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Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
Steven Tsan · Sukanya Krishna · Raghav Kansal · Anthony Aportela · Farouk Mokhtar · Daniel Diaz · Javier Duarte · Maurizio Pierini · jean-roch vlimant

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in Machine Learning and the Physical Sciences »

Autoencoders have useful applications in high energy physics in both compression and 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 jets, 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.

Author Information

Steven Tsan (University of California, San Diego)
Sukanya Krishna (UCSD)
Raghav Kansal (UC San Diego)
Anthony Aportela (UCSD Physics)
Farouk Mokhtar (UC San Diego)
Daniel Diaz (University of California San Diego)
Javier Duarte (UC San Diego)

I am an Assistant Professor in experimental high energy physics at UC San Diego and a member of the CMS collaboration at CERN. My research interests include measuring the properties and couplings of the Higgs boson and searching for beyond-the-standard-model particles in LHC data. I am interested in developing machine learning algorithms, real-time trigger systems (with applications to embedded devices), and heterogenous computing architectures for the next generation of high energy physics experiments.

Maurizio Pierini (CERN)
jean-roch vlimant (California Institute of Technology)

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