Towards Automation for γ-Ray Spectroscopy

Over the past century, γ-ray spectroscopy has been a powerful experimental tool for probing the structure of atomic nuclei. Incremental improvements in radiation detectors and ion accelerator technologies have dramatically increased both the quality and quantity of the nuclear data that can be collected in a single measurement. However, traditional methods of analyzing spectroscopic data towards the goal of constructing accurate nuclear decay schemes have remained largely unchanged over time. Visually inspecting one- and two-dimensional histograms, time-gating on γ-ray coincidence data, fitting spectra, and building upon previously reported level diagrams within the academic literature are time-consuming and error-prone processes, which would likely benefit from the application of modern data science techniques. Here, we discuss the development of computational tools for analyzing high-statistics, γ-ray datasets, presenting preliminary capabilities benchmarked against evaluated nuclear data. In addition to automating familiar analysis steps, such as multidimensional background subtraction and Gaussian peak-fitting, we also propose a reformulation of the scheme-building procedure as a Bayesian inverse problem. Using existing numerical optimization methods, this novel approach to spectroscopic analysis enables the recovery of a directed level-scheme graph from symmetric γ-γ coincidence matrices.