Accurate modelling of spectra produced by X-ray sources requires the use of
Monte-Carlo simulations. These simulations need to evaluate physical processes,
such as those occurring in accretion processes around compact objects by
sampling a number of different probability distributions. This is
computationally time-consuming and could be sped up if replaced by neural
networks. We demonstrate, on an example of the Maxwell-J\"uttner distribution
that describes the speed of relativistic electrons, that the generative
adversarial network (GAN) is capable of statistically replicating the
distribution. The average value of the Kolmogorov-Smirnov test is 0.5 for
samples generated by the neural network, showing that the generated
distribution cannot be distinguished from the true distribution.

对 X 射线源产生的光谱进行精确建模需要使用蒙特卡罗模拟。这些模拟需要评估物理过程，例如在致密物体周围的吸积过程中发生的过程，通过对多个不同的概率分布进行采样。我们以描述相对论电子速度的 Maxwell-Jüttner 分布为例，证明生成对抗网络（GAN）能够统计复制该分布。通过神经网络生成的样本的 Kolmogorov-Smirnov 测试的平均值为 0.5，表明生成的分布与真实分布无法区分。