论文标题
改进的神经网络蒙特卡洛模拟
Improved Neural Network Monte Carlo Simulation
论文作者
论文摘要
基于ARXIV中提出的人工神经网络（ANN）的Parton级事件的蒙特卡洛模拟算法：1810.11509用于执行$ h \ f the 4 \ ell $ decay的模拟。已经实施了培训算法的改进，以避免数值不稳定性。 ANN评估的综合衰减宽度占真实值的0.7％，而未加权效率达到了26％。虽然ANN在输入空间和输出空间之间并非自动任何型，这可能会导致模拟质量的问题，但我们认为训练程序自然更喜欢生物地图，并证明训练有素的ANN是非常良好的近似值。
The algorithm for Monte Carlo simulation of parton-level events based on an Artificial Neural Network (ANN) proposed in arXiv:1810.11509 is used to perform a simulation of $H\to 4\ell$ decay. Improvements in the training algorithm have been implemented to avoid numerical instabilities. The integrated decay width evaluated by the ANN is within 0.7% of the true value and unweighting efficiency of 26% is reached. While the ANN is not automatically bijective between input and output spaces, which can lead to issues with simulation quality, we argue that the training procedure naturally prefers bijective maps, and demonstrate that the trained ANN is bijective to a very good approximation.
