Reinforced sorting networks for particle physics analyses

Abstract

Deep learning architectures in particle physics are often strongly dependent on the order of their input variables. We present a two-stage deep learning architecture consisting of a network for sorting input objects and a subsequent network for data analysis. The sorting network (agent) is trained through reinforcement learning using feedback from the analysis network (environment). The optimal order depends on the environment and is learned by the agent in an unsupervised approach. Thus, the two-stage system can choose an optimal solution which is not known to the physicist in advance. We present the new approach and its application to the signal and background separation in top-quark pair associated Higgs boson production.