262 citations · 535 across the 15 of their papers we have counts for
6 papers · 1 filter
Reconstructing particles in jets using set transformer and hypergraph prediction networks
Francesco Armando Di Bello, Etienne Dreyer, Sanmay Ganguly +8
The task of reconstructing particles from low-level detector response data to predict the set of final state particles in collision events represents a set-to-set prediction task r…
Set-Conditional Set Generation for Particle Physics
Francesco Armando Di Bello, Etienne Dreyer, Sanmay Ganguly +6
The simulation of particle physics data is a fundamental but computationally intensive ingredient for physics analysis at the Large Hadron Collider, where observational set-valued…
Improving Di-Higgs Sensitivity at Future Colliders in Hadronic Final States with Machine Learning
Artur Apresyan, Daniel Diaz, Javier Duarte +8
One of the central goals of the physics program at the future colliders is to elucidate the origin of electroweak symmetry breaking, including precision measurements of the Higgs s…
Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges
Savannah Thais, Paolo Calafiura, Grigorios Chachamis +7
Many physical systems can be best understood as sets of discrete data with associated relationships. Where previously these sets of data have been formulated as series or image dat…
Symmetry Group Equivariant Architectures for Physics
Alexander Bogatskiy, Sanmay Ganguly, Thomas Kipf +8
Physical theories grounded in mathematical symmetries are an essential component of our understanding of a wide range of properties of the universe. Similarly, in the domain of mac…
Machine Learning and LHC Event Generation
Anja Butter, Tilman Plehn, Steffen Schumann +48
First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predicti…