Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers
It offers guidance for researchers and reviewers in astronomy to improve ML application reporting, but is incremental as it synthesizes existing practices.
The paper addresses the incomplete reporting of machine learning best practices in astronomy by providing a primer to ensure accuracy, reproducibility, and usefulness of methods.
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.