Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance

Abstract

Autoencoders have useful applications in high energy physics in anomaly detection, particularly for jets - collimated showers of particles produced in collisions such as those at the CERN Large Hadron Collider. We explore the use of graph-based autoencoders, which operate on jets in their "particle cloud" representations and can leverage the interdependencies among the particles within a jet, for such tasks. Additionally, we develop a differentiable approximation to the energy mover's distance via a graph neural network, which may subsequently be used as a reconstruction loss function for autoencoders.

5 pages, 2 figures. Accepted to the Machine Learning for the Physical Sciences workshop at NeurIPS 2021. arXiv admin note: text overlap with arXiv:2101.08320