Deep Learning for Energy Estimation and Particle Identification in Gamma-ray Astronomy
Deep learning techniques, namely convolutional neural networks (CNN), have previously been adapted to select gamma-ray events in the TAIGA experiment, having achieved a good quality of selection as compared with the conventional Hillas approach. Another important task for the TAIGA data analysis was also solved with CNN: gamma-ray energy estimation showed some improvement in comparison with the conventional method based on the Hillas analysis. Furthermore, our software was completely redeveloped for the graphics processing unit (GPU), which led to significantly faster calculations in both of these tasks. All the results have been obtained with the simulated data of TAIGA Monte Carlo software; their experimental confirmation is envisaged for the near future.
Code (0)
등록된 구현이 없습니다.
Tasks
AstronomyGPUSimilar Papers 제목 키워드 기반
Particle identification in ground-based gamma-ray astronomy using convolutional neural networks
Modern detectors of cosmic gamma-rays are a special type of imaging telescopes (air Cherenkov telescopes) supplied with cameras with a relatively large number of photomultiplier-based pixels. For example, the camera of t…
AstronomyDeep LearningMachine Learning in Gamma Astronomy
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.
AstronomyDeep LearningAnalysis of the HiSCORE Simulated Events in TAIGA Experiment Using Convolutional Neural Networks
TAIGA is a hybrid observatory for gamma-ray astronomy at high energies in range from 10 TeV to several EeV. It consists of instruments such as TAIGA-IACT, TAIGA-HiSCORE, and others. TAIGA-HiSCORE, in particular, is an ar…
AstronomyProcessing Images from Multiple IACTs in the TAIGA Experiment with Convolutional Neural Networks
Extensive air showers created by high-energy particles interacting with the Earth atmosphere can be detected using imaging atmospheric Cherenkov telescopes (IACTs). The IACT images can be analyzed to distinguish between …
Developing a Machine Learning Algorithm-Based Classification Models for the Detection of High-Energy Gamma Particles
Cherenkov gamma telescope observes high energy gamma rays, taking advantage of the radiation emitted by charged particles produced inside the electromagnetic showers initiated by the gammas, and developing in the atmosph…