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

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 heads, 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 could achieve 8.8 µm for the single track and 11.4 µm (15.2 µm) for the 1-3 tracks (1-5 tracks), and the angle resolution could achieve 0.15 • and 0.21 • (0.29 • ) respectively. These results show a significant improvement in accuracy and multi-track compatibility compared to traditional methods.