The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- California Inst. of Technology (CalTech), Pasadena, CA (United States)
- 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
The HEP.TrkX Project: Deep Learning for Particle Tracking
|
journal | September 2018 |
FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing
|
journal | October 2019 |
Proton Tracking Algorithm in a Pixel-Based Range Telescope for Proton Computed Tomography | preprint | January 2020 |
Similar Records
Novel deep learning methods for track reconstruction
Generalizing mkFit and its Application to HL-LHC