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Title: The HEP.TrkX Project: Deep Learning for Particle Tracking

Abstract

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.

Authors:
 [1];  [2];  [2];  [3];  [1];  [4];  [3];  [1];  [1];  [1];  [3];  [3];  [1];  [2];  [2];  [2];  [1]
  1. Fermilab
  2. Caltech
  3. LBL, Berkeley
  4. Fribourg U.
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
OSTI Identifier:
1490852
Report Number(s):
FERMILAB-CONF-18-596-CD
1699885
DOE Contract Number:  
AC02-07CH11359
Resource Type:
Conference
Journal Name:
J.Phys.Conf.Ser.
Additional Journal Information:
Journal Volume: 1085; Journal Issue: 4; Conference: 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Seattle, WA, USA, 08/21-08/25/2017
Country of Publication:
United States
Language:
English

Citation Formats

Tsaris, Aristeidis, Anderson, Dustin, Bendavid, Josh, Calafiura, Paolo, Cerati, Giuseppe, Esseiva, Julien, Farrell, Steven, Gray, Lindsey, Kapoor, Keshav, Kowalkowski, Jim, Mudigonda, Mayur, Prabhat, Prabhat,, Spentzouris, Panagiotis, Spiropoulou, Maria, Vlimant, Jean-Roch, Zheng, Stephan, and Zurawski, Daniel. The HEP.TrkX Project: Deep Learning for Particle Tracking. United States: N. p., 2018. Web. doi:10.1088/1742-6596/1085/4/042023.
Tsaris, Aristeidis, Anderson, Dustin, Bendavid, Josh, Calafiura, Paolo, Cerati, Giuseppe, Esseiva, Julien, Farrell, Steven, Gray, Lindsey, Kapoor, Keshav, Kowalkowski, Jim, Mudigonda, Mayur, Prabhat, Prabhat,, Spentzouris, Panagiotis, Spiropoulou, Maria, Vlimant, Jean-Roch, Zheng, Stephan, & Zurawski, Daniel. The HEP.TrkX Project: Deep Learning for Particle Tracking. United States. doi:10.1088/1742-6596/1085/4/042023.
Tsaris, Aristeidis, Anderson, Dustin, Bendavid, Josh, Calafiura, Paolo, Cerati, Giuseppe, Esseiva, Julien, Farrell, Steven, Gray, Lindsey, Kapoor, Keshav, Kowalkowski, Jim, Mudigonda, Mayur, Prabhat, Prabhat,, Spentzouris, Panagiotis, Spiropoulou, Maria, Vlimant, Jean-Roch, Zheng, Stephan, and Zurawski, Daniel. Thu . "The HEP.TrkX Project: Deep Learning for Particle Tracking". United States. doi:10.1088/1742-6596/1085/4/042023. https://www.osti.gov/servlets/purl/1490852.
@article{osti_1490852,
title = {The HEP.TrkX Project: Deep Learning for Particle Tracking},
author = {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, Prabhat, and Spentzouris, Panagiotis and Spiropoulou, Maria and Vlimant, Jean-Roch and Zheng, Stephan and Zurawski, Daniel},
abstractNote = {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.},
doi = {10.1088/1742-6596/1085/4/042023},
journal = {J.Phys.Conf.Ser.},
number = 4,
volume = 1085,
place = {United States},
year = {2018},
month = {10}
}

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