Distinguishing signals at hadron colliders using neural networks
Spencer Chang (Department of Physics and Institute for Fundamental Science University of Oregon, Eugene, Oregon 97403, USA); Ting-Kuo Chen (Department of Physics, National Taiwan University, Taipei 10617, Taiwan); Cheng-Wei Chiang (Department of Physics, National Taiwan University, Taipei 10617, Taiwan, Physics Division, National Center for Theoretical Sciences, Taipei, Taiwan 10617, Republic of China)
We investigate a neural-network-based hypothesis test to distinguish different and charged scalar resonances through the channel at hadron colliders. This is traditionally challenging due to a fourfold ambiguity at proton-proton colliders, such as the Large Hadron Collider. Of the neural network approaches we study, we find a multiclass classifier based on a fully connected neural network trained upon two-dimensional histograms made from kinematic variables of the final state to be the most powerful. Furthermore, by considering the one-jet processes, we demonstrate that one can generalize to multiple two-dimensional histograms to represent different variable pairs. Finally, as a comparison to traditional approaches, we compare our method with Bayesian hypothesis testing and discuss the pros and cons of each approach. The neural network scheme presented in this paper is a powerful tool that can help probe the properties of charged resonances.
