The use of Wasserstein Generative Adversarial Networks in searches for new resonances at the LHC.

Abstract

Abstract In the search for physics beyond the standard model, machine learning classifiers provide methods for extracting signals from background processes in data produced at the LHC. Semi-supervised machine learning models are trained on a labeled background and unlabelled signal. When using semi-supervised techniques in the training of machine learning models, over-training can lead to background events incorrectly being labeled as signal events. The extent of false signals generated must therefore be quantified before semi-supervised techniques can be used in resonance searches. In this study, a frequentest methodology is presented to quantify the extent of fake signals generated in the training of semi supervised DNN classifiers when confronting side-bands and the signal regions. The use of a WGAN is explored as a machine learning based data generator.