论文标题
绿洲：事件产生的最佳分析特定重要性抽样
OASIS: Optimal Analysis-Specific Importance Sampling for event generation
论文作者
论文摘要
我们提出了一种称为最佳分析特定重要性采样（OASIS）的技术，以减少高能实验分析所需的模拟事件数量以达到目标灵敏度。我们提供配方以获取最佳采样分布，这些分布优先将事件生成重点放在具有高实用性的相位区域上。绿洲在蒙特卡洛管道的各个阶段都可以节省资源，包括全探测器模拟，并且是寻求加快模拟管道的方法的补充。
We propose a technique called Optimal Analysis-Specific Importance Sampling (OASIS) to reduce the number of simulated events required for a high-energy experimental analysis to reach a target sensitivity. We provide recipes to obtain the optimal sampling distributions which preferentially focus the event generation on the regions of phase space with high utility to the experimental analyses. OASIS leads to a conservation of resources at all stages of the Monte Carlo pipeline, including full-detector simulation, and is complementary to approaches which seek to speed-up the simulation pipeline.
