Amplitude uncertainties everywhere all at once
Abstract
Ultra-fast, precise, and controlled amplitude surrogates are essential for future LHC event generation. First, we investigate the noise reduction and biases of network ensembles and outline a new method to learn well-calibrated systematic uncertainties for them. We also establish evidential regression as a sampling-free method for uncertainty quantification. In a second part, we tackle localized disturbances for amplitude regression and demonstrate that learned uncertainties from Bayesian networks, ensembles, and evidential regression all identify numerical noise or gaps in the training data.
Fields of Study & Subjects
Particle physics theoretical and experimental studies
High-Energy Particle Collisions Research
Quantum Chromodynamics and Particle Interactions
Publication details
- Journal / source
- SciPost Physics
- Publisher
- Stichting SciPost
- Volume
- 20
- Issue
- 3
- Publication date
- 12 March 2026
- Language
- EN