论文标题
用嵌套采样探索相空间
Exploring phase space with Nested Sampling
论文作者
论文摘要
我们介绍了嵌套采样算法的第一个应用，以探索粒子碰撞事件的高维相空间。我们描述了该算法的适应性，该算法旨在执行贝叶斯推理计算，对党散射横截面的整合以及根据相应的平方矩阵元素分布的单个事件的产生。作为第一个具体示例，我们考虑到3-，4和5-Gluon最终状态中的Gluon散射过程，并将其与已建立的采样技术进行比较。从平坦的先前分布嵌套采样开始优于Vegas算法，并获得与专用的多渠道重要性采样器相当的结果。我们概述了将嵌套采样与非平板先验分布相结合的可能方法，以进一步降低积分估计的方差并提高未加权的效率。
We present the first application of a Nested Sampling algorithm to explore the high-dimensional phase space of particle collision events. We describe the adaptation of the algorithm, designed to perform Bayesian inference computations, to the integration of partonic scattering cross sections and the generation of individual events distributed according to the corresponding squared matrix element. As a first concrete example we consider gluon scattering processes into 3-, 4- and 5-gluon final states and compare the performance with established sampling techniques. Starting from a flat prior distribution Nested Sampling outperforms the Vegas algorithm and achieves results comparable to a dedicated multi-channel importance sampler. We outline possible approaches to combine Nested Sampling with non-flat prior distributions to further reduce the variance of integral estimates and to increase unweighting efficiencies.
