Graph Neural Networks for Particle Physics

by Mrs. Nathalie Soybelman

WIS

at Particles and Fields Seminar

Mon, 08 Jan 2024, 14:00
Sacta-Rashi Building for Physics (54), room 207

Abstract

In LHC experiments, particle collision data is recorded by complex detectors storing up to 6 GB of data per second.
Given this data’s complexity, cutting-edge machine learning is not just a tool but the essential pathway for its interpretation, simulation, and a comprehensive understanding of its implications regarding the fundamental laws of physics.
Graphs offer a natural representation of particle physics data. Consequently, we employ graph neural networks to address this multitude of challenges.
In this talk, I will give an overview of the current efforts in employing graph neural networks for event simulation, particle reconstruction, and physics analysis.

Created on 01-01-2024 by Lublinsky, Michael (lublinm)
Updaded on 01-01-2024 by Lublinsky, Michael (lublinm)