In anisotropic neutrino gases, νν-bar pairing instabilities emerge when the excessive pair-occupation number distribution changes sign, producing pair conversions at growth rates comparable to fast flavor instabilities.
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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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Collective neutrino-antineutrino pair oscillations
In anisotropic neutrino gases, νν-bar pairing instabilities emerge when the excessive pair-occupation number distribution changes sign, producing pair conversions at growth rates comparable to fast flavor instabilities.
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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%.