Generating Synthetic Images of Gamma-Ray Events for Imaging Atmospheric Cherenkov Telescopes Using Conditional Generative Adversarial Networks

In recent years, machine learning techniques have seen huge adoption in astronomy applications. In this work, we discuss the generation of realistic synthetic images of gamma-ray events, similar to those captured by imaging atmospheric Cherenkov telescopes (IACTs), using the generative model called...

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Veröffentlicht in:	Moscow University physics bulletin 2023-12, Vol.78 (Suppl 1), p.S64-S70
Hauptverfasser:	Dubenskaya, Yu. Yu, Kryukov, A. P., Demichev, A. P., Polyakov, S. P., Zhurov, D. P., Gres, E. O., Vlaskina, A. A.
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer simulation Gamma ray astronomy Gamma rays Generative adversarial networks Machine learning Machine Learning in Fundamental Physics Mathematical and Computational Physics Monte Carlo simulation Neural networks Parameters Physics Physics and Astronomy Telescopes Theoretical
Online-Zugang:	Volltext
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