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HEP.TrkX Project: Deep Learning for Particle Tracking

Tsaris, Aristeidis and Anderson, Dustin and Bendavid, Josh and Calafiura, Paolo and Cerati, Giuseppe and Esseiva, Julien and Farrell, Steven and Gray, Lindsey and Kapoor, Keshav and Kowalkowski, Jim and Mudigonda, Mayur and Prabhat, Mr. and Spentzouris, Panagiotis and Spiropoulou, Maria and Vlimant, Jean-Roch and Zheng, Stephan and Zurawski, Daniel (2018) HEP.TrkX Project: Deep Learning for Particle Tracking. Journal of Physics Conference Series, 1085 . Art. No. 042023. ISSN 1742-6588. doi:10.1088/1742-6596/1085/4/042023.

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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.

Item Type:Article
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URLURL TypeDescription
Spiropoulou, Maria0000-0001-8172-7081
Vlimant, Jean-Roch0000-0002-9705-101X
Additional Information:© 2018 Published under licence by IOP Publishing Ltd. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. The authors would like to thank the funding agencies DOE ASCR and COMP HEP for supporting this work, as well as the numerous tracking experts from ATLAS and CMS who have shared insights and experience.
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Department of Energy (DOE)UNSPECIFIED
Record Number:CaltechAUTHORS:20190606-105037785
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Official Citation:Aristeidis Tsaris et al 2018 J. Phys.: Conf. Ser. 1085 042023
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:96191
Deposited By: Tony Diaz
Deposited On:06 Jun 2019 22:02
Last Modified:12 Jul 2022 17:06

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