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Found 6 papers, 2 papers with code
Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges
Where previously these sets of data have been formulated as series or image data to match the available machine learning architectures, with the advent of graph neural networks (GNNs), these systems can be learned natively as graphs.
Graph Neural Networks for Charged Particle Tracking on FPGAs
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 density conditions expected during the future high-luminosity phase of the LHC (HL-LHC).
Charged particle tracking via edge-classifying interaction networks
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 energy particle physics.
Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking
The Exa. TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking.
Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC
3D instance segmentation remains a challenging problem in computer vision.
Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks.
