A DNN-based region of interest detection method for SBN neutrino detectors outperforms traditional wire-by-wire thresholding in identification accuracy and reconstruction quality while being more robust to performance variations.
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Neural networks for HEP tasks can be fooled at significant rates by subtle perturbations inside uncertainty envelopes, revealing hidden systematics not captured by conventional methods.
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Enhanced Ionization Charge Identification in the Short-Baseline Neutrino Program Neutrino Detectors with Deep Neural Networks
A DNN-based region of interest detection method for SBN neutrino detectors outperforms traditional wire-by-wire thresholding in identification accuracy and reconstruction quality while being more robust to performance variations.
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Uncovering Hidden Systematics in Neural Network Models for High Energy Physics
Neural networks for HEP tasks can be fooled at significant rates by subtle perturbations inside uncertainty envelopes, revealing hidden systematics not captured by conventional methods.