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.

中文翻译：

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改进的神经网络蒙特卡洛模拟

arXiv：1810.11509中提出的基于人工神经网络（ANN）的Parton级事件的蒙特卡罗模拟算法用于执行$ H \至4 \ ell $衰减的模拟。为了避免数值不稳定性，已经对训练算法进行了改进。ANN评估的积分衰减宽度在真实值的0.7％以内，并且失重效率达到26％。虽然ANN在输入和输出空间之间不是自动双射的，这可能会导致模拟质量问题，但我们认为训练过程自然更喜欢双射图，并证明训练后的ANN具有很好的近似双射性。