A Deep Set encoder plus normalizing flow model trained on five million CRPropa 3 events recovers UHECR source parameters without bias and classifies primary composition at over 98 percent accuracy.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Complex normalizing flows nearly correspond to information Kähler-Ricci flows because the log-determinant term matches Ricci curvature under differentiation, recovering a Kähler-Ricci variation in the continuum limit.
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Neural Posterior Estimation for UHECR source inference from 3D propagation simulations
A Deep Set encoder plus normalizing flow model trained on five million CRPropa 3 events recovers UHECR source parameters without bias and classifies primary composition at over 98 percent accuracy.
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Complex normalizing flows can almost be information K\"ahler-Ricci flows
Complex normalizing flows nearly correspond to information Kähler-Ricci flows because the log-determinant term matches Ricci curvature under differentiation, recovering a Kähler-Ricci variation in the continuum limit.