论文标题
详尽的神经重要性抽样应用于蒙特卡洛事件生成
Exhaustive Neural Importance Sampling applied to Monte Carlo event generation
论文作者
论文摘要
中微子振荡实验所需的准确中微子核心横截面模型的产生需要同时描述许多自由度和精确计算以模拟核反应。完整模型的详细计算使蒙特卡洛发生器缓慢而不切实际。我们提出了详尽的神经重要性采样（ENIS），这是一种基于归一化流的方法，以自动有效地找到拒绝采样的合适建议密度，并讨论该技术如何解决拒绝算法的常见问题。
The generation of accurate neutrino-nucleus cross-section models needed for neutrino oscillation experiments require simultaneously the description of many degrees of freedom and precise calculations to model nuclear responses. The detailed calculation of complete models makes the Monte Carlo generators slow and impractical. We present Exhaustive Neural Importance Sampling (ENIS), a method based on normalizing flows to find a suitable proposal density for rejection sampling automatically and efficiently, and discuss how this technique solves common issues of the rejection algorithm.
