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.
Uncertainty-aware machine learning for high energy physics
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An iterative ranking-based optimization of cut-and-count using MadAnalysis5 enhances signal-background separation and discovery reach for singly charged Higgs in the Two Higgs Doublet Model.
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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.
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Optimizing The Cut And Count Method In Phenomenological Studies
An iterative ranking-based optimization of cut-and-count using MadAnalysis5 enhances signal-background separation and discovery reach for singly charged Higgs in the Two Higgs Doublet Model.
- Enhanced Ionization Charge Identification in the Short-Baseline Neutrino Program Neutrino Detectors with Deep Neural Networks