Monte Carlo methods are widely used in particle physics to integrate and sample probability distributions (differential cross sections or decay rates) on multi-dimensional phase spaces. We present a Neural Network (NN) algorithm optimized to perform this task. The algorithm has been applied to several examples of direct relevance for particle physics, including situations with non-trivial features such as sharp resonances and soft/collinear enhancements. Excellent performance has been demonstrated in all examples, with the properly trained NN achieving unweighting efficiencies of between 30% and 75%. In contrast to traditional Monte Carlo algorithms such as VEGAS, the NN-based approach does not require that the phase space coordinates be aligned with resonant or other features in the cross section.

中文翻译：

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基于神经网络的相空间集成方法

蒙特卡洛方法广泛用于粒子物理学中，以对多维相空间上的概率分布（微分截面或衰减率）进行积分和采样。我们提出了一种优化的神经网络（NN）算法来执行此任务。该算法已应用于粒子物理学的直接相关性的几个示例，包括具有非平凡特征（例如尖锐共振和软/共线性增强）的情况。在所有示例中都展示了出色的性能，经过适当训练的神经网络实现了30％至75％的失重效率。与传统的蒙特卡洛算法（如VEGAS）相比，基于NN的方法不需要将相空间坐标与横截面中的共振或其他特征对齐。