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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.
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Xiangyang Ju · 2003
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“The ATLAS Experiment at the CERN Large Hadron Collider”
G. Aad · 2008
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“The CMS experiment at the CERN LHC”
S. Chatrchyan · 2008
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“The new ATLAS track reconstruction (NEWT)”
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“Scikit-learn: Machine Learning in Python”
F. Pedregosa et al · 2011
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“High Luminosity Large Hadron Collider HL-LHC”
G. Apollinari, O. Brüning, T. Nakamoto and Lucio Rossi · 2015
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“HEP advanced tracking algorithms with cross-cutting applications”, 2016
The.TrkX Collaboration · 2016
Cited alongside, same era.
“Geometric deep learning: going beyond Euclidean data”
Michael. Bronstein et al · 2016
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“Semi-Supervised Classification with Graph Convolutional Networks”
Thomas. Kipf and Max Welling · 2016
Cited alongside, same era.
“Deep residual learning for image recognition”
“The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking”
Steven Farrell · 2017
Later among the works it cites.
Petar Veličković et al · 2017
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“CuPy : A NumPy-Compatible Library for NVIDIA GPU Calculations”, 2017
Ryosuke Okuta et al · 2017
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“Novel deep learning methods for track reconstruction”
Steven Farrell · 2018
Later among the works it cites.
“HEP advanced tracking algorithms at the exascale”, 2019
The.TrkX Collaboration · 2019
Later among the works it cites.
“Graph Neural Networks: A Review of Methods and Applications”, 2019
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Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Cited alongside, same era.
URL: https://www.kaggle.com/c/trackml-particle-identification
“TrackML Particle Tracking Challenge”
Cited in the paper.
Jie Zhou et al · 2019
Later among the works it cites.