Deep learning for track recognition in pixel and strip-based particle detectors

TL;DR

This paper introduces two deep learning neural network algorithms, TrackNETv3 and RDGraphNet, for efficient and accurate track recognition in pixel and strip-based particle detectors, addressing the limitations of classical methods in handling large data volumes.

Contribution

The paper presents novel deep learning architectures for local and global track recognition, demonstrating high accuracy on real detector data, which improves upon traditional tracking methods.

Findings

RDGraphNet achieved 95% recall and 74% precision.

TrackNETv3 achieved 95% recall and 76% precision.

Results indicate promising potential for deep learning in particle tracking.

Abstract

The reconstruction of charged particle trajectories in tracking detectors is a key problem in the analysis of experimental data for high-energy and nuclear physics. The amount of data in modern experiments is so large that classical tracking methods, such as the Kalman filter, cannot process them fast enough. To solve this problem, we developed two neural network algorithms based on deep learning architectures for track recognition in pixel and strip-based particle detectors. These are TrackNETv3 for local (track by track) and RDGraphNet for global (all tracks in an event) tracking. These algorithms were tested using the GEM tracker of the BM@N experiment at JINR (Dubna) and the cylindrical GEM inner tracker of the BESIII experiment at IHEP CAS (Beijing). The RDGraphNet model, based on a reverse directed graph, showed encouraging results: 95% recall and 74% precision for track finding. The TrackNETv3 model demonstrated a recall value of 95% and 76% precision. This result can be improved after further model optimization.