Using conditional variational autoencoders to generate images from atmospheric Cherenkov telescopes by Stanislav Polyakov et al. on Thursday 24 November

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.

arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.12553v1