A machine learning method to separate cosmic ray electrons from protons from 10 to 100 GeV using DAMPE data

2018/03/18 by Hao Zhao, Zhao, Hao, Wen-Xi Peng +15 · 1 citation
Physics and Astronomy · #Astrophysics #Astrophysics and Cosmic Phenomena #Cosmic ray #Dark Matter and Cosmic Phenomena #Dark matter #Electron #Energy (signal processing) #FOS: Physical sciences #Instrumentation and Detectors (physics.ins-det) #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Nuclear physics #Observatory #Particle (ecology) #Particle Detector Development and Performance #Particle physics #Physics #Positron #Range (aeronautics) #astro-ph.IM #physics.ins-det

paper · pdf · doi:10.48550/arxiv.1803.06628

11 pages, 8 figures, accepted for publication in RAA. arXiv admin note: text overlap with arXiv:physics/0703039 by other authors

openalex publication_date 2018/03/18 · arxiv created 2018/06/21 · arxiv updated 2018/06/25 · openalex created_date 2022/10/03 · openalex updated_date 2026/08/05

Abstract

DArk Matter Particle Explorer (DAMPE) is a general purpose high energy cosmic ray and gamma ray observatory, aiming to detect high energy electrons and gammas in the energy range 5 GeV to 10 TeV and hundreds of TeV for nuclei. This paper provides a method using machine learning to identify electrons and separate them from gammas,protons,helium and heavy nuclei with the DAMPE data from 2016 January 1 to 2017 June 30, in energy range from 10 to 100 GeV.

Citations

• TMVA - Toolkit for Multivariate Data Analysis

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• A neural network classifier for electron identification on the DAMPE experiment

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