What is it about?

This article is at the intersection of astrophysics and artificial intelligence. Two-dimensional power-law relationships are used extremely frequently in astrophysics. We show machine learning can find more accurate relations in higher dimensions. Deep neural networks are powerful but not interpretable, we use a machine learning tool which models patterns in a dataset in the form of interpretable equations.

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Why is it important?

Numerous upcoming observational surveys want to measure masses of clusters of galaxies in order to more accurately infer the fundamental properties of the Universe. Our new equations helps make the mass inference more accurate.


Deep neural networks are ubiquitously used to

Digvijay Wadekar
Institute for Advanced Study

Read the Original

This page is a summary of: Augmenting astrophysical scaling relations with machine learning: Application to reducing the Sunyaev–Zeldovich flux–mass scatter, Proceedings of the National Academy of Sciences, March 2023, Proceedings of the National Academy of Sciences,
DOI: 10.1073/pnas.2202074120.
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