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

• URL: http://arxiv.org/abs/2111.12849v1
• Date: Wed, 24 Nov 2021 23:50:15 GMT
• Title: Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
• Authors: Steven Tsan, Raghav Kansal, Anthony Aportela, Daniel Diaz, Javier Duarte, Sukanya Krishna, Farouk Mokhtar, Jean-Roch Vlimant, Maurizio Pierini
• Abstract summary: Autoencoders operate on jets in their "particle cloud" representations. We develop a differentiable approximation to the energy mover's distance via a graph neural network.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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