CaltechAUTHORS
Published August 8, 2017 | Version Published
Journal Article Open

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

Creators

  • 1. ROR icon Lawrence Berkeley National Laboratory
  • 2. ROR icon California Institute of Technology
  • 3. ROR icon Fermilab

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. We will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data.

Additional Information

© 2017 The Authors, published by EDP Sciences. This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Published online: 8 August 2017. 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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Published - epjconf_ctdw2017_00003.pdf

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Additional details

Identifiers

Eprint ID
85399
Resolver ID
CaltechAUTHORS:20180321-110952123

Funding

Department of Energy (DOE)
CompHEP

Dates

Created
2018-03-26
Created from EPrint's datestamp field
Updated
2021-11-15
Created from EPrint's last_modified field

Caltech Custom Metadata

Caltech groups
CMS@Caltech , Physics Department