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For the analysis of data taken by Imaging Air Cherenkov Telescopes (IACTs), alarge number of air shower simulations are needed to derive the instrumentresponse. The simulations are very complex, involving computational andmemory-intensive calculations, and are usually performed repeatedly fordifferent observation intervals to take into account the varying opticalsensitivity of the instrument. The use of generative models based on deepneural networks offers the prospect for memory-efficient storing of hugesimulation libraries and cost-effective generation of a large number ofsimulations in an extremely short time. In this work, we use WassersteinGenerative Adversarial Networks to generate photon showers for an IACT equippedwith the FlashCam design, which has more than $1{,}500$ pixels. Usingsimulations of the H.E.S.S. experiment, we demonstrate the successfulgeneration of high-quality IACT images. The analysis includes a comprehensivestudy of the generated image quality based on low-level observables and thewell-known Hillas parameters that describe the shower shape. We demonstrate forthe first time that the generated images have high fidelity with respect tolow-level observables, the Hillas parameters, their physical properties, aswell as their correlations. The found increase in generation speed in the orderof $10^5$ yields promising prospects for fast and memory-efficient simulationsof air showers for IACTs.

Ultra-Fast Generation of Air Shower Images for Imaging Air Cherenkov Telescopes using Generative Adversarial Networks