PIRSA:21100002

Video URL

https://pirsa.org/21100002

Simulations of Cosmological Structure and Machine Learning

Bird, S. (2021). Simulations of Cosmological Structure and Machine Learning. Perimeter Institute for Theoretical Physics. https://pirsa.org/21100002

Bird, Simeon. Simulations of Cosmological Structure and Machine Learning. Perimeter Institute for Theoretical Physics, Oct. 19, 2021, https://pirsa.org/21100002 

          @misc{ scivideos_PIRSA:21100002,
            doi = {10.48660/21100002},
            url = {https://pirsa.org/21100002},
            author = {Bird, Simeon},
            keywords = {Cosmology},
            language = {en},
            title = {Simulations of Cosmological Structure and Machine Learning},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2021},
            month = {oct},
            note = {PIRSA:21100002 see, \url{https://scivideos.org/index.php/pirsa/21100002}}
          }

Simeon Bird Johns Hopkins University

Talk numberPIRSA:21100002
DOI10.48660/21100002
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

The large scale distribution of matter in the Universe contains the answers to many mysteries, such as the nature of dark matter, the reionization of the Universe, and the growth of galaxies. Cosmological simulations are the only way to understand these questions. I will talk about how modern current simulation models, work, discuss some new models and improvements in our latest simulation runs, especially our implementations of reionization and cosmology. I will then talk about some new work to dramatically expand the region of applicability of these simulations using machine learning. This can both to expand their dynamic range and combine different simulations to infer the physical parameters of the Universe.