  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 (image size), which is in direct
correlation with the energy of primary particles. We used a cGAN technique to
generate images similar to whose obtained in the TAIGA-IACT experiment. As a
training set, we used a set of two-dimensional images generated using the TAIGA
Monte Carlo simulation software. We artificiallly divided the training set into
10 classes, sorting images by size and defining the boundaries of the classes
so that the same number of images fall into each class. These classes were used
while training our network. The paper shows that for each class, the size
distribution of the generated images is close to normal with the mean value
located approximately in the middle of the corresponding class. We also show
that for the generated images, the total image size distribution obtained by
summing the distributions over all classes is close to the original
distribution of the training set. The results obtained will be useful for more
accurate generation of realistic synthetic images similar to the ones taken by
IACTs.
