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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2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2025 2verdicts
UNVERDICTED 2representative citing papers
Supervised ML classification of neutrino events by interaction channel prior to energy reconstruction improves accuracy and sensitivity by 10-20% in simulated DUNE analyses while remaining robust to generator mismodeling.
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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Improving Neutrino Oscillation Measurements through Event Classification
Supervised ML classification of neutrino events by interaction channel prior to energy reconstruction improves accuracy and sensitivity by 10-20% in simulated DUNE analyses while remaining robust to generator mismodeling.