Detecting anomaly in vector boson scattering

TL;DR

This paper introduces a neural network approach that compresses vector boson scattering features into a latent space, enabling efficient anomaly detection, polarization mode distinction, and new physics constraints in high-energy physics experiments.

Contribution

The paper presents a novel neural network method for analyzing VBS data by compressing features into a latent space and applying likelihood analysis for anomaly detection and new physics constraints.

Findings

Network distinguishes polarization modes in WWjj production

Method constrains effective field theory parameters

Sensitive to generic new physics in VBS

Abstract

Measuring the vector boson scattering (VBS) precisely is an important step towards understanding the electroweak symmetry breaking of the standard model (SM) and detecting new physics beyond the SM. We propose a neural network which compress the features of the VBS into three dimensional latent space. The consistency of the SM prediction and the experimental data is tested by the binned log-likelihood analysis in the latent space. We will show that the network is capable of distinguish different polarization modes of $WWjj$ production in both dileptonic channel and semi-leptonic channel. The method is also applied to constrain the effective field theory and two Higgs Doublet Model. The results demonstrate that the method is sensitive to generic new physics contributing to the VBS.