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The rapid progress in image classification has been largely driven by the adoption of Graph Convolutional Networks (GCNs), which offer a robust framework for handling complex data structures. This study introduces a novel approach that…
Photomultipliers (PMTs) are currently adopted for the photodetector plane of Imaging Atmospheric Cherenkov Telescopes (IACTs). Even though PMT quantum efficiency has improved impressively in the recent years, one of the main limitation for…
The Cherenkov Telescope Array (CTA) is the next generation observatory for the study of very high-energy gamma rays from about 20 GeV up to 300 TeV. Thanks to the large effective area and field of view, the CTA observatory will be…
The Cherenkov Telescope Array (CTA) represents the next generation of ground-based instruments for very-high-energy (VHE) gamma-ray astronomy, aimed at improving on the sensitivity of current-generation experiments by an order of magnitude…
We show, by using an extensive sample of viable supersymmetric models as templates, that indirect detection of dark matter through gamma rays may have a large potential for identifying the nature of dark matter. This is in particular true…
Forthcoming imaging surveys will potentially increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of tens of millions of galaxies will have to be inspected to identify potential…
The Cherenkov Telescope Array (CTA) is a forthcoming ground-based observatory for very-high-energy gamma rays. CTA will consist of two arrays of imaging atmospheric Cherenkov telescopes in the Northern and Southern hemispheres, and will…
The classical method of determining the atomic structure of complex molecules by analyzing diffraction patterns is currently undergoing drastic developments. Modern techniques for producing extremely bright and coherent X-ray lasers allow a…
The measurement of $\gamma$-rays originating from active galactic nuclei offers the unique opportunity to study the propagation of very-high-energy photons over cosmological distances. Most prominently, $\gamma$-rays interact with the…
The Cherenkov Telescope Array (CTA) is a next generation ground-based very-high-energy gamma-ray observatory that will allow for observations in the >10 GeV range with unprecedented photon statistics and sensitivity. This will enable the…
This work utilizes a MobileNetV2 Convolutional Neural Network (CNN) for fast, mobile detection of satellites, and rejection of stars, in cluttered unresolved space imagery. First, a custom database is created using imagery from a synthetic…
The Cherenkov Telescope Array (CTA) will be the world's largest and most sensitive ground-based gamma-ray observatory in the energy range from a few tens of GeV to tens of TeV. The LST-1 prototype, currently in its commissioning phase, is…
Once completed, the Cherenkov Telescope Array (CTA) will be able to map the gamma-ray sky in a wide energy range from several tens of GeV to some hundreds of TeV and will be more sensitive than previous experiments by an order of magnitude.…
Current synoptic sky surveys monitor large areas of the sky to find variable and transient astronomical sources. As the number of detections per night at a single telescope easily exceeds several thousand, current detection pipelines make…
This research presents a machine-learning approach for tumor detection in medical images using convolutional neural networks (CNNs). The study focuses on preprocessing techniques to enhance image features relevant to tumor detection,…
Ground-based atmospheric Cherenkov telescopes are proven to be effective instruments for observations of very high energy (VHE) gamma-radiation from celestial objects. For effective use of such technique one needs detailed Monte Carlo…
This paper presents a new state-of-the-art for document image classification and retrieval, using features learned by deep convolutional neural networks (CNNs). In object and scene analysis, deep neural nets are capable of learning a…
The increasing volume of gamma-ray data demands new analysis approaches that can handle large-scale datasets while providing robustness for source detection. We present a Deep Learning (DL) based pipeline for detection, localization, and…
Land Cover (LC) image classification has become increasingly significant in understanding environmental changes, urban planning, and disaster management. However, traditional LC methods are often labor-intensive and prone to human error.…
Imaging Atmospheric Cherenkov Telescopes (IACTs) are ground-based instruments devoted to the study of very high energy gamma-rays coming from space. The detection technique consists of observing images created by the Cherenkov light emitted…