ARCANE 重加权：一种解决对撞机事件生成中负权重问题的蒙特卡洛技术
ARCANE Reweighting: A Monte Carlo Technique to Tackle the Negative Weights Problem in Collider Event Generation
AI总结：
针对粒子碰撞MC模拟中负权重事件增加计算资源需求的问题，本文提出精确无偏的ARCANE重加权技术，通过加法重加权重新分配事件路径贡献以减少或消除负权重事件，配套论文有物理实例验证。
摘要：
粒子碰撞的蒙特卡洛（MC）模拟中出现的负权重事件显著增加了当前及未来对撞机实验的计算资源需求。本文介绍并从理论上讨论了一种名为ARCANE重加权的MC技术，用于减少或消除负权重事件。该技术通过（加法重加权的方式）重新分配事件生成器中导致同一最终事件的不同路径的贡献来发挥作用。此技术精确，不会对物理可观测量的分布引入任何偏差。一篇配套论文展示了该技术在一个物理实例中的应用。
英文摘要：
Negatively weighted events, which appear in the Monte Carlo (MC) simulation of particle collisions, significantly increases the computational resource requirements of current and future collider experiments. This paper introduces and theoretically discusses an MC technique called ARCANE reweighting for reducing or eliminating negatively weighted events. The technique works by redistributing (via an additive reweighting) the contributions of different pathways within an event generator that lead to the same final event. The technique is exact and does not introduce any biases in the distributions of physical observables. A companion paper demonstrates the technique for a physics example.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
