Toward using GANs in astrophysical Monte-Carlo simulations
<|reference_start|>Toward using GANs in astrophysical Monte-Carlo simulations: 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.<|reference_end|>
@article{isaac2024toward,
    title={Toward using GANs in astrophysical Monte-Carlo simulations},
    author={Ahab Isaac, Wesley Armour, Karel Ad'amek},
    journal={arXiv preprint arXiv:2402.12396},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12396},
    primaryClass={astro-ph.HE cs.AI}
}