发表机构
Brown University（布朗大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高能物理NLO模拟中负权重事件降低统计效能的问题，提出基于最优传输距离度量的单元重加权方案，在Z+jets事件上验证了其有效性与性能。
AI 中文摘要
随着高能物理（HEP）实验结果的精度不断提高，对精确蒙特卡洛（MC）模拟的需求也随之增加。在次领头阶（NLO）及更高阶精度下生成事件，会带来负权重事件。这些负权重事件降低了样本的统计效能，增加了需要产生的事件数量，并给本已有限的计算资源带来压力。我们提出了一种事后重加权方案，该方案采用基于单元的重采样，并使用红外安全（IRC-safe）度量来定义单元半径。最优度量将运动学上相似的事件嵌入同一单元内，从而在重加权时最小化偏差。这促使我们探索基于最优传输（OT）的距离度量。我们比较了重加权算法在不同度量选择下的性能，并明确展示了其在NLO精度下产生的模拟Z+jets事件上的表现。
英文摘要
As the accuracy of experimental results in high energy physics (HEP) increases, so does the demand for precision Monte Carlo (MC) simulation. Higher-accuracy event generation at next-to-leading order (NLO) and beyond brings with it negatively weighted events. These negatively weighted events reduce the statistical power of samples, increasing the number of events that need to be produced and straining already limited computational resources. We present a post-hoc reweighting scheme that employs cell-based resampling using an IRC-safe metric to define the cell radii. An optimal metric embeds kinematically similar events within the same cells, minimizing bias when reweighted. This motivates our exploration of a Optimal Transport (OT) based distance metrics. We compare the performance of the reweighting algorithm with different choices of metric, and explicitly demonstrate the performance on simulated Z+jets events produced at NLO accuracy.
