29 citations · 39 across the 3 of their papers we have counts for
5 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…
Secondary Vertex Finding in Jets with Neural Networks
Jonathan Shlomi, Sanmay Ganguly, Eilam Gross +5
Jet classification is an important ingredient in measurements and searches for new physics at particle coliders, and secondary vertex reconstruction is a key intermediate step in b…
Graph Neural Networks in Particle Physics
Jonathan Shlomi, Peter Battaglia, Jean-Roch Vlimant
Particle physics is a branch of science aiming at discovering the fundamental laws of matter and forces. Graph neural networks are trainable functions which operate on graphs---set…
Efficiency Parameterization with Neural Networks
C. Badiali, F. A. Di Bello, G. Frattari +4
Multidimensional efficiency maps are commonly used in high energy physics experiments to mitigate the limitations in the generation of large samples of simulated events. Binned mul…