Fast Muon Tracking with Machine Learning Implemented in FPGA

TL;DR

This paper introduces neural network-based algorithms for rapid muon tracking in collider experiments, implemented efficiently on FPGA hardware, achieving high angular resolution and extremely low latency.

Contribution

It presents novel neural network models tailored for muon tracking, optimized for FPGA implementation, enabling fast, accurate first-level trigger processing in collider experiments.

Findings

Angular resolution of 2 mrad achieved

Latency less than 100 ns

Throughput rate of 160 MHz

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.