Related papers: Gamma/Hadron Separation in Imaging Air Cherenkov T…
Imaging Atmospheric Cherenkov Telescopes (IACTs) of gamma ray observatory TAIGA detect the Extesnive Air Showers (EASs) originating from the cosmic or gamma rays interactions with the atmosphere. Thereby, telescopes obtain images of the…
Dark100 is a planned array of six telescopes, using the Panoramic Search for Extraterrestrial Intelligence (PANOSETI) telescope system. It will operate as an imaging atmospheric Cherenkov telescope array, with a telescope design and array…
In this work, we present a new, high performance algorithm for background rejection in imaging atmospheric Cherenkov telescopes. We build on the already popular machine-learning techniques used in gamma-ray astronomy by the application of…
This paper presents a comprehensive comparative survey of TensorFlow and PyTorch, the two leading deep learning frameworks, focusing on their usability, performance, and deployment trade-offs. We review each framework's programming paradigm…
Ground based gamma-ray observations with Imaging Atmospheric Cherenkov Telescopes (IACTs) play a significant role in the discovery of very high energy (E > 100 GeV) gamma-ray emitters. The analysis of IACT data demands a highly efficient…
Gamma-ray astronomy from hundreds of GeV to PeV is confined to ground-based experiments that detect air showers induced by $\gamma$-rays entering Earth's atmosphere. While particle detector arrays feature huge detection areas, accurately…
Background showers triggered by hadrons represent over 99.9% of all particles arriving at ground-based gamma-ray observatories. An important stage in the data analysis of these observatories, therefore, is the removal of hadron-triggered…
The efficiency of the $\gamma$/hadron separation worsens significantly at low energies for Imaging Air Cherenkov Telescopes (IACT). This observed effect was partially explained by the occurrence of a hardly reducible hadronic background…
The Cherenkov Telescope Array Observatory (CTAO) is the next generation of observatories employing the imaging air Cherenkov technique for the study of very high energy gamma rays. The deployment of deep learning methods for the…
Machine learning, through the use of convolutional and recurrent neural networks is a promising avenue for the improvement of background rejection performance in imaging atmospheric Cherenkov telescopes. However, it is of paramount…
The effective observation time of Imaging Air Cherenkov Telescopes (IACTs) plays an important role in the detection of gamma-ray sources, especially when the expected flux is low. This time is strongly limited by the atmospheric conditions.…
The development of Imaging Atmospheric Cherenkov Telescopes (IACTs) unveiled the sky in the teraelectronvolt regime, initiating the so-called "TeV revolution", at the beginning of the new millennium. This revolution was also facilitated by…
Imaging atmospheric Cherenkov telescopes (IACTs) are sensitive to rare gamma-ray photons, buried in the background of charged cosmic-ray (CR) particles, the flux of which is several orders of magnitude greater. The ability to separate gamma…
The simulation of atmospheric showers through Monte-Carlo processes as well as their projection into Imaging Atmospheric Cherenkov Telescopes (IACT) is long and very computing intensive. As these simulations are the most advanced ones from…
A new approach to Gamma/Hadron separation algorithms is proposed. The differences between Gamma and Hadron showers are notorious in two main aspects. The first is the wideness of the shower, and the second is the distribution of the angles…
The HEGRA system of Imaging Atmospheric Cherenkov Telescopes (IACTs) detects Cherenkov light produced by air showers. The concept of stereoscopic observation with the five HEGRA telescopes allows the reconstruction of various shower…
Identification of Cherenkov light generated by muons has been suggested as a promising way to dramatically improve the background rejection power of Imaging Atmospheric Cherenkov Telescope (IACT) arrays at high energies. However, muon…
We compared convolutional neural networks to the classical boosted decision trees for the separation of atmospheric particle showers generated by gamma rays from the particle-induced background. We conduct the comparison of the two…
TorchOptics is an open-source Python library for differentiable Fourier optics simulations, developed using PyTorch to enable GPU-accelerated tensor computations and automatic differentiation. It provides a comprehensive framework for…
We present tntorch, a tensor learning framework that supports multiple decompositions (including Candecomp/Parafac, Tucker, and Tensor Train) under a unified interface. With our library, the user can learn and handle low-rank tensors with…