RHINE emulates r-process heating in NSM hydro simulations via neural networks trained on full nuclear trajectories, achieving <10% agreement with post-processing and boosting BH-torus ejecta mass by 40%.
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Monte Carlo simulation of post-merger remnant shows pair annihilation rates greatly increased in cold low-density regions and inelastic electron scattering important for heavy-lepton neutrino thermalization, processes not included in prior merger simulations.
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R-process heating implementation in hydrodynamic simulations with neural networks
RHINE emulates r-process heating in NSM hydro simulations via neural networks trained on full nuclear trajectories, achieving <10% agreement with post-processing and boosting BH-torus ejecta mass by 40%.
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Assessing the Relative Importance of Neutrino Matter Interaction Channels in Post-Merger Remnant of Binary Neutron Stars
Monte Carlo simulation of post-merger remnant shows pair annihilation rates greatly increased in cold low-density regions and inelastic electron scattering important for heavy-lepton neutrino thermalization, processes not included in prior merger simulations.