Ultra-fast generation of air shower images for Imaging Air Cherenkov Telescopes using Generative Adversarial Networks

Abstract

For the analysis of data taken by Imaging Air Cherenkov Telescopes (IACTs), a large number of air shower simulations are needed to derive the instrument response. The simulations are very complex, involving computational and memory-intensive calculations, and are usually performed repeatedly for different observation intervals to take into account the varying optical sensitivity of the instrument. The use of generative models based on deep neural networks offers the prospect for memory-efficient storing of huge simulation libraries and cost-effective generation of a large number of simulations in an extremely short time. In this work, we use Wasserstein Generative Adversarial Networks to generate photon showers for an IACT equipped with the FlashCam design, which has more than 1,500 pixels. Using simulations of the H.E.S.S. experiment, we demonstrate the successful generation of high-quality IACT images. The analysis includes a comprehensive study of the generated image quality based on low-level observables and the well-known Hillas parameters that describe the shower shape. We demonstrate for the first time that the generated images have high fidelity with respect to low-level observables, the Hillas parameters, their physical properties, as well as their correlations. The found increase in generation speed in the order of 105 yields promising prospects for fast and memory-efficient simulations of air showers for IACTs.