Generation of Imaging Air Cherenkov Telescope images using Diffusion Models

TL;DR

This paper introduces score-based diffusion models for generating realistic Imaging Air Cherenkov Telescope images, outperforming GANs especially for proton showers, and enabling efficient, analysis-ready simulations for gamma-ray astronomy.

Contribution

It is the first application of diffusion models to generate IACT images, demonstrating superior quality over GANs for both gamma-ray and proton showers, and establishing a new analysis-ready surrogate modeling approach.

Findings

Diffusion models outperform GANs in generating proton shower images.

Generated images are statistically indistinguishable from simulations at analysis level.

Diffusion models enable fast, analysis-ready simulations for gamma-ray astronomy.

Abstract

Substantial amounts of air-shower simulations are needed to derive the instrument response for analyzing Imaging Air Cherenkov Telescope (IACT) data. This process is both computationally intensive and requires repetition under varying observation conditions, due to detector aging, changes in the atmosphere, or the instrument hardware. Generative models offer an efficient alternative, significantly accelerating simulations while compactly storing extensive simulation libraries, and providing a differentiable surrogate model of the instrument. However, their applicability has so far been limited in gamma-ray astronomy, particularly for modeling hadronic showers that dominate the background and exhibit significant intrinsic fluctuations that are challenging to model. In this study, we present the first application of score-based diffusion models to generate monoscopic $\gamma$-ray and proton shower images with nearly 2,000 pixels and benchmark the performance against Wasserstein GANs using H.E.S.S. simulations. We examine quality using both low-level parameters and well-established shower-shape observables, and assess analysis readiness via state-of-the-art $\gamma$-hadron separation. While GAN-based approaches can reproduce $\gamma$-ray showers with high fidelity, they fail to generate proton events of comparable quality, leading to a measurable degradation in analysis performance. In contrast, score-based diffusion modles achieve significantly superior quality for $\gamma$-ray and proton showers, accurately reproducing high-level correlations and generating events that are statistically indistinguishable from simulations at the analysis level. These results establish diffusion-based models as the first analysis-ready surrogate model of a single IACT, opening new prospects for fast instrument response generation, detector optimization, and connected downstream tasks.