Related papers: Gamma/hadron segregation for a ground based imagin…
The goal of this work is to investigate the possibility of improving current gamma/hadron discrimination based on their shower patterns recorded on the ground. To this end we propose the use of Convolutional Neural Networks (CNNs) for their…
Random forest (RF) stands out as a highly favored machine learning approach for classification problems. The effectiveness of RF hinges on two key factors: the accuracy of individual trees and the diversity among them. In this study, we…
The Cherenkov Telescope Array (CTA) will be the next generation gamma-ray observatory and will be the major global instrument for very-high-energy astronomy over the next decade, offering 5 - 10 x better flux sensitivity than current…
In atmospheric \v{C}erenkov technique $\gamma-$rays are detected against abundant background produced by hadronic showers. In order to improve signal to noise ratio of the experiment, it is necessary to reject a significant fraction of…
Water Cherenkov detectors offer a robust and economical solution for real-time radiation monitoring by detecting Cherenkov light from charged particles moving faster than light in water. This work presents a novel two-stage classification…
Wide-angle, non-imaging air Cherenkov detectors provide a way to observe cosmic gamma-rays which is complementary to observations by imaging Cherenkov telescopes. Their particular strength lies in the multi-TeV to ultra high energy range (E…
The Imaging Atmospheric Cherenkov technique allows to detect very high energy gamma rays from few tens of GeV to hundreds of TeV using ground-based instrumentation. At these energies a gamma ray generates a shower of secondary particles…
The next generation of observatories will facilitate the discovery of new types of astrophysical transients. The detection of such phenomena, whose characteristics are presently poorly constrained, will hinge on the ability to perform blind…
Gamma/hadron discrimination in ground-based gamma-ray observatories at the sub-TeV energy range is challenging as traditional muon-based methods become less effective at lower energies. This work explores a novel gamma/hadron discrimination…
This research note concerns the application of deep-learning-based multi-view-imaging techniques to data from the H.E.S.S. Imaging Atmospheric Cherenkov Telescope array. We find that the earlier the fusion of layer information from…
Random Forest (Breiman, 2001) is a successful and widely used regression and classification algorithm. Part of its appeal and reason for its versatility is its (implicit) construction of a kernel-type weighting function on training data,…
The operation of a small-size Cherenkov gamma-ray telescope TAIGA-IACT with camera on SiPMs OnSemi MicroFJ-60035 has been modelled by multiparticle Monte Carlo (MC) methods. The model implies that telescope camera is equipped with two…
The upcoming Cherenkov Telescope Array (CTA) project is expected to provide unprecedented sensitivity in the low-energy ( <~100 GeV) range for Cherenkov telescopes. In order to exploit fully the potential of the telescopes the standard…
In the atmospheric Cerenkov technique gamma rays are detected against the abundant background produced by hadronic showers. In order to improve the signal to noise ratio of the experiment, it is necessary to reject a significant fraction of…
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…
Multi-dimensional data classification is an important and challenging problem in many astro-particle experiments. Neural networks have proved to be versatile and robust in multi-dimensional data classification. In this article we shall…
The use of machine learning is becoming ubiquitous in astronomy, but remains rare in the study of the atmospheres of exoplanets. Given the spectrum of an exoplanetary atmosphere, a multi-parameter space is swept through in real time to find…
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…
Over the last decade, the Imaging Air Cerenkov technique has proven itself to be an extremely powerful means to study very energetic gamma-radiation from a number of astrophysical sources in a regime which is not practically accessible to…
The Cherenkov Telescope Array (CTA) will be the next generation ground-based observatory for very-high-energy (VHE) gamma-ray astronomy, with the deployment of tens of highly sensitive and fast-reacting Cherenkov telescopes. It will cover a…