TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.16.1.037
TI  - Back to the formula - LHC edition
PY  - 2024/01/29
UR  - https://scipost.org/SciPostPhys.16.1.037
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 16
IS  - 1
SP  - 037
A1  - Butter, Anja
AU  - Plehn, Tilman
AU  - Soybelman, Nathalie
AU  - Brehmer, Johann
AB  - While neural networks offer an attractive way to numerically encode functions, actual formulas remain the language of theoretical particle physics. We use symbolic regression trained on matrix-element information to extract, for instance, optimal LHC observables. This way we invert the usual simulation paradigm and extract easily interpretable formulas from complex simulated data. We introduce the method using the effect of a dimension-6 coefficient on associated ZH production. We then validate it for the known case of CP-violation in weak-boson-fusion Higgs production, including detector effects.
ER  -

@Article{10.21468/SciPostPhys.16.1.037,
	title={{Back to the formula - LHC edition}},
	author={Anja Butter and Tilman Plehn and Nathalie Soybelman and Johann Brehmer},
	journal={SciPost Phys.},
	volume={16},
	pages={037},
	year={2024},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.16.1.037},
	url={https://scipost.org/10.21468/SciPostPhys.16.1.037},
}