论文标题
气态漂移室中多轨位置和方向的深度学习方法
A deep learning approach to multi-track location and orientation in gaseous drift chambers
论文作者
论文摘要
精确测量光束监测系统中各个颗粒的位置和方向对多个学科的研究人员特别感兴趣。在可行的方法中，具有混合像素传感器的气态漂移室具有实现长期稳定测量的巨大潜力。在本文中，我们引入了深度学习，以分析光束投影图像中的模式，以促进粒子轨迹的三维重建。我们提出了一个基于分割和拟合特征提取和回归的端到端神经网络。两个分割分支，称为二进制分割和语义分割，执行初始轨道确定和像素轨道关联。然后将像素分配给多个轨道，并通过完整的后填充实现加权最小二乘拟合。此外，我们引入了一种中心角度措施，以结合两个单独的因素来判断位置和方向的精度。初始位置分辨率为单轨达到8.8 $μm$，1-3个轨道（1-5个轨道）的11.4 $ $ $（15.2 $μm$），而角度分辨率可实现0.15 $^{\ circ} $和0.21 $^{\ circ} $（0.29 $（0.29 $^$^\ circ）。与传统方法相比，这些结果表明，准确性和多轨兼容性有了显着提高。
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 $μm$ for the single track and 11.4 $μm$ (15.2 $μm$) 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.
