Periodic Reporting for period 3 - PeVSPACE (Direct Detection of TeV--PeV Cosmic Rays in Space)
Période du rapport: 2023-02-01 au 2024-07-31
The main objective of this project is to radically improve and optimize the techniques for Cosmic Ray detection at TeV—PeV energy region, including particle reconstruction, identification, and simulation, using a state-of-the-art Artificial Intelligence (AI) approach. As a result, Cosmic Ray spectra and composition will be measured first with the DArk Matte Particle Explorer (DAMPE) space mission and then subsequently with the next-generation space instrument — High Energy Radiation Detector (HERD), with unprecedented precision, which could not be achieved otherwise. There are two main innovations in the project. First, the application of AI techniques in astroparticle physics will be pioneered at the highest energies, in an unconventional use case. Second, the Monte-Carlo hadronic simulation models will be tested and tuned using the detector data, for the first time at such high energies.
A method of Cosmic Ray Electron (CRE) identification based on the Deep Neural Net (DNN) technique has been developed in cooperation with DAMPE colleagues. The method provides up to factor 4 improvement of proton background rejection compared to the standard methods and makes possible a precise CRE spectrum measurement up to unprecedented energy of about 10 TeV. The work is published in the Journal of Instrumentaion (2021). Consequently, a preliminary measurement of CRE spectrum using the developed method has been performed and is under internal review by the DAMPE collaboration. Further Research and Development of a Convolutional Neural Net (CNN) approach using the images of cosmic rays in the detector have been performed by the team members. In the current implementation, the CNN approach shows partial enhancement over the DNN method, likely reaching the theoretical performance limit for a given type of detector to which it was optimized (DAMPE). At the same time, it shows very high potential and serves as a stepping stone for research and development of the electron identification technique for the future HERD mission, using the unique 3D particle calorimeter images, to be held in the next phases of the project.
Work is in progress on repeating Cosmic Ray proton spectrum measurement with the latest DAMPE data, using different hadronic simulation models and the developed track reconstruction algorithm. The goal is to significantly improve the accuracy of the hadronic models, substantially reducing the related systematic uncertainty, which is a dominant factor limiting the accuracy of the Cosmic Ray proton spectrum measurement. A unique interface is being developed for the integration of the state-of-the-art hadronic simulation models from the Cosmic Ray Monte Carlo (CRMC) package into the detector simulation toolkit, Geant4, used both in DAMPE data analysis and HERD development and optimization. In particular, this work is at the core of the Cosmic Ray helium spectrum measurement performed by the DAMPE collaboration, published in Physical Review Letters journal (2021), appearing as an Editor's highlight.
Results of the project were presented in oral contributions at multiple international conferences, including EPS-HEP 2021 (online conference), CORSIKA 8 Air-Shower Simulation and Development Workshop 2022, Heidelberg, Germany, Connecting The Dots 2022, Princeton, USA, and TeV Particle Astrophysics (TeVPA) 2022, Kingston, Canada.
A Deep Learning solution is elaborated for the Cosmic Ray Electron identification, demonstrating significant advantage with respect to the existing techniques at the highest energies of direct Cosmic Ray Electron detection, opening now prospectives for high-energy Cosmic Ray Electron measurements, which could not be achieved without it. Further development in the course of the project, in particular, the application of deep learning to 3D particle images in space missions, which was never attempted before, demonstrates the clear potential of advancing the field of particle detection techniques beyond the state-of-the-art.
The project team is leading the development of a tool that solves a long-standing problem of particle simulation at energies beyond those of the Large Hadron Collider, with a significant impact and contribution to high-energy physics and astroparticle physics communities. The tool is part of the project research aimed at optimization and tuning of hadronic simulation models at the highest energies.