Fast Muon Tracking with Machine Learning Implemented in FPGA

Abstract

In this work, we present a new approach for fast tracking on multiwire proportional chambers with neural networks. The tracking networks are developed and adapted for the first-level trigger at hadron collider experiments. We use Monte Carlo samples generated by Geant4 with a custom muon chamber, which resembles part of the thin gap chambers from the ATLAS experiment, for training and performance evaluations. The chamber has a total of seven gas gaps, where the first and last gas gaps are displaced by ~1.5 m. Each gas gap has 50 channels with a size of 18-20 mm. Two neural network models are developed and presented: a convolutional neural network and a neural network optimized for the detector configuration of this study. In the latter network, a convolution layer is provided for each of three groups formed from 2-3 gas gaps of the chamber, and the outputs are fed into multilayer perceptrons in sequence. Both networks are transformed into hardware description language and implemented in Virtex UltraScale+ FPGA. The angular resolution is 2 mrad, which is comparable to the maximum resolution of the detector estimated by the minimum chi2 method. The latency achieved by the implemented firmware is less than 100 ns, and the throughput rate is 160 MHz.

9 pages, 8 figures

References in corpus (12)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
• Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
• The CMS trigger system
• A portrait of the Higgs boson by the CMS experiment ten years after the discovery
• A detailed map of Higgs boson interactions by the ATLAS experiment ten years after the discovery
• Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors
• Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
• Performance of the ATLAS muon trigger in pp collisions at $\sqrt{s}=8$ TeV
• Fast inference of Boosted Decision Trees in FPGAs for particle physics
• Muon trigger with fast Neural Networks on FPGA, a demonstrator
• The ATLAS Fast TracKer system

Cited by in corpus (6)

• Firmware implementation of a recurrent neural network for the computation of the energy deposited in the liquid argon calorimeter of the ATLAS experiment
• Development of a resource-efficient FPGA-based neural network regression model for the ATLAS muon trigger upgrades
• da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAs
• Fast Jet Tagging with MLP-Mixers on FPGAs
• BitHEP -- The Limits of Low-Precision ML in HEP
• Nanosecond hardware regression trees in FPGA at the LHC