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

TL;DR

This paper presents a machine learning approach to distinguish cosmic ray electrons from protons using DAMPE data within the 10 to 100 GeV energy range, enhancing particle identification in high-energy astrophysics.

Contribution

The paper introduces a novel machine learning method specifically designed to separate electrons from protons in DAMPE data, improving particle discrimination accuracy.

Findings

Effective separation of electrons and protons in the 10-100 GeV range.

Enhanced accuracy over traditional identification methods.

Potential for improved cosmic ray composition analysis.

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.