Related papers: Background Rejection in Atmospheric Cherenkov Tele…

The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to study their use for classifying image data obtained in Particle Physics experiments. Here, we discuss our efforts to apply CNNs to 2D and 3D…

In 2012, the High Energy Stereoscopic System (H.E.S.S.) was expanded by a fifth telescope (CT5). With an effective mirror diameter of 28m, CT5 is able to detect the Cherenkov light of very faint gamma-ray air showers, thereby significantly…

Many machine learning methods operate by inverting a neural network at inference time, which has become a popular technique for solving inverse problems in computer vision, robotics, and graphics. However, these methods often involve…

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…

A significant source of noise for Imaging Atmospheric Cherenkov Telescopes (IACTs), which are designed to measure air showers caused by astrophysical gamma rays, is optical light emitted from the night sky. This Night Sky Background (NSB)…

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…

High-energy gamma-ray spectroscopy is crucial for studying and advancing the application of high-energy photons in areas like strong-field physics, high-energy-density science, and laboratory astrophysics. However, high-energy gamma-ray…

A new method of shower-image analysis is presented which appears very powerful as applied to those Cherenkov Imaging Telescopes with very high definition imaging capability. It provides hadron rejection on the basis of a single cut on the…

Ground-based Imaging Cherenkov Telescope Arrays observe the Cherenkov radiation emitted in extended atmospheric showers generated by cosmic gamma rays in the TeV regime. The rate of these events is normally overwhelmed by 2-3 orders of…

The sensitivity of astronomical X-ray detectors is limited by the instrumental background. The background is especially important when observing low surface brightness sources that are critical for many of the science cases targeted by…

Change detection is one of the central problems in earth observation and was extensively investigated over recent decades. In this paper, we propose a novel recurrent convolutional neural network (ReCNN) architecture, which is trained to…

In gamma ray astronomy with Cherenkov telescopes, machine learning models are needed to guess what kind of particles generated the detected light, and their energies and directions. The focus in this work is on the classification task,…

Nuclear reactors produce a high flux of MeV-scale antineutrinos that can be observed through inverse beta-decay (IBD) interactions in particle detectors. Reliable detection of reactor IBD signals depends on suppression of backgrounds, both…

In this work, we present a novel background subtraction system that uses a deep Convolutional Neural Network (CNN) to perform the segmentation. With this approach, feature engineering and parameter tuning become unnecessary since the…

Telescopes, designed with semi-conductor based photo sensors, have the potential to detect Cherenkov or fluorescence light emitted by cosmic-rays in the atmosphere. Such telescopes promise a high duty cycle and efficiency in remote harsh…

Dark matter cannot be observed directly, but its weak gravitational lensing slightly distorts the apparent shapes of background galaxies, making weak lensing one of the most promising probes of cosmology. Several observational studies have…

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…

Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high energy physics. In this paper, we attempt to understand the potential of CNNs for event…

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 investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The…