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Uncovering a rich atlas of physical concepts in a neutrino model reveals that interpretability can dramatically enhance angular resolution in particle physics tasks.
Injection-driven, weakly supervised training can achieve reliable Real-Bogus classification without human labels, providing calibrated uncertainties and robust performance under class contamination.
