G. Dezoort
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View article: An Object Condensation Pipeline for Charged Particle Tracking at the High Luminosity LHC
An Object Condensation Pipeline for Charged Particle Tracking at the High Luminosity LHC Open
Recent work has demonstrated that graph neural networks (GNNs) trained for charged particle tracking can match the performance of traditional algorithms while improving scalability to prepare for the High Luminosity LHC experiment. Most ap…
View article: High Pileup Particle Tracking with Object Condensation
High Pileup Particle Tracking with Object Condensation Open
Recent work has demonstrated that graph neural networks (GNNs) can match the performance of traditional algorithms for charged particle tracking while improving scalability to meet the computing challenges posed by the HL-LHC. Most GNN tra…
View article: An Object Condensation Pipeline for Charged Particle Tracking at the High Luminosity LHC
An Object Condensation Pipeline for Charged Particle Tracking at the High Luminosity LHC Open
Recent work has demonstrated that graph neural networks (GNNs) trained for charged particle tracking can match the performance of traditional algorithms while improving scalability to prepare for the High Luminosity LHC experiment. Most ap…
View article: Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations
Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations Open
This article derives and validates three principles for initialization and architecture selection in finite width graph neural networks (GNNs) with ReLU activations. First, we theoretically derive what is essentially the unique generalizat…
View article: Graph Neural Networks for Charged Particle Tracking on FPGAs
Graph Neural Networks for Charged Particle Tracking on FPGAs Open
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-luminos…
View article: Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges
Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges Open
Many physical systems can be best understood as sets of discrete data with associated relationships. Where previously these sets of data have been formulated as series or image data to match the available machine learning architectures, wi…
View article: Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC
Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC Open
3D instance segmentation remains a challenging problem in computer vision. Particle tracking at colliders like the LHC can be conceptualized as an instance segmentation task: beginning from a point cloud of hits in a particle detector, an …
View article: Physics and Computing Performance of the Exa.TrkX TrackML Pipeline.
Physics and Computing Performance of the Exa.TrkX TrackML Pipeline. Open
The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. The Exa.TrkX tracking pipeline clusters detector measurements to form track candidates and filters the…
View article: Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle\n Tracking
Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle\n Tracking Open
The Exa.TrkX project has applied geometric learning concepts such as metric\nlearning and graph neural networks to HEP particle tracking. Exa.TrkX's\ntracking pipeline groups detector measurements to form track candidates and\nfilters them…
View article: Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs Open
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 framework for writing programs that execute across heterogeneou…