Speaker
Description
Recent decades have witnessed exponential growth in both the quality and volume of experimental nuclear data, driven by advancements in detector technologies and accelerator capabilities. Gamma- ray spectroscopy, in particular, has benefited from these technological improvements, enabling the collection of increasingly complex and high-dimensional datasets from large-scale spectrometers such as GRIFFIN and TIGRESS at TRIUMF, located in Vancouver, Canada. However, the traditional, labor-intensive methods of visually inspecting one- and two-dimensional histograms, time-gating on gamma-gamma coincidences, fitting spectra, and building upon existing level diagrams have struggled to keep pace with the mounting data and have remained prone to human error.
To specifically address the challenges associated with constructing excited-state decay schemes, this research reformulates the construction of level schemes as an inverse optimization problem, taking the gamma-ray singles spectrum and symmetric gamma-gamma coincidence matrices as primary inputs into the algorithm. Using modern software packages for numerical optimization, a machine learning framework is employed to recover directed level-scheme graphs.