Related papers: Generation of Imaging Air Cherenkov Telescope imag…
Imaging Atmospheric Cherenkov Telescopes (IACTs) have resulted in a breakthrough in very-high energy (VHE) gamma-ray astrophysics. While early IACT installations faced the problem of detecting any sources at all, current instruments are…
Score-based diffusion models have significantly advanced generative deep learning for image processing. Measurement conditioned models have also been applied to inverse problems such as CT reconstruction. However, the conventional approach,…
In order to better utilize the information contained in the shower images generated by imaging Cherenkov telescopes (IACTs) equipped with cameras with small pixels, images are fit to a parametrization of image shapes gained from Monte Carlo…
Extensive air showers created by high-energy particles interacting with the Earth atmosphere can be detected using imaging atmospheric Cherenkov telescopes (IACTs). The IACT images can be analyzed to distinguish between the events caused by…
We have studied the distribution patterns of lateral density, arrival time and angular position of Cherenkov photons generated in Extensive Air Showers (EASs) initiated by $\gamma$-ray, proton and iron primaries incident with various…
Imaging atmospheric Cherenkov telescopes (IACTs) detect gamma rays by measuring the Cherenkov light emitted by secondary particles in the air shower when the gamma rays hit the atmosphere. At low energies, the limited amount of Cherenkov…
We present a high-performance event reconstruction algorithm: an Image Pixel-wise fit for Atmospheric Cherenkov Telescopes (ImPACT). The reconstruction algorithm is based around the likelihood fitting of camera pixel amplitudes to an…
In this paper, we address the problem of generative dataset distillation that utilizes generative models to synthesize images. The generator may produce any number of images under a preserved evaluation time. In this work, we leverage the…
Arrays of imaging atmospheric Cherenkov telescopes (IACT) are superb instruments to probe the very-high-energy gamma-ray sky. This type of telescope focuses the Cherenkov light emitted from air showers, initiated by very-high-energy gamma…
Diffusion models have achieved state-of-the-art results on many modalities including images, speech, and video. However, existing models are not tailored to support remote sensing data, which is widely used in important applications…
Stereoscopic arrays of Imaging Atmospheric Cherenkov Telescopes allow to reconstruct gamma-ray-induced showers in 3 dimensions, which offers several advantages: direct access to the shower parameters in space and straightforward…
Diffusion models have shown a great ability at bridging the performance gap between predictive and generative approaches for speech enhancement. We have shown that they may even outperform their predictive counterparts for non-additive…
Generative models, particularly diffusion models, have made significant success in data synthesis across various modalities, including images, videos, and 3D assets. However, current diffusion models are computationally intensive, often…
A simple 3D-reconstruction method for gamma-ray induced air showers is presented, which takes full advantage of the assets of a system of Atmospheric Cherenkov Telescopes combining stereoscopy and fine-grain imaging like the High Energy…
Generative modeling has drawn much attention in creative and scientific data generation tasks. Score-based Diffusion Models, a type of generative model that iteratively learns to denoise data, have shown state-of-the-art results on tasks…
The photon density on the ground is a fundamental quantity in all experiments based on Cherenkov light measurements, e.g. in the Imaging Air Cherenkov Telescopes (IACT). IACT's are commonly and successfully used in order to search and study…
We estimate the limiting angular resolution and detection area for an array of 3 large-aperture Imaging Atmospheric Cherenkov Telescopes. We consider an idealized IACT system in order to understand the limitations imposed by the intrinsic…
In this work, we present a conditional denoising-diffusion surrogate for electromagnetic calorimeter showers that is trained to generate high-fidelity energy-deposition maps conditioned on key detector and beam parameters. The model employs…
We present a new paradigm for the simulation of arrays of Imaging Atmospheric Cherenkov Telescopes (IACTs) which overcomes limitations of current approaches. Up to now, all major IACT experiments rely on the same Monte-Carlo simulation…
Generative AI, in general, and synthetic visual data generation, in specific, hold much promise for benefiting surgical training by providing photorealism to simulation environments. Current training methods primarily rely on reading…