Investigation of r-process abundance patterns through impact studies and machine learning methods

Metal poor stars are ideal candidates for studies of nucleosynthesis processes, in particular the rapid neutron capture process (r-process), as their abundance patterns offer unique insights into elemental production and evolution in the early stages of the Universe. We investigate abundance predictions calculated using the newly obtained experimental masses of neutron-rich isotopes ^149-151Cs and ^151,152Ba, measured using the Multi-Reflection Time-of-Flight (MR-ToF) mass spectrometer at TRIUMF's Ion Trap for Atomic and Nuclear science (TITAN). These mass values are incorporated into important r-process inputs including neutron capture rates, β-decay rates, and one-neutron separation energies. We examine the impact on abundance ratio predictions of elements in the lanthanide region, and compare our predictions to Solar and stellar abundances. Furthermore, work to incorporate machine learning methods to aid our studies of the nucleosynthesis processes and the analysis of metal poor star abundances will also be discussed.