The Role of Machine Learning in the Next Decade of Cosmology

Michelle Ntampaka , Camille Avestruz , Steven Boada , João Caldeira , Jessi Cisewski-Kehe , Rosanne Di Stefano , Cora Dvorkin , August E. Evrard , Arya Farahi , Doug Finkbeiner , Shy Genel , Alyssa Goodman , Andy Goulding , Shirley Ho , Arthur Kosowsky , Paul La Plante , François Lanusse , Michelle Lochner , Rachel Mandelbaum , Daisuke Nagai , Jeffrey A. Newman , Brian Nord , J.E.G. Peek , Austin Peel (1) , Barnabás Póczos , Markus Michael Rau , Aneta Siemiginowska , Dougal J. Sutherland , Hy Trac , Benjamin Wandelt (2, 3)

1 AIM (UMR7158 / UMR_E_9005 / UM_112) - Astrophysique Interprétation Modélisation
2 IAP - Institut d'Astrophysique de Paris
3 Institut Lagrange de Paris

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.

Domaines: Instrumentations et Détecteurs [physics.ins-det]; Astrophysique [astro-ph]

Michelle Ntampaka, Camille Avestruz, Steven Boada, João Caldeira, Jessi Cisewski-Kehe, et al.. The Role of Machine Learning in the Next Decade of Cosmology. 2019. ⟨hal-02065883⟩