The HEP.TrkX Project: Deep Learning for Particle Tracking

Conference · Thu Oct 18 00:00:00 EDT 2018 · J.Phys.Conf.Ser.

DOI:https://doi.org/10.1088/1742-6596/1085/4/042023· OSTI ID:1490852

Tsaris, Aristeidis ^[1]; Anderson, Dustin ^[2]; Bendavid, Josh ^[2]; Calafiura, Paolo ^[3]; Cerati, Giuseppe ^[1]; Esseiva, Julien ^[4]; Farrell, Steven ^[3]; Gray, Lindsey ^[1]; Kapoor, Keshav ^[1]; Kowalkowski, Jim ^[1]; Mudigonda, Mayur ^[3]; Prabhat, Prabhat, ^[3]; Spentzouris, Panagiotis ^[1]; Spiropoulou, Maria ^[2]; Vlimant, Jean-Roch ^[2]; Zheng, Stephan ^[2]; Zurawski, Daniel ^[1]

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1. Fermilab
2. Caltech
3. LBL, Berkeley
4. Fribourg U.

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.

• Research Organization: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP)
• DOE Contract Number: AC02-07CH11359
• OSTI ID: 1490852
• Report Number(s): FERMILAB-CONF-18-596-CD; 1699885
• Journal Information: J.Phys.Conf.Ser., Vol. 1085, Issue 4; Conference: 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Seattle, WA, USA, 08/21-08/25/2017
• Country of Publication: United States
• Language: English