An Unsupervised Machine Learning Method for Electron--Proton Discrimination of the DAMPE Experiment

TL;DR

This paper introduces an unsupervised machine learning approach for discriminating electrons and positrons from protons in the DAMPE cosmic ray experiment, reducing reliance on simulations and improving background rejection across multiple energy ranges.

Contribution

The work presents a novel unsupervised learning method that outperforms traditional morphological techniques and matches supervised methods in background rejection for cosmic ray particle identification.

Findings

Residual background fractions are below 1% in lower energy ranges.

Background rejection power exceeds 10^4 across all tested energy ranges.

Method reduces uncertainties associated with simulation-based training.

Abstract

Galactic cosmic rays are mostly made up of energetic nuclei, with less than $1\%$ of electrons (and positrons). Precise measurement of the electron and positron component requires a very efficient method to reject the nuclei background, mainly protons. In this work, we develop an unsupervised machine learning method to identify electrons and positrons from cosmic ray protons for the Dark Matter Particle Explorer (DAMPE) experiment. Compared with the supervised learning method used in the DAMPE experiment, this unsupervised method relies solely on real data except for the background estimation process. As a result, it could effectively reduce the uncertainties from simulations. For three energy ranges of electrons and positrons, 80--128 GeV, 350--700 GeV, and 2--5 TeV, the residual background fractions in the electron sample are found to be about (0.45 $\pm$ 0.02)$\%$, (0.52 $\pm$ 0.04)$\%$, and (10.55 $\pm$ 1.80)$\%$, and the background rejection power is about (6.21 $\pm$ 0.03) $\times$ $10^4$, (9.03 $\pm$ 0.05) $\times$ $10^4$, and (3.06 $\pm$ 0.32) $\times$ $10^4$, respectively. This method gives a higher background rejection power in all energy ranges than the traditional morphological parameterization method and reaches comparable background rejection performance compared with supervised machine learning~methods.