Related papers: Particle identification in ground-based gamma-ray …
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…
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…
The purpose of this paper is to review the most popular deep learning methods used to analyze astroparticle data obtained with Imaging Atmospheric Cherenkov Telescopes and provide references to the original papers.
The Cherenkov Telescope Array is a next generation ground-based gamma-ray observatory de- signed to detect photons in the 20 GeV to 300 TeV energy range. With a sensitivity improvement of up to one order of magnitude on the entire energy…
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…
In this paper we have introduced a novel method for gamma hadron separation in Imaging Atmospheric Cherenkov Telescopes (IACT) using Quantum Machine Learning. IACTs captures images of Extensive Air Showers (EAS) produced from very high…
Gamma-rays provide a powerful insight into the non-thermal universe and perhaps a unique probe for new physics beyond the standard model. Current experiments are already giving results in the physics of acceleration of cosmic rays in…
The Cherenkov Telescope Array (CTA), the new-generation ground-based observatory for $\gamma$-ray astronomy, provides unique capabilities to address significant open questions in astrophysics, cosmology, and fundamental physics. We study…
Imaging atmospheric Cherenkov telescopes (IACTs) detect extended air showers (EASs) generated when very-high-energy (VHE) gamma rays or cosmic rays interact with the Earth's atmosphere. Cherenkov photons produced during an EAS are captured…
Deep convolutional neural networks (DCNs) are a promising machine learning technique to reconstruct events recorded by imaging atmospheric Cherenkov telescopes (IACTs), but require optimization to reach full performance. One of the most…
Imaging Atmospheric Cherenkov Telescopes (IACTs) detect very-high-energy gamma rays from ground level by capturing the Cherenkov light of the induced particle showers. Convolutional neural networks (CNNs) can be trained on IACT camera…
Very-high-energy (>100 GeV) gamma-ray astronomy is emerging as an important discipline in both high energy astrophysics and astro-particle physics. This field is currently dominated by Imaging Atmospheric-Cherenkov Telescopes (IACTs) and…
Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks allow a machine…
In this work we compare two open source machine learning libraries, PyTorch and TensorFlow, as software platforms for rejecting hadron background events detected by imaging air Cherenkov telescopes (IACTs). Monte Carlo simulation for the…
The ability to discover new transients via image differencing without direct human intervention is an important task in observational astronomy. For these kind of image classification problems, machine Learning techniques such as…
Ground-based gamma-ray astronomy has experienced a major breakthrough in the last decade thanks to the advent of new generation instruments such as H.E.S.S., MAGIC, Milagro and VERITAS. A large variety of cosmic particle accelerators has…
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…
The stereoscopic imaging atmospheric Cherenkov technique, developed in the 1980s and 1990s, is now used by a number of existing and planned gamma-ray observatories around the world. It provides the most sensitive view of the very high…
We present a deep learning, computer vision algorithm constructed for the purposes of identifying and classifying charged particles in camera image sensors. We apply our algorithm to data collected by the Distributed Electronic Cosmic-ray…
This paper reports on the use of a convolutional neural network (CNN) methodology to analyse fluorescence images of calcium-ion Coulomb crystals in the gas phase. A transfer-learning approach is adopted using the publicly available RESNET50…