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

  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},
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… 




The possibility of improving the generated images by preprocessing the training dataset is discussed, and an example of a GAN built and trained with these requirements in mind is presented.

Generative deep fields: arbitrarily sized, random synthetic astronomical images through deep learning

Generative adversarial networks (GANs) are a class of artificial neural network that can produce realistic, but artificial, images that resemble those in a training set. In typical GAN

Modeling Images of Proton Events for the TAIGA Project Using a Generative Adversaria Network: Features of the Network Architecture and the Learning Process

  • J. DubenskayaA. KryukovA. Demichev
  • Computer Science, Physics
    Proceedings of The 5th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2021)
  • 2021
A machine learning method, namely the generative adversarial networks (GANs), is applied to generate images of proton events similar to those obtained from IACTs of the TAIGA project to significantly increase the speed of image generation.

Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit

The ability to better recover detailed features from low-signal-to-noise and low angular resolution imaging data significantly increases the ability to study existing data sets of astrophysical objects as well as future observations with observatories such as the Large Synoptic Sky Telescope and the Hubble and James Webb space telescopes.

Fast simulation of gamma/proton event images for the TAIGA-IACT experiment using generative adversarial networks

High energy cosmic rays and gamma rays interacting the atmosphere produce extensive air showers (EAS) of secondary particles emitting Cherenkov light. Being detected with a telescope this light forms

Solar image deconvolution by generative adversarial network

A deep neural network, referring to Generative Adversarial Network (GAN), is proposed for solar image deconvolution and the experimental results demonstrate that the proposed model is markedly better than traditional CLEAN on solar images.

Conditional Generative Adversarial Nets

The conditional version of generative adversarial nets is introduced, which can be constructed by simply feeding the data, y, to the generator and discriminator, and it is shown that this model can generate MNIST digits conditioned on class labels.

Fast cosmic web simulations with generative adversarial networks

This paper demonstrates the application of a machine learning technique called Generative Adversarial Networks (GAN) to learn models that can efficiently generate new, physically realistic realizations of the cosmic web, and finds a good match for the correlation matrix of full Pk$P_{k}$ range for 100 Mpc data and of small scales for 500 Mpc, with ∼20% disagreement for large scales.

Galaxy Image Simulation Using Progressive GANs

The proposed solution generates naturalistic images of galaxies that show complex structures and high diversity, which suggests that data-driven simulations using machine learning can replace many of the expensive model-driven methods used in astronomical data processing.

ExoGAN: Retrieving Exoplanetary Atmospheres Using Deep Convolutional Generative Adversarial Networks

This work introduces ExoGAN, the Exoplanet Generative Adversarial Network, a new deep learning algorithm able to recognise molecular features, atmospheric trace-gas abundances and planetary parameters using unsupervised learning.