Publications (6)
Graph Neural Networks for Charged Particle Tracking on FPGAs
Abdelrahman Elabd, Vesal Razavimaleki, Shi-Yu Huang +12
The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction…
Accelerating the Inference of the Exa.TrkX Pipeline
Alina Lazar, Xiangyang Ju, Daniel Murnane +21
Recently, graph neural networks (GNNs) have been successfully used for a variety of particle reconstruction problems in high energy physics, including particle tracking. The Exa.Tr…
Reconstruction of Large Radius Tracks with the Exa.TrkX pipeline
Chun-Yi Wang, Xiangyang Ju, Shih-Chieh Hsu +22
Particle tracking is a challenging pattern recognition task at the Large Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms, such as those based on the Kalm…
Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
Aneesh Heintz, Vesal Razavimaleki, Javier Duarte +18
We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a fram…
Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking
Xiangyang Ju, Daniel Murnane, Paolo Calafiura +21
The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX's tracking pipeline groups detecto…
Charged particle tracking via edge-classifying interaction networks
Gage DeZoort, Savannah Thais, Javier Duarte +5
Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high energ…