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

Journal Article · 07 August 2017 · EPJ Web of Conferences (Online)

DOI: https://doi.org/10.1051/epjconf/201715000003 · OSTI ID:1375725

Farrell, Steven ^[1]; Anderson, Dustin ^[2]; Calafiura, Paolo ^[1]; Cerati, Giuseppe ^[3]; Gray, Lindsey ^[3]; Kowalkowski, Jim ^[3]; Mudigonda, Mayur ^[1]; Prabhat, . ^[1]; Spentzouris, Panagiotis ^[3]; Spiropoulou, Maria ^[2]; Tsaris, Aristeidis ^[3]; Vlimant, Jean-Roch ^[2]; Zheng, Stephan ^[2]

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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)

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.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
• Grant/Contract Number: AC02-07CH11359
• OSTI ID: 1375725
• Report Number(s): FERMILAB-CONF--17-326-CD; 1616026
• Journal Information: EPJ Web of Conferences (Online), Journal Name: EPJ Web of Conferences (Online) Vol. 150; ISSN 2100-014X
• Publisher: EDP SciencesCopyright Statement
• Country of Publication: United States
• Language: English