A deep learning method for the trajectory reconstruction of cosmic rays with the DAMPE mission
| Autor: | Andrii Tykhonov, Andrii Kotenko, Paul Coppin, Maksym Deliyergiyev, David Droz, Jennifer Maria Frieden, Chiara Perrina, Enzo Putti-Garcia, Arshia Ruina, Mikhail Stolpovskiy, Xin Wu |
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| Rok vydání: | 2022 |
| Předmět: |
instrumentation
High Energy Astrophysical Phenomena (astro-ph.HE) Physics - Instrumentation and Detectors Astrophysics::High Energy Astrophysical Phenomena pamela Astrophysics::Instrumentation and Methods for Astrophysics deep learning track reconstruction knee FOS: Physical sciences Astronomy and Astrophysics Instrumentation and Detectors (physics.ins-det) cosmic ray and gamma ray direct detection in space calibration large-area telescope cosmic rays nuclei calorimeter high-energy Astrophysics - Instrumentation and Methods for Astrophysics Astrophysics - High Energy Astrophysical Phenomena physics Instrumentation and Methods for Astrophysics (astro-ph.IM) performance proton |
| Zdroj: | Astroparticle Physics |
| DOI: | 10.48550/arxiv.2206.04532 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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