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

Autor(en)

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. Villar, V. A. Villar, E. Zinger

Abstrakt

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.

Organisation(en)

Institut für Astrophysik

Journal

Bulletin of the American Astronomical Society

ISSN

0002-7537

Publikationsdatum

10-2023

Peer-reviewed

Ja

ÖFOS 2012

103003 Astronomie, 103004 Astrophysik

Schlagwörter

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/475e78c7-3f55-4520-bd8d-9b556eb0e0db