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 (49)

• Adam: A Method for Stochastic Optimization
• The Large Area Telescope on the Fermi Gamma-ray Space Telescope Mission
• Machine learning and the physical sciences
• EPOS LHC : test of collective hadronization with LHC data
• PAMELA Measurements of Cosmic-ray Proton and Helium Spectra
• Direct detection of a break in the teraelectronvolt cosmic-ray spectrum of electrons and positrons
• CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
• Discrepant hardening observed in cosmic-ray elemental spectra
• Energy Spectra of Abundant Nuclei of Primary Cosmic Rays from the Data of ATIC-2 Experiment: Final Results
• 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
• Proton and Helium Spectra from the CREAM-III Flight
• A Convolutional Neural Network Neutrino Event Classifier
• Direct Measurement of the Cosmic-Ray Proton Spectrum from 50 GeV to 10 TeV with the Calorimetric Electron Telescope on the International Space Station
• Pass 8: Toward the Full Realization of the Fermi-LAT Scientific Potential
• Measurement of the cosmic ray helium energy spectrum from 70 GeV to 80 TeV with the DAMPE space mission
• Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
• Dijet resonance search with weak supervision using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector
• A new universal cosmic-ray knee near the magnetic rigidity 10 TV with the NUCLEON space observatory
• Fermi-LAT improved Pass~8 event selection
• Potential PeVatron supernova remnant G106.3+2.7 seen in the highest-energy gamma rays
• The Plastic Scintillator Detector at DAMPE
• Determination of the Point-Spread Function for the Fermi Large Area Telescope from On-orbit Data and Limits on Pair Halos of Active Galactic Nuclei
• The calibration and electron energy reconstruction of the BGO ECAL of the DAMPE detector
• 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
• A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory
• Progress in high-energy cosmic ray physics
• First results of the cosmic ray NUCLEON experiment
• Graph Neural Networks for Particle Tracking and Reconstruction
• Charge Measurement of Cosmic Ray Nuclei with the Plastic Scintillator Detector of DAMPE
• Cosmic-Ray Hardenings in the Light of AMS-02
• Internal alignment and position resolution of the silicon tracker of DAMPE determined with orbit data
• Pulsar Wind Nebulae inside Supernova Remnants as Cosmic-Ray PeVatrons
• Calibration and performance of the neutron detector onboard of the DAMPE mission
• Deep-Learning based Reconstruction of the Shower Maximum $X_{\max }$ using the Water-Cherenkov Detectors of the Pierre Auger Observatory
• Classification of Fermi-LAT sources with deep learning using energy and time spectra
• Calibration of the DAMPE Plastic Scintillator Detector and its on-orbit performance
• A Method of Alignment of the Plastic Scintillator Detector of DAMPE
• In-flight performance of the DAMPE silicon tracker
• Deep learning techniques applied to the physics of extensive air showers
• DAMPE silicon tracker on-board data compression algorithm
• Correction Method for the Readout Saturation of the DAMPE Calorimeter
• A neural network classifier for electron identification on the DAMPE experiment
• The CALorimetric Electron Telescope (CALET) on the International Space Station: Results from the First Two Years On Orbit
• Fast convolutional neural networks for identifying long-lived particles in a high-granularity calorimeter
• Machine learning-based method of calorimeter saturation correction for helium flux analysis with DAMPE experiment
• Energy reconstruction in a liquid argon calorimeter cell using convolutional neural networks

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