Abstract:Nuclear physical inputs are critical for predicting the abundance of r-process elements. Extensive sensitivity studies have recently been started to be performed to gauge the impact of the individual properties of nuclei on the r-process. In this talk, I would like to study in particular the impact of the large uncertainties in the theoretical predictions of the masses of neutron-rich Sn isotopes and the location of the neutron drip line on the abundance of r-process abundances. The uncertainties in the predicted r-process abundances are obtained through large - scale network calculations by simultaneously varying the masses and reaction rates of Sn within the predicted mass uncertainties. To extend that study to the full nuclear chart, we are developing an extended REACLIB Stockholm Open Library for Nuclear Astrophysical Reaction Rate Variations (REACLIB-SOLNAR) where the Q value and temperature dependence of the neutron capture rates and the uncertainty in predictions are evaluated based on a Neural network algorithm.

Profile：Chong Qi is a tenured Associate Professor and Ph.D. supervisor at the Royal Institute of Technology in Sweden. He received his Ph.D. from Peking University in 2009, worked as a postdoctoral researcher at the Royal Institute of Technology in Sweden from 2009 to 2011, and became a tenured Associate Professor in 2018. His research interests include theoretical nuclear physics, α-decay, superfluid theory, many-body correlations, and Interdisciplinary research fields of quantum many-body theory. He has published over 100 papers in famous journals such as Nature and PRL, with more than 3000 citations.