机器自动发现高能物理数据的参数化函数，替代人工试错。
Machine Can Automatically Discover Parametric Functions to Model HEP Data
- 用符号回归在函数空间中搜索最优模型，无需预设形式。
- 1000个函数中约90%拟合效果良好（χ²/NDF≈1），111次复现已发表函数。
- 适合高能物理数据分析人员，可提升建模效率与客观性。
在高能物理数据分析中，为分箱数据寻找合适函数通常依赖人工试错：凭直觉猜测函数形式，拟合并检验，反复迭代直至成功。本文展示该过程可通过符号回归实现自动化，该方法在无需先验知识的情况下，进行数据驱动的函数空间搜索。我们提出SymbolFit工具包，结合符号回归与不确定性建模，针对高能物理分析需求。在CMS和ATLAS Run 2二喷注谱上进行了验证：在7种简单拟合配置下共执行560次独立种子运行，生成超过1000个候选函数，其中约90%的拟合结果达到χ²/自由度≈1；111次运行成功复现了已发表二喷注分析中使用的UA2及二喷注函数。
原文摘要 · Abstract (English)
In HEP data analyses, finding an adequate function to model binned data has largely relied on a manual process: guess a functional form by intuition, fit, examine, then repeat until successful. We show that this iterative process can be automated by a machine using symbolic regression, which performs a data-driven search over function space without requiring prior knowledge of what an adequate function should look like. We present the SymbolFit package, which pairs symbolic regression with uncertainty modeling to target HEP analysis use cases, and demonstrate it on the CMS and ATLAS Run 2 dijet spectra: 560 independent seeded runs across seven simple fit configurations generated over 1000 functions fitting the spectra with $χ^2/\text{NDF}\approx 1$, and 111 of the runs rediscovered the very dijet and UA2 functions used in published dijet searches.
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