GPU-Accelerated Event Reconstruction for the COMET Phase-I Experiment

TL;DR

This paper presents a GPU-accelerated algorithm for efficient event reconstruction in the COMET Phase-I experiment, significantly speeding up processing while maintaining accuracy in identifying electron tracks.

Contribution

The paper introduces a parallelized GPU-based method for event reconstruction that improves speed without sacrificing accuracy in complex electron track analysis.

Findings

GPU implementation achieved an order of magnitude speedup over CPU

Reconstruction accuracy and momentum resolution were maintained

GPU and CPU results were identical

Abstract

This paper discusses a parallelized event reconstruction of the COMET Phase-I experiment. The experiment aims to discover charged lepton flavor violation by observing 104.97 MeV electrons from neutrinoless muon-to-electron conversion in muonic atoms. The event reconstruction of electrons with multiple helix turns is a challenging problem because hit-to-turn classification requires a high computation cost. The introduced algorithm finds an optimal seed of position and momentum for each turn partition by investigating the residual sum of squares based on distance-of-closest-approach (DCA) between hits and a track extrapolated from the seed. Hits with DCA less than a cutoff value are classified for the turn represented by the seed. The classification performance was optimized by tuning the cutoff value and refining the set of classified hits. The workload was parallelized over the seeds and the hits by defining two GPU kernels, which record track parameters extrapolated from the seeds and finds the DCAs of hits, respectively. A reasonable efficiency and momentum resolution was obtained for a wide momentum region which covers both signal and background electrons. The event reconstruction results from the CPU and GPU were identical to each other. The benchmarked GPUs had an order of magnitude of speedup over a CPU with 16 cores while the exact speed gains varied depending on their architectures.