Machine Can Automatically Discover Parametric Functions to Model HEP Data

Abstract: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 $\chi^2/\text{NDF}\approx 1$, and 111 of the runs rediscovered the very dijet and UA2 functions used in published dijet searches.

6 pages. to be presented at ICHEP 2026

High Energy Physics - Experiment (hep-ex); Machine Learning (cs.LG)

arXiv:2607.19750 [hep-ex]

(or arXiv:2607.19750v1 [hep-ex] for this version)

https://doi.org/10.48550/arXiv.2607.19750 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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