Toward Using GANs in Astrophysical Monte-Carlo Simulations
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-Jüttner 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.
Fields of Study & Subjects
Scientific Research and Discoveries
Publication details
- Journal / source
- Astronomical Society of the Pacific Conference Series
- Publisher
- Astronomical Society of the Pacific Conference Series
- Volume
- 541
- Pages
- 234–237
- Publication date
- 28 February 2026
- Language
- EN