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

TL;DR

This paper offers best practices and guidelines for astronomers and reviewers to effectively implement, report, and evaluate machine learning models, ensuring scientific accuracy, reproducibility, and usefulness in astronomical research.

Contribution

It provides a comprehensive primer on machine learning application and reporting standards tailored for the astronomical community, addressing current gaps in best practices.

Findings

Highlights common challenges and pitfalls in ML applications in astronomy.

Provides a structured framework for reporting ML results.

Emphasizes importance of reproducibility and transparency in ML research.

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.