论文标题
机器比理论家聪明：带有符号回归的粒子运动学公式
Is the Machine Smarter than the Theorist: Deriving Formulas for Particle Kinematics with Symbolic Regression
论文作者
论文摘要
我们证明了符号回归在得出分析公式中的使用，这是在对撞机现象学中典型实验分析的各个阶段所需的。作为第一个应用程序，我们考虑了运动变量，例如stransverse质量，$ m_ {t2} $，它们是通过优化过程而不是根据分析公式来定义算法的。然后，我们训练符号回归，并为文献中所有已知的$ M_ {T2} $的特殊情况获得正确的分析表达式。作为第二个应用程序，我们从数据中复制了正确的分析表达式（NLO）运动分布，该分布通过NLO事件生成器模拟。最后，我们在检测器模拟后得出了NLO运动学分布的分析近似，目前尚无已知的分析公式。
We demonstrate the use of symbolic regression in deriving analytical formulas, which are needed at various stages of a typical experimental analysis in collider phenomenology. As a first application, we consider kinematic variables like the stransverse mass, $M_{T2}$, which are defined algorithmically through an optimization procedure and not in terms of an analytical formula. We then train a symbolic regression and obtain the correct analytical expressions for all known special cases of $M_{T2}$ in the literature. As a second application, we reproduce the correct analytical expression for a next-to-leading order (NLO) kinematic distribution from data, which is simulated with a NLO event generator. Finally, we derive analytical approximations for the NLO kinematic distributions after detector simulation, for which no known analytical formulas currently exist.
