Machine learning-based method of calorimeter saturation correction for helium flux analysis with DAMPE experiment

Abstract

DAMPE is a space-borne experiment for the measurement of the cosmic-ray fluxes at energies up to around 100 TeV per nucleon. At energies above several tens of TeV, the electronics of DAMPE calorimeter would saturate, leaving certain bars with no energy recorded. In the present work we discuss the application of machine learning techniques for the treatment of DAMPE data, to compensate the calorimeter energy lost by saturation.

References in corpus (6)

• Direct detection of a break in the teraelectronvolt cosmic-ray spectrum of electrons and positrons
• The DArk Matter Particle Explorer mission
• Measurement of the cosmic-ray proton spectrum from 40 GeV to 100 TeV with the DAMPE satellite
• The on-orbit calibration of DArk Matter Particle Explorer
• Calibration and performance of the neutron detector onboard of the DAMPE mission
• Correction Method for the Readout Saturation of the DAMPE Calorimeter

Cited by in corpus (1)

• A deep learning method for the trajectory reconstruction of cosmic rays with the DAMPE mission