Combining Deep Sets and Dynamic Graph Convolutional Neural Networks for Collider Event Classification

At experiments such as those occurring at the Large Hadron Collider, classifying between signal and background events is vital for the success of experimental analyses. Methods traditionally employed to classify events suffer from several shortcomings stemming from the fact that traditional machine learning methods restrict us from representing events in a way that both respects the permutation symmetry of the objects like jets or leptons and maximizes the kinematic information the architecture can take as input. In this talk we present novel methods incorporating modern machine learning techniques which allow us to represent events more naturally as variable sized lists of objects like jets or leptons and inherently utilize the permutation symmetry and relational information between objects within an event. We then recreate event classification problems faced by recent experimental analyses of the H→ττ channel and compare the performance of our architectures against more traditional methods to find that these novel architectures lead to a threefold increase in event classification performance over traditional methods employed for experimental analyses.