标题： 七维轨迹重构用于VAMOS++ 显示英文标题

标题： Seven-dimensional Trajectory Reconstruction for VAMOS++

摘要： VAMOS++磁谱仪的特点是具有大的角度和动量接受范围，以及高度非线性的离子光学特性，这需要使用软件离子轨迹重建方法来测量离子的磁刚度以及从束流相互作用点到谱仪焦平面的轨迹长度。 标准测量方法，涉及使用薄靶和窄束斑，允许假设离子轨迹重建的点状束流相互作用体积。 然而，这对于大束斑尺寸或扩展的气体靶体积的情况来说是一个限制。 为了克服这一限制，开发了一种七维重建方法，结合了反应位置坐标，利用了人工深度神经网络。 神经网络是在由标准磁光束追踪代码生成的理论数据集上进行训练的。 讨论了该方法未来应用于体积较大的气体靶的可能性，这需要在轨迹重建方法中显式包含束流相互作用点在靶中的三维位置。 新方法的性能以及与之前报告的模型在薄靶实验数据情况下的质量分辨率进行了比较。 显示英文摘要

摘要： The VAMOS++ magnetic spectrometer is characterized by a large angular and momentum acceptance and highly non-linear ion optics properties requiring the use of software ion trajectory reconstruction methods to measure the ion magnetic rigidity and the trajectory length between the beam interaction point and the focal plane of the spectrometer. Standard measurements, involving the use of a thin target and a narrow beam spot, allow the assumption of a point-like beam interaction volume for ion trajectory reconstruction. However, this represents a limitation for the case of large beam spot size or extended gaseous target volume. To overcome this restriction, a seven-dimensional reconstruction method incorporating the reaction position coordinates was developed, making use of artificial deep neural networks. The neural networks were trained on a theoretical dataset generated by standard magnetic ray-tracing code. Future application to a voluminous gas target, necessitating the explicit inclusion of the three-dimensional position of the beam interaction point within the target in the trajectory reconstruction method, is discussed. The performances of the new method are presented along with a comparison of mass resolution obtained with previously reported model for the case of thin-target experimental data.