%0 Generic %1 dvorkin2022machine %A Dvorkin, Cora %A Mishra-Sharma, Siddharth %A Nord, Brian %A Villar, V. Ashley %A Avestruz, Camille %A Bechtol, Keith %A Ćiprijanović, Aleksandra %A Connolly, Andrew J. %A Garrison, Lehman H. %A Narayan, Gautham %A Villaescusa-Navarro, Francisco %D 2022 %K cosmology lss machine_learning review %T Machine Learning and Cosmology %U http://arxiv.org/abs/2203.08056 %X 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.

@misc{dvorkin2022machine, 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.}, added-at = {2022-12-31T13:17:35.000+0100}, author = {Dvorkin, Cora and Mishra-Sharma, Siddharth and Nord, Brian and Villar, V. Ashley and Avestruz, Camille and Bechtol, Keith and Ćiprijanović, Aleksandra and Connolly, Andrew J. and Garrison, Lehman H. and Narayan, Gautham and Villaescusa-Navarro, Francisco}, biburl = {https://www.bibsonomy.org/bibtex/2236e01b47af3f05d36053401ee8b5b2b/intfxdx}, description = {Machine Learning and Cosmology}, interhash = {c274f7b2bf052fbadc8ed1dde45548df}, intrahash = {236e01b47af3f05d36053401ee8b5b2b}, keywords = {cosmology lss machine_learning review}, note = {cite arxiv:2203.08056Comment: Contribution to Snowmass 2021. 32 pages}, timestamp = {2022-12-31T13:17:35.000+0100}, title = {Machine Learning and Cosmology}, url = {http://arxiv.org/abs/2203.08056}, year = 2022 }