VBF Event Classification with Recurrent Neural Networks at ATLAS’s LHC Experiment

Abstract

A novel machine learning (ML) approach based on a recurrent neural network (RNN) for event topology identification in high energy physics (HEP) is presented. The vector-boson fusion (VBF) production mechanism arising in proton-to-proton collisions is predicted both from the current theoretical model of the particle physics, the standard model, and from its extensions that foresee potential new physics phenomena. This physical process has a well-defined event topology in the final state and a distinctive detector signature. In this work, an ML approach based on the RNN architecture is developed to deal with hadronic-only event information in order to enhance the acceptance of this production mechanism in physics analysis of the data. This technique was applied to a physics analysis in the context of high-mass diboson resonance searches using data collected by the ATLAS experiment.