Add to ORKGThe 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 detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to produ...
The document describes the challenge data, task and organizationCan Machine Learning assist High Ene...
The document describes the challenge data, task and organizationCan Machine Learning assist High Ene...
To address the unprecedented scale of HL-LHC data, the Exa.TrkX (previously HEP.TrkX) project has be...
The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neura...
Abstract The Exa.TrkX project has applied geometric learning concepts such as metric learning and gr...
The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neura...
Particle track reconstruction in dense environments such as the detectors of the High Luminosity Lar...
Particle track reconstruction in dense environments such as the detectors of the High Luminosity Lar...
Particle track reconstruction in dense environments such as the detectors of the High Luminosity Lar...
Charged particle reconstruction in dense environments, such as the detectors of the High Luminosity ...
Reconstruction of charged particle tracks is a central task in the processing of physics data at the...
Recently, graph neural networks (GNNs) have been successfully used for a variety of particle reconst...
The document describes the challenge data, task and organizationCan Machine Learning assist High Ene...
The document describes the challenge data, task and organizationCan Machine Learning assist High Ene...
In this work, we present a study on ways that tracking algorithms can be improved with machine learn...
The document describes the challenge data, task and organizationCan Machine Learning assist High Ene...
The document describes the challenge data, task and organizationCan Machine Learning assist High Ene...
To address the unprecedented scale of HL-LHC data, the Exa.TrkX (previously HEP.TrkX) project has be...
The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neura...
Abstract The Exa.TrkX project has applied geometric learning concepts such as metric learning and gr...
The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neura...
Particle track reconstruction in dense environments such as the detectors of the High Luminosity Lar...
Particle track reconstruction in dense environments such as the detectors of the High Luminosity Lar...
Particle track reconstruction in dense environments such as the detectors of the High Luminosity Lar...
Charged particle reconstruction in dense environments, such as the detectors of the High Luminosity ...
Reconstruction of charged particle tracks is a central task in the processing of physics data at the...
Recently, graph neural networks (GNNs) have been successfully used for a variety of particle reconst...
The document describes the challenge data, task and organizationCan Machine Learning assist High Ene...
The document describes the challenge data, task and organizationCan Machine Learning assist High Ene...
In this work, we present a study on ways that tracking algorithms can be improved with machine learn...
The document describes the challenge data, task and organizationCan Machine Learning assist High Ene...
The document describes the challenge data, task and organizationCan Machine Learning assist High Ene...
To address the unprecedented scale of HL-LHC data, the Exa.TrkX (previously HEP.TrkX) project has be...