Machine Phenomenology: A Simple Equation Classifying Fast Radio Bursts
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918230847651840 |
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| author | Liu, Yang Lu, Yuhao Moradi, Rahim Yang, Bo Zhang, Bing Lin, Wenbin Wang, Yu |
| author_facet | Liu, Yang Lu, Yuhao Moradi, Rahim Yang, Bo Zhang, Bing Lin, Wenbin Wang, Yu |
| contents | This work shows how human physical reasoning can guide machine-driven symbolic regression toward discovering empirical laws from observations. As an example, we derive a simple equation that classifies fast radio bursts (FRBs) into two distinct Gaussian distributions, indicating the existence of two physical classes. This human-AI workflow integrates feature selection, dimensional analysis, and symbolic regression: deep learning first analyzes CHIME Catalog 1 and identifies six independent parameters that collectively provide a complete description of FRBs; guided by Buckingham-$π$ analysis and correlation analysis, humans then construct dimensionless groups; finally, symbolic regression performed by the machine discovers the governing equation. When applied to the newer CHIME Catalog, the equation produces consistent results, demonstrating that it captures the underlying physics. This framework is applicable to a broad range of scientific domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04204 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Phenomenology: A Simple Equation Classifying Fast Radio Bursts Liu, Yang Lu, Yuhao Moradi, Rahim Yang, Bo Zhang, Bing Lin, Wenbin Wang, Yu Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena Artificial Intelligence This work shows how human physical reasoning can guide machine-driven symbolic regression toward discovering empirical laws from observations. As an example, we derive a simple equation that classifies fast radio bursts (FRBs) into two distinct Gaussian distributions, indicating the existence of two physical classes. This human-AI workflow integrates feature selection, dimensional analysis, and symbolic regression: deep learning first analyzes CHIME Catalog 1 and identifies six independent parameters that collectively provide a complete description of FRBs; guided by Buckingham-$π$ analysis and correlation analysis, humans then construct dimensionless groups; finally, symbolic regression performed by the machine discovers the governing equation. When applied to the newer CHIME Catalog, the equation produces consistent results, demonstrating that it captures the underlying physics. This framework is applicable to a broad range of scientific domains. |
| title | Machine Phenomenology: A Simple Equation Classifying Fast Radio Bursts |
| topic | Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena Artificial Intelligence |
| url | https://arxiv.org/abs/2512.04204 |