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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UNVERDICTED 2representative citing papers
Ultraheavy nuclei have longer energy loss lengths at ≲300 EeV than lighter nuclei, allowing them to explain UHECRs above 100 EeV from sources like collapsars and neutron star mergers while predicting distinct shower maxima.
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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Ultraheavy Ultrahigh-Energy Cosmic Rays
Ultraheavy nuclei have longer energy loss lengths at ≲300 EeV than lighter nuclei, allowing them to explain UHECRs above 100 EeV from sources like collapsars and neutron star mergers while predicting distinct shower maxima.