FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
Uncertainty-aware machine learning for high energy physics
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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.
DNN ROI detection outperforms traditional wire-by-wire thresholding in identifying ionization signals in SBND and ICARUS detectors and shows greater robustness to performance variations.
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.
citing papers explorer
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Local Conformal Predictions for Calibrated Surrogates
FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
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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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Enhanced Ionization Charge Identification in the Short-Baseline Neutrino Program Neutrino Detectors with Deep Neural Networks
DNN ROI detection outperforms traditional wire-by-wire thresholding in identifying ionization signals in SBND and ICARUS detectors and shows greater robustness to performance variations.
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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.