Past Events

Abstract

The r-process is the process of creating half of the elements heavier than iron in the Universe. In the neutron-rich region of the nuclear chart, the neutron capture rate competes with the β-decay rate, leading to the r-process occurring. However, the r-process models cannot reproduce the solar r-process abundance distribution well because of the high uncertainties in the theoretical results of nuclear mass, β-decay half-life, β-delayed neutron emission probability (P_n), and fission fragment yield in the neutron-rich regions. To improve understanding of the r-process, machine learning models for nuclear mass, β-decay half-life, P_n, and fission fragment yield which are the key inputs in the r-process will be set up.

Nuclear mass, β-decay half-life, P_n, and fission fragment yield play important roles in r-process and they are interrelated. The nuclear mass governs the reaction energies (Q-value) of the β-decay half-life and P_n. And all three of them are the determining factors for the fission fragment yield. Therefore, a consistent machine-learning model will be developed for all of them. Under the machine learning model, a consistent database of these 4 key inputs will be developed. After obtaining these key quantities from the established machine learning model, r-process nucleosynthesis calculation will be performed. Then, the abundance of elements and the r-process path will be calculated. The abundance of elements calculated will then be compared with the solar r-process abundance distribution, and the sites for the r-process will be investigated.

Anyone interested is welcome to attend.