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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Simulations of 195 stellar progenitors indicate that neutrino flavor conversion alters explodability and remnant mass distributions, particularly for stars of 16-30 solar masses.
citing papers explorer
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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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Neutrino Flavor Conversion Shapes the Rate of Failed Core-collapse Supernovae
Simulations of 195 stellar progenitors indicate that neutrino flavor conversion alters explodability and remnant mass distributions, particularly for stars of 16-30 solar masses.