  High-energy particles hitting the upper atmosphere of the Earth produce
extensive air showers that can be detected from the ground level using imaging
atmospheric Cherenkov telescopes. The images recorded by Cherenkov telescopes
can be analyzed to separate gamma-ray events from the background hadron events.
Many of the methods of analysis require simulation of massive amounts of events
and the corresponding images by the Monte Carlo method. However, Monte Carlo
simulation is computationally expensive. The data simulated by the Monte Carlo
method can be augmented by images generated using faster machine learning
methods such as generative adversarial networks or conditional variational
autoencoders. We use a conditional variational autoencoder to generate images
of gamma events from a Cherenkov telescope of the TAIGA experiment. The
variational autoencoder is trained on a set of Monte Carlo events with the
image size, or the sum of the amplitudes of the pixels, used as the conditional
parameter. We used the trained variational autoencoder to generate new images
with the same distribution of the conditional parameter as the size
distribution of the Monte Carlo-simulated images of gamma events. The generated
images are similar to the Monte Carlo images: a classifier neural network
trained on gamma and proton events assigns them the average gamma score 0.984,
with less than 3% of the events being assigned the gamma score below 0.999. At
the same time, the sizes of the generated images do not match the conditional
parameter used in their generation, with the average error 0.33.
