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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Magnetically driven shocks from neutron star merger remnants can reheat ejecta to nuclear statistical equilibrium, alter r-process yields, and produce observable changes in kilonova color and light curves.
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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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Effects of magnetically driven shocks on nucleosynthesis and kilonovae from neutron star mergers
Magnetically driven shocks from neutron star merger remnants can reheat ejecta to nuclear statistical equilibrium, alter r-process yields, and produce observable changes in kilonova color and light curves.