Extract the energy scale of anomalous $\gamma\gamma \to W^+W^-$ scattering in the vector boson scattering process using artificial neural networks
Ji-Chong Yang, Jin-Hua Chen, Yu-Chen Guo

TL;DR
This paper employs artificial neural networks to accurately reconstruct the energy scale of anomalous gamma-gamma to W+W- scattering in vector boson scattering at the LHC, aiding in the search for new physics beyond the Standard Model.
Contribution
It introduces an ANN-based method to determine the energy scale in complex collider processes, improving upon traditional kinematic analysis and enhancing sensitivity to anomalous couplings.
Findings
ANN effectively reconstructs the energy scale of gamma-gamma to W+W- scattering.
The approximate formula for ANN interpretation outperforms kinematic-based approximations.
Applying unitarity bounds with ANN reduces background, improving sensitivity to new physics.
Abstract
As a model independent approach to search for the signals of new physics~(NP) beyond the Standard Model~(SM), the SM effective field theory~(SMEFT) draws a lot of attention recently. The energy scale of a process is an important parameter in the study of an EFT such as the SMEFT. However, for the processes at a hadron collider with neutrinos in the final states, the energy scales are difficult to reconstruct. In this paper, we study the energy scale of anomalous scattering in the vector boson scattering~(VBS) process at the large hadron collider~(LHC) using artificial neural networks~(ANNs). We find that the ANN is a powerful tool to reconstruct the energy scale of scattering. The factors affecting the effects of ANNs are also studied. In addition, we make an attempt to interpret the ANN and arrive…
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