Speaker
Description
Neutron-rich outflows in neutron-star mergers (NSMs) or other explosive events can be subject to substantial heating through the release of rest-mass energy in the course of the rapid neutron-capture (r-) process. This r-process heating can potentially have a significant impact on the dynamics determining the velocity distribution of the ejecta, but due to the complexity of detailed nuclear networks required to describe the r-process self-consistently, hydrodynamic models of NSMs often neglect r-process heating or include it using crude parametrizations. In this talk, I will present a conceptually new method, RHINE, for emulating the r-process and concomitant energy release in hydrodynamic simulations via machine-learning algorithms. I will talk about the effect of r-process heating on the velocity boost, nucleosynthesis yields, and kilonova light curves.