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

• DOI: 10.48550/arxiv.2310.12528
• Publication Date: 2023-01-01
• Authors: Daniela Huppenkothen; Michelle Ntampaka; Matthew Ho; M. Fouesneau; B. Nord; J. E. G. Peek; Mike Walmsley; John F. Wu; Camille Avestruz; Tobias Buck; M. Brescia; Douglas P. Finkbe...; Andy D. Goulding; Tomasz Kacprzak; P. Melchior; Mario Pasquato; Nesar Ramachandra; Yuan-Sen Ting; Glenn van de Ven; Sergio Serrano‐Vi...; V. Ashley Villar; Elad Zinger
• Keywords: Best practice

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