The Role of Machine Learning in the Next Decade of Cosmology

Autor:	Ntampaka, Michelle, Avestruz, Camille, Boada, Steven, Caldeira, Joao, Cisewski-Kehe, Jessi, Di Stefano, Rosanne, Dvorkin, Cora, Evrard, August E., Farahi, Arya, Finkbeiner, Doug, Genel, Shy, Goodman, Alyssa, Goulding, Andy, Ho, Shirley, Kosowsky, Arthur, La Plante, Paul, Lanusse, Francois, Lochner, Michelle, Mandelbaum, Rachel, Nagai, Daisuke, Newman, Jeffrey A., Nord, Brian, Peek, J. E. G., Peel, Austin, Poczos, Barnabas, Rau, Markus Michael, Siemiginowska, Aneta, Sutherland, Danica J., Trac, Hy, Wandelt, Benjamin
Rok vydání:	2019
Předmět:	Astrophysics - Instrumentation and Methods for Astrophysics Astrophysics - Cosmology and Nongalactic Astrophysics
Druh dokumentu:	Working Paper
Popis:	In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmological discovery, but will also present new challenges for adopting ML methodologies and understanding the results. ML could transform our field, but this transformation will require the astronomy community to both foster and promote interdisciplinary research endeavors. Comment: Submitted to the Astro2020 call for science white papers
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1902.10159 Zobrazit plný text záznamu View this record from Arxiv