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The tin isotope 100Sn is of singular interest for nuclear structure due to its closed-shell proton and neutron configurations. It is also the heaviest nucleus comprising protons and neutrons in equal numbers—a feature that enhances the contribution of the short-range proton–neutron pairing interaction and strongly influences its decay via the weak interaction. Decay studies in the region of 100Sn have attempted to prove its doubly magic character1but few have studied it from an ab initio theoretical perspective2,3, and none of these has addressed the odd-proton neighbours, which are inherently more difficult to describe but crucial for a complete test of nuclear forces. Here we present direct mass measurements of the exotic odd-proton nuclide 100In, the beta-decay daughter of 100Sn, and of 99In, with one proton less than 100Sn. We use advanced mass spectrometry techniques to measure 99In, which is produced at a rate of only a few ions per second, and to resolve the ground and isomeric states in 101In. The experimental results are compared with ab initio many-body calculations. The 100-fold improvement in precision of the 100In mass value highlights a discrepancy in the atomic-mass values of 100Sn deduced from recent beta-decay results4,5.

Abstract: A selection procedure with no optimization step called LOLA, for Learning Out of Leaders with Adaptation is proposed. LOLA is an auto-driven algorithm with two thresholding steps. The consistency of the LOL procedure (the non adaptive version of LOLA) is proved under sparsity conditions and simulations are conducted to illustrate the practical good performances of LOLA. The behavior of the algorithm is studied when instrumental variables are artificially added without a priori significant connection to the model. Finally, the problem of empirically verifying the conditional convergence hypothesis used in economics concerning the growth rate is studied. To avoid unnecessary discussion about the choice and the pertinence of instrumental variables, the model is embedded in a very high dimensional setting. Using the LOLA algorithm, a solution for modeling the link between the growth rate and the initial level of the gross domestic product is provided and the convergence hypothesis is empirically proved. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]