Physics inspired feature engineering with Lorentz Boost Networks

Abstract

We present a neural network architecture designed to autonomously create characteristic features of high energy physics collision events from basic four-vector information. It consists of two stages, the first of which we call the Lorentz Boost Network (LBN). The LBN creates composite particles and rest frames from the combination of final state particles, and then boosts said particles into their corresponding rest frames. From these boosted particles, characteristic features are created and used by the second network stage to solve a given physics problem. We apply our model to the task of separating top-quark pair associated Higgs boson events from a background, and observe improved performance compared to using domain unspecific deep neural networks. We also investigate the learned combinations and boosts to gain insights into what the network is learning.