27 citations · 103 across the 19 of their papers we have counts for
4 papers · 2 filters
Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
Steven Tsan, Raghav Kansal, Anthony Aportela +6
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 t…
Explaining machine-learned particle-flow reconstruction
Farouk Mokhtar, Raghav Kansal, Daniel Diaz +4
The particle-flow (PF) algorithm is used in general-purpose particle detectors to reconstruct a comprehensive particle-level view of the collision by combining information from dif…
Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC
Breno Orzari, Thiago Tomei, Maurizio Pierini +5
We develop a generative neural network for the generation of sparse data in particle physics using a permutation-invariant and physics-informed loss function. The input dataset use…
Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
Raghav Kansal, Javier Duarte, Breno Orzari +5
We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC). We demonstrate this approach by training…