2021/02/28 by David Droz, Andrii Tykhonov, A. Tykhonov +12 · 17 citations
Physics and Astronomy · #Artificial intelligence #Artificial neural network #Astrophysics #Astrophysics and Cosmic Phenomena #COSMIC cancer database #Computer science #Cosmic ray #Dark Matter and Cosmic Phenomena #Dark matter #Detector #Electron #Monte Carlo method #Nuclear physics #Observatory #Optics #Particle Detector Development and Performance #Particle identification #Particle physics #Physics #Positron #Proton #astro-ph.HE #astro-ph.IM #hep-ex #physics.ins-det
paper · pdf · doi:10.1088/1748-0221/16/07/p07036
published in Journal of Instrumentation 16(07), P07036 (Institute of Physics) · 19 pages, 12 figures, accepted for publication in Journal of Instrumentation (JINST)
openalex created_date 2021/02/15 · arxiv created 2021/05/11 · openalex publication_date 2021/07/01 · arxiv updated 2021/08/11 · openalex updated_date 2026/08/05
The Dark Matter Particle Explorer (DAMPE) is a space-borne particle detector and cosmic ray observatory in operation since 2015, designed to probe electrons and gamma rays from a few GeV to 10 TeV in energy, as well as cosmic protons and nuclei up to 100 TeV. Among the main scientific objectives is the precise measurement of the cosmic electron + positron flux, which, due to the very large proton background in orbit, requires a powerful particle identification method. In the past decade, the field of machine learning has provided us the needed tools. This paper presents a neural network based approach to cosmic electron identification and proton rejection and showcases its performance based on simulated Monte Carlo data. The neural network reaches significantly lower background than the classical, cut-based method for the same detection efficiency, especially at the highest energies probed by the detector. Good agreement between simulation and real data is demonstrated.