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

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]
  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:
Report Number(s):
FERMILAB-CONF-17-326-CD
Journal ID: ISSN 2100-014X; 1616026
Grant/Contract Number:
AC02-07CH11359
Type:
Accepted Manuscript
Journal Name:
EPJ Web of Conferences
Additional Journal Information:
Journal Volume: 150; Journal ID: ISSN 2100-014X
Publisher:
EDP Sciences
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)
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
OSTI Identifier:
1375725

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., Web. doi:10.1051/epjconf/201715000003.
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. doi:10.1051/epjconf/201715000003.
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. 2017. "The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking". United States. doi:10.1051/epjconf/201715000003. https://www.osti.gov/servlets/purl/1375725.
@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},
number = ,
volume = 150,
place = {United States},
year = {2017},
month = {8}
}