A Deeper Understanding of the R Process

The overarching aim of my PhD research is to deepen our understanding of r-process nu- cleosynthesis. The initial phase of my work involved developing a Machine Learning-based nuclear mass model, specifically tailored to predict nuclear masses crucial for r-process nucleosynthesis. This model was then applied to simulate the r-process, focusing on neutron-rich nuclei significant to nucleosynthesis. The simulations yielded r-process abundance patterns, extending up to thorium and uranium, that align qualitatively with the observed solar system abundance patterns, with the characteristic peaks well positioned. Advancing our study further, we introduced a novel graph-based methodology named GrRproc for calculating r-process abundances. This innovative approach provides an in-depth analysis of the reaction dynamics and traces the abundance flows within the r-process. GrRproc uniquely enables the examination of the relative contribution of neighboring nuclei to specific species, offering a detailed perspective on abundance evolution. Employing GrRproc, we conducted a comprehensive study of isotopic abundance flows during the critical freeze-out phase of the r-process, shedding light on the intricate dynamics governing nuclear synthesis