The HEP.TrkX Project: Deep Learning for Particle Tracking

Charged particle reconstruction in dense environments, such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms, such as the combinatorial Kalman Filter, have been used with great success in HEP experime...

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Published inJournal of physics. Conference series Vol. 1085; no. 4; pp. 42023 - 42029
Main Authors Tsaris, Aristeidis, Anderson, Dustin, Bendavid, Josh, Calafiura, Paolo, Cerati, Giuseppe, Esseiva, Julien, Farrell, Steven, Gray, Lindsey, Kapoor, Keshav, Kowalkowski, Jim, Mudigonda, Mayur, Prabhat, Spentzouris, Panagiotis, Spiropoulou, Maria, Vlimant, Jean-Roch, Zheng, Stephan, Zurawski, Daniel
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.09.2018
Subjects
Algorithms
Artificial neural networks
Charged particles
Combinatorial analysis
Deep learning
Kalman filters
Large Hadron Collider
Luminosity
Machine learning
Occupancy
Parallel processing
Particle tracking
Pattern recognition
Physics
Reconstruction
Two dimensional models
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Summary:Charged particle reconstruction in dense environments, such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms, such as the combinatorial Kalman Filter, have been used with great success in HEP experiments for years. However, these state-of-the-art techniques are inherently sequential and scale quadratically or worse with increased detector occupancy. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as FPGAs or GPUs. In this paper we present the evolution and performance of our recurrent (LSTM) and convolutional neural networks moving from basic 2D models to more complex models and the challenges of scaling up to realistic dimensionality/sparsity.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1085/4/042023