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

Steven Tsan; Raghav Kansal; Anthony Aportela; Daniel Diaz; Javier Duarte; Sukanya Krishna; Farouk Mokhtar; Jean-Roch Vlimant; Maurizio Pierini

arXiv · 2111.12849

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

Steven Tsan·Raghav Kansal·Anthony Aportela·Daniel Diaz·Javier Duarte·Sukanya Krishna·Farouk Mokhtar·Jean-Roch Vlimant·Maurizio Pierini

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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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physics.data-an cs.LG hep-ex

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Steven Tsan, Raghav Kansal, Anthony Aportela, Daniel Diaz, Javier Duarte, Sukanya Krishna, Farouk Mokhtar, Jean-Roch Vlimant, Maurizio Pierini. 2021-11-24. Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance. https://arxiv.org/abs/2111.12849

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