Comparison of symbolic regression algorithms in Star/galaxy/quasar separation
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arXiv
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| Natura: | Preprint |
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2026
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| author | Deshpande, Rachit Desai, Shantanu |
| author_facet | Deshpande, Rachit Desai, Shantanu |
| contents | This work investigates symbolic regression (SR) as an interpretable alternative to black-box machine learning for the classification of stars, galaxies, and quasars in the Sloan Digital Sky Survey Data Release 17 (SDSS DR17). We conduct a systematic comparative study of four state-of-the-art SR frameworks: {\tt PySR}, Exhaustive Symbolic Regression ({\tt ESR}) with MDL-based selection, Physical Symbolic Optimization ({\tt PhySO}) using deep reinforcement learning, and Multi-View Symbolic Regression ({\tt MvSR}). By deriving compact analytic functions (complexity $\leq 10$) on a representative training subset and subsequently evaluating them via a 5-fold stratified cross-validation protocol on 100,000 spectroscopically confirmed objects, we map spectroscopic redshift ($z$) to continuous classification scores. Our results demonstrate that these low-complexity expressions achieve high predictive reliability, with {\tt MvSR} reaching a Cohen's Kappa of 0.8948 and {\tt PhySO} achieving exceptional parametric stability ($σ< 0.002$). We show that these models not only match the performance of traditional baselines but also provide a transparent, mathematically concise characterization of the astrophysical boundaries separating galactic and extragalactic populations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_24022 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Comparison of symbolic regression algorithms in Star/galaxy/quasar separation Deshpande, Rachit Desai, Shantanu Instrumentation and Methods for Astrophysics Astrophysics of Galaxies This work investigates symbolic regression (SR) as an interpretable alternative to black-box machine learning for the classification of stars, galaxies, and quasars in the Sloan Digital Sky Survey Data Release 17 (SDSS DR17). We conduct a systematic comparative study of four state-of-the-art SR frameworks: {\tt PySR}, Exhaustive Symbolic Regression ({\tt ESR}) with MDL-based selection, Physical Symbolic Optimization ({\tt PhySO}) using deep reinforcement learning, and Multi-View Symbolic Regression ({\tt MvSR}). By deriving compact analytic functions (complexity $\leq 10$) on a representative training subset and subsequently evaluating them via a 5-fold stratified cross-validation protocol on 100,000 spectroscopically confirmed objects, we map spectroscopic redshift ($z$) to continuous classification scores. Our results demonstrate that these low-complexity expressions achieve high predictive reliability, with {\tt MvSR} reaching a Cohen's Kappa of 0.8948 and {\tt PhySO} achieving exceptional parametric stability ($σ< 0.002$). We show that these models not only match the performance of traditional baselines but also provide a transparent, mathematically concise characterization of the astrophysical boundaries separating galactic and extragalactic populations. |
| title | Comparison of symbolic regression algorithms in Star/galaxy/quasar separation |
| topic | Instrumentation and Methods for Astrophysics Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2602.24022 |