| |
 |
| Paper: |
Toward Using GANs in Astrophysical Monte-Carlo Simulations |
| Volume: |
541, ADASS XXXIII |
| Page: |
234 |
| Authors: |
Isaac, Ahab; Armour, Wesley; Adámek, Karel |
| DOI: |
10.26624/WSMO2413 |
| 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. |
|
|
|
|
|
