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

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