Use of Anomaly Detection algorithms to unveil new physics in Vector Boson Scattering

Abstract

A new methodology to improve the sensitivity to new physics contributions to the Standard Model processes at LHC is presented. A Variational AutoEncoder trained on Standard Model processes is used to identify Effective Field Theory contributions as anomalies. While the output of the model is supposed to be very similar to the inputs for Standard Model events, it is expected to deviate significantly for events generated through new physics processes. The reconstruction loss can then be used to select a signal enriched region which is by construction independent of the nature of the chosen new physics process. In order to improve further the discrimination power, an adversarial layer is introduced with a cross entropy term added to the loss function, optimizing at the same time the reconstruction of the input variables of the Standard Model and classification of new physics processes. This procedure ensures that the model is optimized for discrimination, with a small price in terms of model dependency to physics process. In this work I will discuss in detail the above-mentioned method using generator level Vector Boson Scattering events produced at LHC assuming an integrated luminosity of 350/fb.