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

Abstract

A deep learning method for the particle trajectory reconstruction with the DAMPE experiment is presented. The developed algorithms constitute the first fully machine-learned track reconstruction pipeline for space astroparticle missions. Significant performance improvements over the standard hand-engineered algorithms are demonstrated. Thanks to the better accuracy, the developed algorithms facilitate the identification of the particle absolute charge with the tracker in the entire energy range, opening a door to the measurements of cosmic-ray proton and helium spectra at extreme energies, towards the PeV scale, hardly achievable with the standard track reconstruction methods. In addition, the developed approach demonstrates an unprecedented accuracy in the particle direction reconstruction with the calorimeter at high deposited energies, above several hundred GeV for hadronic showers and above a few tens of GeV for electromagnetic showers.

References in corpus (13)

• Direct detection of a break in the teraelectronvolt cosmic-ray spectrum of electrons and positrons
• Discrepant hardening observed in cosmic-ray elemental spectra
• The DArk Matter Particle Explorer mission
• Classification without labels: Learning from mixed samples in high energy physics
• Measurement of the cosmic-ray proton spectrum from 40 GeV to 100 TeV with the DAMPE satellite
• Potential PeVatron supernova remnant G106.3+2.7 seen in the highest-energy gamma rays
• The Hunt for Pevatrons: The Case of Supernova Remnants
• HAWC J2227+610 and its association with G106.3+2.7, a new potential Galactic PeVatron
• Progress in high-energy cosmic ray physics
• Calibration and performance of the neutron detector onboard of the DAMPE mission
• Correction Method for the Readout Saturation of the DAMPE Calorimeter
• The CALorimetric Electron Telescope (CALET) on the International Space Station: Results from the First Two Years On Orbit
• Machine learning-based method of calorimeter saturation correction for helium flux analysis with DAMPE experiment

Cited by in corpus (3)

• Hadronic cross section measurements with the DAMPE space mission using 20GeV-10TeV cosmic-ray protons and ${}^4$He
• Machine-learning correction for the calorimeter saturation of cosmic-ray ions with the Dark Matter Particle Explorer: towards the PeV scale
• Energy Reconstruction of Non-fiducial Electron-Positron Events in the DAMPE Experiment Using Convolutional Neural Networks