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arXiv · 2211.08420

Is the Machine Smarter than the Theorist: Deriving Formulas for Particle Kinematics with Symbolic Regression

Zhongtian Dong·Kyoungchul Kong·Konstantin T. Matchev·Katia Matcheva
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Abstract

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.

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Zhongtian Dong, Kyoungchul Kong, Konstantin T. Matchev, Katia Matcheva. 2022-11-15. Is the Machine Smarter than the Theorist: Deriving Formulas for Particle Kinematics with Symbolic Regression. https://doi.org/10.1103/physrevd.107.055018

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