Analysis methods and code for very high-precision mass measurements of unstable isotopes

We present a robust analysis code developed in the Python language and incorporating libraries of the ROOT data analysis framework for the state-of-the-art mass spectrometry method called phase-imaging ion-cyclotron-resonance (PI-ICR). A step-by-step description of the dataset construction and analysis algorithm is given. The code features a new phase-determination approach that offers up to 10 times smaller statistical uncertainties. This improvement in statistical uncertainty is confirmed using extensive Monte-Carlo simulations and allows for very high-precision studies of exotic nuclear masses to test, among others, the standard model of particle physics.

本文针对当前顶尖的相成像离子回旋共振（phase-imaging ion-cyclotron-resonance, PI-ICR）质谱分析方法，开发了一款基于Python语言且集成ROOT数据分析框架库的稳健性分析代码。本文详细给出了数据集构建与分析算法的分步说明。该代码搭载了一种全新的相位确定方案，可将统计不确定度最高降低至原有水平的1/10。此项统计不确定度的优化已通过大规模蒙特卡洛模拟得到验证，该方法可支持对奇异核质量开展超高精度研究，进而用于检验包括粒子物理标准模型在内的诸多物理理论。