使用神经重要性采样探索相空间
Exploring phase space with Neural Importance Sampling
摘要：
提出了一种新的方法，用于高能物理中散射截面的积分和部分子事件样本的产生。我们提出了一个重要的采样技术，能够克服现有方法的典型缺陷，结合神经网络。该方法保证了全相空间覆盖和所需目标分布的精确再现，在我们的情况下，由平方过渡矩阵元素给出。我们研究了几个有代表性的例子，包括顶夸克对生产和胶子散射到三个和四个胶子的最终状态的算法的性能。
英文摘要：
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.
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。
