Novelty Detection Neural Networks for Model-Independent New Physics Search

Recent advancements in model-independent approaches in high energy physics have encountered challenges due to the limited effectiveness of unsupervised algorithms when compared to their supervised counterparts. In this paper, we present a novel approach utilizing a one-class deep neural network (DNN) to achieve accuracy levels comparable to supervised learning methods. Our proposed novelty detection algorithm uses a multilayer perceptron to learn and distinguish a specific class from simulated noise signals. By training on a single class, our algorithm constructs a hyperplane similar to one-class support vector machines (SVMs) but with enhanced accuracy and significantly reduced training and inference times. This research contributes to the advancement of model-independent techniques for uncovering New Physics phenomena, showcasing the potential of one-class DNNs as a viable alternative to traditional supervised learning approaches. For the demonstration of the method, the distinguishing of flavour changing neutral currents in top quark interactions from the Standard Model processes has been considered. The obtained results demonstrate the effectiveness of our proposed algorithm, paving the way for improved anomaly detection and exploration of uncharted territories in high energy physics.