most citedGraph Neural Networks in Particle Physics

175 citations · 286 across the 7 of their papers we have counts for

collaborators

7 papers

hep-ex2022★ 29 cited

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…

hep-ex2022★ 5 cited

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…

hep-ex2020★ 20 cited

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…

hep-ex2020★ 175 cited

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…

hep-ex2020★ 5 cited

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…

physics.data-an2020★ 36 cited

Towards a Computer Vision Particle Flow

Francesco Armando Di Bello, Sanmay Ganguly, Eilam Gross +4

In High Energy Physics experiments Particle Flow (PFlow) algorithms are designed to provide an optimal reconstruction of the nature and kinematic properties of the particles produc…

cs.LG2020★ 16 cited

Set2Graph: Learning Graphs From Sets

Hadar Serviansky, Nimrod Segol, Jonathan Shlomi +4

Many problems in machine learning can be cast as learning functions from sets to graphs, or more generally to hypergraphs; in short, Set2Graph functions. Examples include clusterin…