论文标题
探索具有神经重要性抽样的相位空间
Exploring phase space with Neural Importance Sampling
论文作者
论文摘要
我们提出了一种新颖的方法，用于整合散射横截面和高能物理学中的党派事件样品的产生。我们提出了一种重要的抽样技术，能够通过合并神经网络来克服现有方法的典型缺陷。该方法保证了全相空间覆盖范围和所需目标分布的精确再现，在我们的情况下，正方形过渡矩阵元素给出。我们研究了一些代表性示例的算法的性能，包括Quark对的产生和Gluon散射，分为三胶状和四聚光的最终状态。
We present a novel approach for the integration of scattering cross sections and the generation of partonic event samples in high-energy physics. We propose an importance sampling technique capable of overcoming typical deficiencies of existing approaches by incorporating neural networks. The method guarantees full phase space coverage and the exact reproduction of the desired target distribution, in our case given by the squared transition matrix element. We study the performance of the algorithm for a few representative examples, including top-quark pair production and gluon scattering into three- and four-gluon final states.
