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