Machine Learning and Cosmology
Abstract
Methods based on machine learning have recently made substantial inroads in many corners of cosmology. Through this process, new computational tools, new perspectives on data collection, model development, analysis, and discovery, as well as new communities and educational pathways have emerged. Despite rapid progress, substantial potential at the intersection of cosmology and machine learning remains untapped. In this white paper, we summarize current and ongoing developments relating to the application of machine learning within cosmology and provide a set of recommendations aimed at maximizing the scientific impact of these burgeoning tools over the coming decade through both technical development as well as the fostering of emerging communities.
Keywords
Cite
@article{arxiv.2203.08056,
title = {Machine Learning and Cosmology},
author = {Cora Dvorkin and Siddharth Mishra-Sharma and Brian Nord and V. Ashley Villar and Camille Avestruz and Keith Bechtol and Aleksandra Ćiprijanović and Andrew J. Connolly and Lehman H. Garrison and Gautham Narayan and Francisco Villaescusa-Navarro},
journal= {arXiv preprint arXiv:2203.08056},
year = {2022}
}
Comments
Contribution to Snowmass 2021. 32 pages