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arXiv · 2402.12396

Toward using GANs in astrophysical Monte-Carlo simulations

Ahab Isaac·Wesley Armour·Karel Adámek
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Abstract

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.

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Ahab Isaac, Wesley Armour, Karel Adámek. 2024-02-16. Toward using GANs in astrophysical Monte-Carlo simulations. https://arxiv.org/abs/2402.12396

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