Using a Conditional Generative Adversarial Network to Control the Statistical Characteristics of Generated Images for IACT Data Analysis

@article{Dubenskaya2022UsingAC,
  title={Using a Conditional Generative Adversarial Network to Control the Statistical Characteristics of Generated Images for IACT Data Analysis},
  author={Julia Y. Dubenskaya and Alexander Kryukov and Andrey Demichev and Stanislav Polyakov and Elizaveta Gres and Anna Vlaskina},
  journal={ArXiv},
  year={2022},
  volume={abs/2211.15807}
}
Generative adversarial networks are a promising tool for image generation in the astronomy domain. Of particular interest are conditional generative adversarial networks (cGANs), which allow you to divide images into several classes according to the value of some property of the image, and then specify the required class when generating new images. In the case of images from Imaging Atmospheric Cherenkov Telescopes (IACTs), an important property is the total brightness of all image pixels… 
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