The Role of Machine Learning in the Next Decade of Cosmology

2019/02/26 by Michelle Ntampaka, Camille Avestruz, Ntampaka, Michelle +57 · 2 citations
Physics and Astronomy · #Astronomy and Astrophysical Research #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences #Galaxies: Formation, Evolution, Phenomena #Gamma-ray bursts and supernovae #Instrumentation and Methods for Astrophysics (astro-ph.IM)

paper · pdf · doi:10.48550/arxiv.1902.10159

openalex publication_date 2019/02/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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