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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 alpha decay rates as well as the theoretical uncertainty of the predictions are evaluated based on a Neural Network algorithm. The uncertainty of the Q value and nuclear mass predictions can be further constrained by a novel Bayesian mixture mass model.
Biography:
Chong Qi is an associate professor at Department of Physics at Royal Institute of Technology (KTH), and also serves as chair of the Nuclear Physics section of the Swedish Physics Society. He obtained his Ph.D. from Peking University in 2009. He held a postdoctoral position at KTH from 2009 to 2011, and then researcher (permanent position) from 2012 to 2014. He was appointed as assistant professor at KTH in 2014 and promoted to associate professor in 2018. Prof. Qi has published more than 150 papers in leading journals in the field (including more than 40 in letter-type and 3 reviews) with ~3300 citations.
Host: Prof. Yu-Min Zhao
Alternative online link: https://meeting.tencent.com/dm/c8m2Gjoes28O (id: 204547863 passcode: 123456）