ArticleProceedings of the National Academy of Sciences of the United States of America2023
Augmenting astrophysical scaling relations with machine learning: Application to reducing the Sunyaev-Zeldovich flux-mass scatter.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Discovering physical laws with parallel symbolic enumeration.Nature computational science · 2026Article
- Interactive symbolic regression with co-design mechanism through offline reinforcement learning.Nature communications · 2025Article
- Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives.Archives of computational methods in engineering : state of the art reviews · 2023Review
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10 authors.
Funding
Abstract
Complex astrophysical systems often exhibit low-scatter relations between observable properties (e.g., luminosity, velocity dispersion, oscillation period). These scaling relations illuminate the underlying physics, and can provide observational tools for estimating masses and distances. Machine learning can provide a fast and systematic way to search for new scaling relations (or for simple extensions to existing relations) in abstract high-dimensional parameter spaces. We use a machine learning tool called symbolic regression (SR), which models patterns in a dataset in the form of analytic equations. We focus on the Sunyaev-Zeldovich flux-cluster mass relation (
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