Toward using GANs in astrophysical Monte-Carlo simulations
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914685618487296 |
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| author | Isaac, Ahab Armour, Wesley Adámek, Karel |
| author_facet | Isaac, Ahab Armour, Wesley Adámek, Karel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12396 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Toward using GANs in astrophysical Monte-Carlo simulations Isaac, Ahab Armour, Wesley Adámek, Karel High Energy Astrophysical Phenomena Artificial Intelligence I.2.0 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. |
| title | Toward using GANs in astrophysical Monte-Carlo simulations |
| topic | High Energy Astrophysical Phenomena Artificial Intelligence I.2.0 |
| url | https://arxiv.org/abs/2402.12396 |