Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers

D. Huppenkothen, M. Ntampaka, M. Ho, M. Fouesneau, B. Nord, J. E. G. Peek, M. Walmsley, J. F. Wu, C. Avestruz, T. Buck, M. Brescia, D. P. Finkbeiner, A. D. Goulding, T. Kacprzak, P. Melchior, M. Pasquato, N. Ramachandra, Y.-S. Ting, G. van de Ven, S. VillarV. A. Villar, E. Zinger

Publications: Contribution to journalArticlePeer Reviewed

Abstract

Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best practices, challenges, and drawbacks, which, at present, are often reported on incompletely in the astrophysical literature. With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method.
Original languageEnglish
JournalBulletin of the American Astronomical Society
Publication statusPublished - Oct 2023

Austrian Fields of Science 2012

  • 103003 Astronomy
  • 103004 Astrophysics

Keywords

  • Astrophysics - Instrumentation and Methods for Astrophysics
  • Computer Science - Machine Learning

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