Data - Apps - AI - Cybernetics

Constructing Impactful Machine Learning Research For Astronomy: Best Practices For Researchers And Reviewers

By Keith Cowing
Status Report
October 23, 2023
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Constructing Impactful Machine Learning Research For Astronomy: Best Practices For Researchers And Reviewers
Approximate trade-off between flexibility and interpretability of ML models for astronomy. Here, high flexibility generally result in very strong fits to complex training data, but these models may suffer from overfitting, slow computation time, and poor generalization, in addition to non-interpretability. Low flexibility models may be unable to fit extremely complex problems, but will often be the preferred solution in many astronomy problems where simpler baselines have been established. We note that the axes of this plot are qualitative and, in practice, will depend highly on the specific problem. Each referenced model class includes a citation of an example application paper in astronomy. — astro-ph.IM

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.

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

Comments: 14 pages, 3 figures; submitted to the Bulletin of the American Astronomical Society
Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG)
Cite as: arXiv:2310.12528 [astro-ph.IM] (or arXiv:2310.12528v1 [astro-ph.IM] for this version)
Submission history
From: Daniela Huppenkothen
[v1] Thu, 19 Oct 2023 07:04:36 UTC (130 KB)
Astrobiology, Astronomy, Astrochemisty,

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