Reweighting Monte Carlo Predictions and Automated Fragmentation Variations in Pythia 8

TL;DR

This paper introduces a Monte Carlo-veto based method for estimating uncertainties in collider event simulations, enabling efficient exploration of model parameter variations without rerunning full simulations.

Contribution

It extends previous uncertainty estimation techniques to include the Lund string-fragmentation model, reducing computational costs for parameter sensitivity studies.

Findings

Enables reweighting of simulated events for different model parameters

Includes uncertainty estimates for the Lund string-fragmentation model

Reduces the need for multiple full simulation runs

Abstract

This work reports on a method for uncertainty estimation in simulated collider-event predictions. The method is based on a Monte Carlo-veto algorithm, and extends previous work on uncertainty estimates in parton showers by including uncertainty estimates for the Lund string-fragmentation model. This method is advantageous from the perspective of simulation costs: a single ensemble of generated events can be reinterpreted as though it was obtained using a different set of input parameters, where each event now is accompanied with a corresponding weight. This allows for a robust exploration of the uncertainties arising from the choice of input model parameters, without the need to rerun full simulation pipelines for each input parameter choice. Such explorations are important when determining the sensitivities of precision physics measurements. Accompanying code is available at https://gitlab.com/uchep/mlhad-weights-validation.