Reweighting Monte Carlo Predictions and Automated Fragmentation Variations in Pythia 8
arxiv(2023)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要