Utilizing machine learning for fast-timing calibration between LaBr3(Ce) detectors in the neutron-rich N = 20 and N=50 regions

The nuclear shell evolution can be attributed to changing proton and neutron numbers within the nucleus. Nuclear transition rates, which significantly depend on a precise measurement of level lifetimes, are sensitive indicators for investigating the nuclear shell evolution. Experiments using β decay in the neutron-rich N=20 and N=50 regions were performed at the National Superconducting Cyclotron Laboratory (NSCL). β decays were correlated with the implantation of radioactive nuclei, using a CeBr3 scintillator coupled to a Position-Sensitive Photomultiplier Tube (PSMPT), through spatial and temporal analysis techniques. In these experiments, 15 LaBr3(Ce) detectors were employed for γ radiation detection and fast timing measurement. Time-difference spectra between β decays and γ radiation detection were used to measure half-lives. Calibration of the LaBr3 timing response relative to the CeBr3 was performed along with further corrections for the energy dependent time-walk effects were made using machine learning techniques in the neutron-rich N=20 and N=50 regions. Validation results from comparing the output from the machine learning model with the output from the analytical technique used in previous analysis will be presented.