A Data-driven Approach for Star Formation Parameterization Using Symbolic Regression

Acknowledgements: D.M.S. is supported by the 2022 Future Investigators in NASA Earth and Space Science and Technology (NASA FINESST) Fellowship (NASA grant 80NSSC22K1604). D.M.S. is also grateful for the generous support of the 2023 Quad Fellowship. B.B. acknowledges support from NSF grant AST-2009679 and NASA grant No. 80NSSC20K0500. B.B. is grateful for the generous support of the David and Lucile Packard Foundation and the Alfred P. Sloan Foundation. The Flatiron Institute is supported by the Simons Foundation. All authors are grateful for the use of both GPU and CPU resources on the Rusty Supercomputer cluster at the Simons Foundation Flatiron Institute. All authors are deeply grateful for in-depth discussions with and suggestions from Viraj Pandya, Christian Kragh Jespersen, Chris McKee, Shyam Menon, and Deanne Fisher.

Publication Details

Journal
Apollo (University of Cambridge)
Published
2025-05-07
Primary Topic
Stellar, planetary, and galactic studies
Type
article
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article

A Data-driven Approach for Star Formation Parameterization Using Symbolic Regression

Apollo (University of Cambridge)
Stellar, planetary, and galactic studies
article

A Data-driven Approach for Star Formation Parameterization Using Symbolic Regression

article en

Abstract

Acknowledgements: D.M.S. is supported by the 2022 Future Investigators in NASA Earth and Space Science and Technology (NASA FINESST) Fellowship (NASA grant 80NSSC22K1604). D.M.S. is also grateful for the generous support of the 2023 Quad Fellowship. B.B. acknowledges support from NSF grant AST-2009679 and NASA grant No. 80NSSC20K0500. B.B. is grateful for the generous support of the David and Lucile Packard Foundation and the Alfred P. Sloan Foundation. The Flatiron Institute is supported by the Simons Foundation. All authors are grateful for the use of both GPU and CPU resources on the Rusty Supercomputer cluster at the Simons Foundation Flatiron Institute. All authors are deeply grateful for in-depth discussions with and suggestions from Viraj Pandya, Christian Kragh Jespersen, Chris McKee, Shyam Menon, and Deanne Fisher.

Apollo (University of Cambridge)
Openalex Percentile: Top 36%
Stellar, planetary, and galactic studies
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A Data-driven Approach for Star Formation Parameterization Using Symbolic Regression · Apollo (University of Cambridge) (2025) | TGRS Research Map | TGRS