240 citations · 2.4k across the 55 of their papers we have counts for
11 papers · 1 filter
Graph Neural Networks for Particle Tracking and Reconstruction
Javier Duarte, Jean-Roch Vlimant
Machine learning methods have a long history of applications in high energy physics (HEP). Recently, there is a growing interest in exploiting these methods to reconstruct particle…
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning
Cheng Chen, Olmo Cerri, Thong Q. Nguyen +2
We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets. Taking as an example the generation of W+jet events pr…
Angular analysis of the decay B K(892) in proton-proton collisions at 8 TeV
CMS Collaboration
Angular distributions of the decay B K(892) are studied using events collected with the CMS detector in 8 TeV proton-proton collisions at the L…
A Quantum Graph Neural Network Approach to Particle Track Reconstruction
Cenk Tüysüz, Federico Carminati, Bilge Demirköz +6
Unprecedented increase of complexity and scale of data is expected in computation necessary for the tracking detectors of the High Luminosity Large Hadron Collider (HL-LHC) experim…
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…
Track Seeding and Labelling with Embedded-space Graph Neural Networks
Nicholas Choma, Daniel Murnane, Xiangyang Ju +16
To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promisi…