Title: Analysis of Atomic Charge State and Atomic Number for VAMOS++ Magnetic Spectrometer using Deep Neural Networks and Fractionally Labelled Events Show Chinese title

Title: VAMOS++磁谱仪原子电荷态和原子序数的分析，使用深度神经网络和部分标记事件

Abstract: The VAMOS++ magnetic spectrometer is a multi-parametric system that integrates ion optical magnetic elements with a multi-detector stack. The magnetic elements, along with the tracking and timing detectors and the trajectory reconstruction method, provide the analysis of the magnetic rigidity, the trajectory length between the beam interaction point and the focal plane of the spectrometer, and the related velocity and mass-over-charge ratio. The segmented ionization chamber provides the energy measurements necessary to analyze the atomic charge state and atomic number. However, this analysis critically suffers from inherent limitations due to the variable thickness and non-uniformity of the entrance window of the ionization chamber and other detector imperfections. Conventionally, this meticulous, detailed analysis is exceptionally tedious, often requiring several months to complete. We present a novel method utilizing deep neural networks, trained on an experimental dataset with only a small fraction of precisely labeled events for the lowest and best-resolved atomic charge states or numbers. This innovative approach enables the networks to autonomously and accurately classify the remaining events. This method drastically accelerates the acquisition of high-resolution atomic charge state and atomic number spectra, reducing analysis time from months to mere hours. Crucially, by discarding human bias, this approach ensures standardized, optimal, and reproducible results with unprecedented efficiency. Show Chinese abstract

Abstract: VAMOS++磁谱仪是一个多参数系统，将离子光学磁元件与多探测器堆栈集成在一起。 磁元件，以及跟踪和定时探测器和轨迹重建方法，提供了对磁刚度、束流相互作用点与谱仪焦平面之间的轨迹长度，以及相关的速度和质荷比的分析。 分段电离室提供了分析原子电荷态和原子序数所需的能量测量。 然而，这种分析由于电离室入口窗的厚度变化和不均匀性以及其他探测器缺陷而受到固有局限性的严重影响。 传统上，这种细致的分析非常繁琐，通常需要几个月的时间才能完成。 我们提出了一种新方法，利用深度神经网络，该网络仅使用实验数据集中的少量精确标记事件进行训练，这些事件对应于最低且分辨最好的原子电荷态或原子序数。 这种创新方法使网络能够自主且准确地分类剩余事件。 这种方法大大加快了高分辨率原子电荷态和原子序数谱的获取，将分析时间从几个月缩短到几小时。 至关重要的是，通过消除人为偏见，这种方法以前所未有的效率确保了标准化、最优和可重复的结果。