Generation of Imaging Air Cherenkov Telescope images using Diffusion Models
astro-ph.IM, astro-ph.HE
Submitted: 2026-03-06
Updated: 2026-09-01
Terminology
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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