Year: 2025
Type: conference-paper
Abstract: ALPAQUITA-AS, the prototype of ALPACA, has been operating in situ since April 2023. It consists of 97 plastic scintillators, representing a quarter of the full ALPACA surface detector array. High-energy $\gamma$-ray astronomy, which involves measuring air shower particles at ground level, faces the challenge of discriminating $\gamma$ rays from the dominant background of cosmic rays (CR). In this work, we propose a classifier based on Graph Neural Network (GNN) to distinguish between $\gamma$ rays and CR. Following the success of Convolutional Neural Networks (CNN) in many areas and the deep learning revolution it spurred, interest in extending the use of convolution layers operating on graphs has grown. In some applications, GNNs have a notable advantage over CNNs because in certain scenarios the information is represented with graph structures naturally or more effectively. The training and evaluation datasets were generated through Monte Carlo simulations, the results are reported.
DOI: 10.22323/1.501.0653
Author: Rocio Garcia Ginez
Institutions: Institute for Cosmic Ray Research, The University of Tokyo
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Awards: Challenges in Space Weather Prediction Using the Sun's Shadow in the Cosmic Rays, Measurement of cosmic-ray proton energy spectrum to test the standard model of galactic cosmic-ray origin, Gamma Ray Astronomy at Highest Energy in the Southern Hemisphere, G373756783, Novel Development of Highest Energy Gamma Ray Astronomy, Unravelling mystery of cosmic particle accelerator PeVatron by highest-energy gamma-ray observation, Origin of galactic cosmic rays revealed by observations of ultra-high cosmic gamma rays in the southern hemisphere, Demonstration of 100 TeV gamma-ray observation in the southern hemisphere
OpenAlex ID: W4414452017
FWCI:
Cites:
Cited by:
Related to: