FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
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A one-step generative model for calorimeter showers, using MeanFlow, a learned Gaussian-mixture prior, and a physics-constrained loss, matches diffusion-model quality at far fewer evaluations.
Compares ensemble, Bayesian, and evidential regression approaches for uncertainty quantification in amplitude surrogates and shows they detect localized training data issues.
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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CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters
A one-step generative model for calorimeter showers, using MeanFlow, a learned Gaussian-mixture prior, and a physics-constrained loss, matches diffusion-model quality at far fewer evaluations.
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Amplitude Uncertainties Everywhere All at Once
Compares ensemble, Bayesian, and evidential regression approaches for uncertainty quantification in amplitude surrogates and shows they detect localized training data issues.