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Title: The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

Abstract

Particle track 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 LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. 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 GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. Furthermore, we will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data.

Authors:
 [1];  [2];  [1];  [3];  [3];  [3];  [1];  [1];  [3];  [2];  [3];  [2];  [2]
+ Show Author Affiliations
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. California Inst. of Technology (CalTech), Pasadena, CA (United States)
  3. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP)
OSTI Identifier:
1375725
Report Number(s):
FERMILAB-CONF-17-326-CD
Journal ID: ISSN 2100-014X; 1616026
Grant/Contract Number:  
AC02-07CH11359
Resource Type:
Accepted Manuscript
Journal Name:
EPJ Web of Conferences (Online)
Additional Journal Information:
Journal Name: EPJ Web of Conferences (Online); Journal Volume: 150; Journal ID: ISSN 2100-014X
Publisher:
EDP Sciences
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats

Farrell, Steven, Anderson, Dustin, Calafiura, Paolo, Cerati, Giuseppe, Gray, Lindsey, Kowalkowski, Jim, Mudigonda, Mayur, Prabhat, ., Spentzouris, Panagiotis, Spiropoulou, Maria, Tsaris, Aristeidis, Vlimant, Jean-Roch, and Zheng, Stephan. The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking. United States: N. p., 2017. Web. doi:10.1051/epjconf/201715000003.
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Farrell, Steven, Anderson, Dustin, Calafiura, Paolo, Cerati, Giuseppe, Gray, Lindsey, Kowalkowski, Jim, Mudigonda, Mayur, Prabhat, ., Spentzouris, Panagiotis, Spiropoulou, Maria, Tsaris, Aristeidis, Vlimant, Jean-Roch, & Zheng, Stephan. The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking. United States. https://doi.org/10.1051/epjconf/201715000003
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Farrell, Steven, Anderson, Dustin, Calafiura, Paolo, Cerati, Giuseppe, Gray, Lindsey, Kowalkowski, Jim, Mudigonda, Mayur, Prabhat, ., Spentzouris, Panagiotis, Spiropoulou, Maria, Tsaris, Aristeidis, Vlimant, Jean-Roch, and Zheng, Stephan. Tue . "The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking". United States. https://doi.org/10.1051/epjconf/201715000003. https://www.osti.gov/servlets/purl/1375725.
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@article{osti_1375725,
title = {The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking},
author = {Farrell, Steven and Anderson, Dustin and Calafiura, Paolo and Cerati, Giuseppe and Gray, Lindsey and Kowalkowski, Jim and Mudigonda, Mayur and Prabhat, . and Spentzouris, Panagiotis and Spiropoulou, Maria and Tsaris, Aristeidis and Vlimant, Jean-Roch and Zheng, Stephan},
abstractNote = {Particle track 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 LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. 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 GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. Furthermore, we will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data.},
doi = {10.1051/epjconf/201715000003},
journal = {EPJ Web of Conferences (Online)},
number = ,
volume = 150,
place = {United States},
year = {Tue Aug 08 00:00:00 EDT 2017},
month = {Tue Aug 08 00:00:00 EDT 2017}
}
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Publisher's Version of Record
https://doi.org/10.1051/epjconf/201715000003
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Works referenced in this record:

LHC Machine
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ATLAS pixel detector electronics and sensors
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  • Mankel, Rainer
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Long Short-Term Memory
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    FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing
    journal, October 2019

    • Duarte, Javier; Harris, Philip; Hauck, Scott
    • Computing and Software for Big Science, Vol. 3, Issue 1
    • DOI: 10.1007/s41781-019-0027-2

    The HEP.TrkX Project: Deep Learning for Particle Tracking
    journal, September 2018

    Proton Tracking Algorithm in a Pixel-Based Range Telescope for Proton Computed Tomography
    preprint, January 2020

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