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. Program Title: PI-ICR analysis software CPC Library link to program files:https://doi.org/10.17632/5jxkxbkkkr.1 Developer's repository link:https://doi.org/10.5281/zenodo.4553515 Licensing provisions: MIT Programming language: Python Nature of problem: Analysis software for the next-generation mass spectrometry technique PI-ICR for radioactive isotopes and isomers. Solution method: Using Jupyter notebooks in the Python programming language and libraries of the ROOT analysis framework, the full PI-ICR analysis from the raw data to the final mass value is presented. Furthermore, a new phase-determination approach is introduced offering up to ten times smaller statistical uncertainties on the same dataset compared to the state-of-the-art approaches that are based on X/Y projection fits [14]. This improvement was confirmed by extensive Monte-Carlo simulations. Additional comments including restrictions and unusual features:1.A new phase-determination approach is presented offering up to ten times smaller statistical uncertainties on the same dataset compared to state-of-the-art approaches.2.The code features a robust and precise cyclotron-frequency ratio determination based on simultaneous polynomial fitting with several advantages over the commonly used linear extrapolation.3.The use of Jupyter notebooks and Python allows for a cloud-based analysis on any device or operating system offering a web browser through services such as CERN's SWAN platform or Google Colab.4.The entire frequency determination is based on Bayesian analysis using unbinned maximum likelihood estimation.