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

Hao Zhao; Wen-Xi Peng; Huan-Yu Wang; Rui Qiao; Dong-Ya Guo; Hong Xiao; Zhao-Min Wang

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

Instrumentation and Methods for Astrophysics 2018-06-25 v2 Instrumentation and Detectors

Authors: Hao Zhao , Wen-Xi Peng , Huan-Yu Wang , Rui Qiao , Dong-Ya Guo , Hong Xiao , Zhao-Min Wang

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.

Keywords

cosmic rays and extensive air showers dark matter detection precision measurements in physics

Cite

@article{arxiv.1803.06628,
  title  = {A machine learning method to separate cosmic ray electrons from protons from 10 to 100 GeV using DAMPE data},
  author = {Hao Zhao and Wen-Xi Peng and Huan-Yu Wang and Rui Qiao and Dong-Ya Guo and Hong Xiao and Zhao-Min Wang},
  journal= {arXiv preprint arXiv:1803.06628},
  year   = {2018}
}

Comments

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

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

Abstract

Keywords

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