A deep learning approach to multi-track location and orientation in gaseous drift chambers

TL;DR

This paper presents a deep learning method for precise 3D localization and orientation of particles in gaseous drift chambers, improving accuracy and multi-track handling over traditional techniques.

Contribution

It introduces an end-to-end neural network combining segmentation and fitting for multi-track particle localization in gaseous drift chambers.

Findings

Achieved 8.8 μm position resolution for single tracks

Attained 0.15° angle resolution for single tracks

Significantly outperforms traditional methods in accuracy and multi-track capability

Abstract

Accurate measuring the location and orientation of individual particles in a beam monitoring system is of particular interest to researchers in multiple disciplines. Among feasible methods, gaseous drift chambers with hybrid pixel sensors have the great potential to realize long-term stable measurement with considerable precision. In this paper, we introduce deep learning to analyze patterns in the beam projection image to facilitate three-dimensional reconstruction of particle tracks. We propose an end-to-end neural network based on segmentation and fitting for feature extraction and regression. Two segmentation branches, named binary segmentation and semantic segmentation, perform initial track determination and pixel-track association. Then pixels are assigned to multiple tracks, and a weighted least squares fitting is implemented with full back-propagation. Besides, we introduce a center-angle measure to judge the precision of location and orientation by combining two separate factors. The initial position resolution achieves 8.8 $\mu m$ for the single track and 11.4 $\mu m$ (15.2 $\mu m$) for the 1-3 tracks (1-5 tracks), and the angle resolution achieves 0.15$^{\circ}$ and 0.21$^{\circ}$ (0.29$^{\circ}$) respectively. These results show a significant improvement in accuracy and multi-track compatibility compared to traditional methods.