Speaker
Description
A significant number of tasks at ATLAS involve the generation or encoding of particle flow jets. These are jets which are represented by their constituent particles as a set or attributed point cloud. Such tasks include anomaly detection and simulation. Computation on these point clouds using standard dense networks is often difficult due to the requirement of some sort of ordering. We demonstrate that a graph network autoencoder provides two major benefits. Firstly, all operations within the network and it's training are invariant to any permutation on the set, including the calculation of the loss function. Secondly, the attention weighted message passing operations within the network allow us to focus on local information and relationships. We also present a novel batched autoregressive method for graph generation
