
Probabilistic reconstruction of Dark Matter fields from biased tracers using diffusion models
Core Francisco Park, Victoria Ono, Nayantara Mudur, Yueying Ni, Carolina Cuesta-Lazaro
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