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Published October 18, 2018 | Version Published
Journal Article Open

HEP.TrkX Project: Deep Learning for Particle Tracking

Creators

  • 1. ROR icon Fermilab
  • 2. ROR icon California Institute of Technology
  • 3. ROR icon Lawrence Berkeley National Laboratory
  • 4. ROR icon HES-SO Fribourg

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.

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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Published - Tsaris_2018_J._Phys.__Conf._Ser._1085_042023.pdf

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Additional details

Identifiers

Eprint ID
96191
Resolver ID
CaltechAUTHORS:20190606-105037785

Funding

Department of Energy (DOE)

Dates

Created
2019-06-06
Created from EPrint's datestamp field
Updated
2022-07-12
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Caltech groups
CMS@Caltech , Physics Department