Using a Conditional Generative Adversarial Network to Control the Statistical Characteristics of Generated Images for IACT Data Analysis by Julia Dubenskaya et al. on Wednesday 30 November

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.

arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.15807v1