Generation of Imaging Air Cherenkov Telescope images using Diffusion Models

arXiv:2603.06364 · astro-ph.IM, astro-ph.HE · Submitted 2026-03-06 · Read on arXiv

astro-ph.IM, astro-ph.HE

Submitted: 2026-03-06

Updated: 2026-09-01

Comments: 32 pages. 16 figures. This is the Accepted Manuscript of an article published in JCAP

Journal ref: JCAP08(2026)079

DOI: 10.1088/1475-7516/2026/08/079

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Diffusion Model — 31× in this paper · explained in 8 episodes
• Diffusion Models — 26× in this paper · explained in 6 episodes
• Generative Model — 14× in this paper · explained in 2 episodes
• Generative Models — 14× in this paper · explained in 2 episodes
• Fidelity — 9× in this paper · explained in 2 episodes
• Core — 7× in this paper · explained in 2 episodes
• Imaging Air Cherenkov Telescope — 6× in this paper
• Benchmark — 5× in this paper · explained in 2 episodes
• Wasserstein Distance — 5× in this paper · explained in 4 episodes
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Generative Adversarial Networks (GANs) — 3× in this paper · explained in 3 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Mean Squared Error — 2× in this paper
• Normalizing Flow — 2× in this paper · explained in 3 episodes
• Normalizing Flows — 2× in this paper · explained in 4 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Cherenkov Telescope Array Observatory — 1× in this paper
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Large-scale Monte Carlo — 1× in this paper
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Receiver Operating Characteristic — 1× in this paper
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Gammapy: A Python package for gamma-ray astronomy
• A Living Review of Machine Learning for Particle Physics
• CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
• Transferring GANs: generating images from limited data
• Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper
• Modeling hadronization using machine learning
• CTLearn: Deep Learning for Gamma-ray Astronomy
• Muon identification in a compact single-layered water Cherenkov detector and gamma/hadron discrimination using Machine Learning techniques
• Score-Based Generative Modeling through Stochastic Differential Equations
• Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
• Denoising Diffusion Implicit Models
• Generative Adversarial Networks
• Towards Principled Methods for Training Generative Adversarial Networks
• Wasserstein GAN
• Improved Training of Wasserstein GANs
• Deep Residual Learning for Image Recognition
• Attention Is All You Need
• Progressive Distillation for Fast Sampling of Diffusion Models
• Muon efficiency of the H.E.S.S. telescope
• Accelerating Diffusion Models via Early Stop of the Diffusion Process

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