# Amplitude Uncertainties Everywhere All at Once

## TL;DR

This paper introduces new methods for quantifying uncertainties in amplitude regression models crucial for LHC event generation, focusing on noise reduction, calibration, and identifying data gaps.

## Contribution

It presents a novel approach to learn well-calibrated systematic uncertainties and demonstrates evidential regression as an efficient sampling-free uncertainty quantification method.

## Key findings

- Network ensembles can reduce noise and biases in amplitude predictions.
- Evidential regression provides a sampling-free way to quantify uncertainties.
- Bayesian networks and ensembles effectively identify numerical noise and data gaps.

## 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.