A White-box Approach to Targeting Cataclysmic Variables Using Symbolic Regression

Abstract

The growing volume of astronomical data demands automated classification methods to identify celestial objects, such as Cataclysmic Variables. While deep learning models achieve high accuracy, their “black-box” nature often obscures the underlying physical relationships, limiting the potential for deeper scientific insights. To bridge this interpretability gap, this study introduces a novel white-box approach to photometric data classification using Symbolic Regression (SR). Unlike conventional machine learning algorithms, SR generates explicit mathematical expressions and logical rules directly from observational data, providing unparalleled transparency and facilitating physical interpretation. Leveraging a data set of photometric data from SDSS DR17, we use the PySR framework to automatically synthesize a robust, interpretable classification criterion. Our comparative experiments show that this SR-derived criterion not only outperforms several popular machine learning models but also surpasses existing empirical formulae in classification accuracy. The analytical model derived from SR offers significantly higher inference throughput, a crucial advantage for large-scale astronomical surveys. These findings highlight the immense potential of SR as a powerful, transparent, and physically insightful tool for advancing astronomical research. It enables not only efficient classification but also the discovery of new astrophysical insights, offering a clearer path toward understanding the underlying physical processes of celestial phenomena. All associated code and data are publicly available at Github.4 4 https://github.com/yuexiuya1/Cataclysmic-Variable-SR https://github.com/yuexiuya1/Cataclysmic-Variable-SR