GPU-based track-finding for the J-PARC muon g-2/EDM experiment

TL;DR

This paper presents a GPU-accelerated track-finding method for the J-PARC muon g-2/EDM experiment, significantly speeding up analysis while maintaining accuracy, addressing the challenge of rapid positron track reconstruction amidst event pileup.

Contribution

The study introduces a GPU-based implementation of the track-finding algorithm, achieving over 40x speedup and enabling real-time processing in the experiment.

Findings

GPU implementation greatly reduces execution time

Speedup exceeds 40x without losing accuracy

Effective under various pileup conditions

Abstract

The muon \textit{g-2}/EDM experiment at J-PARC is designed to precisely measure the muon's magnetic moment and electric dipole moment, driven by discrepancies between theory and previous experiments. A key challenge is the fast reconstruction of positron tracks from multiple muon decays within a short time span causing an event pileup. One of the aspects is the identification of individual positron tracks from the reconstructed hits, which is currently done using a hough-transform based approach. Results from simulation studies have shown expected results in terms of efficiency and accuracy of track reconstruction. However, the execution time for the entire analysis chain is prohibitively long to be deployed in the experiment. Specifically, preliminary estimations suggest a requirement of 40 $\times$ speedup of the track-finding routine. In this context, we explore a GPU-based solution to accelerate track-finding through parallel processing and present the implementation details and the results of our study for different pileup conditions. The results indicate that the GPU solution far exceeds our expectation in terms of execution speed without compromising on the reconstruction efficiency.