Aug 21 – 25, 2017
University of Washington, Seattle
US/Pacific timezone

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

Aug 22, 2017, 3:10 PM
107 (Alder Hall)


Alder Hall

Oral Track 2: Data Analysis - Algorithms and Tools Track 2: Data Analysis - Algorithms and Tools


Aristeidis Tsaris (Fermilab)


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 talk we will discuss 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.

Primary authors

Dustin James Anderson (California Institute of Technology (US)) Paolo Calafiura (Lawrence Berkeley National Lab. (US)) Giuseppe Cerati (Fermi National Accelerator Lab. (US)) Steven Andrew Farrell (Lawrence Berkeley National Lab. (US)) Lindsey Gray (Fermi National Accelerator Lab. (US)) Jim Kowalkowski (Fermilab) Mayur Mudigonda (Lawrence Berkeley National Laboratory) Mr Prabhat (Lawrence Berkeley National Laboratory) Panagiotis Spentzouris (Fermilab) Maria Spiropulu (California Institute of Technology (US)) Aristeidis Tsaris (Fermilab) Dr Jean-Roch Vlimant (California Institute of Technology (US)) Stephan Zheng (California Institute of Technology)

Presentation materials

Peer reviewing