Summary
The DArk Matter Particle Explorer (DAMPE) mission has recently marked a new epoch in astroparticle physics, extending the direct measurements of cosmic ray spectra beyond a TeV with unprecedented energy resolution. With this project, based on my leadership position in DAMPE and its unique data, I propose to fundamentally improve the precision of direct cosmic ray measurements at the highest energies – in the TeV–PeV range, using for the first time a state-of-the-art artificial intelligence approach. The project will help to solve the century-long problem of cosmic-ray origin at such high energies and its effects on the Universe composition. It will study the cosmic-ray spectrum close to the region of a mysterious decline, so-called “knee”, and shed light on the nature of Dark Matter through the discovery of characteristic fine structures in cosmic-ray and gamma-ray spectra. To achieve this, based on my expertise I propose: i) to develop the TeV–PeV cosmic-ray track reconstruction and identification techniques, using a deep learning or similar artificial intelligence approach; ii) to set up a unique research programme to iteratively improve the precision of hadronic Monte-Carlo models in this rarely explored energy domain, based on the available DAMPE data and data from future experiments. The developed results will be applied to the processing of DAMPE data at the first stage, and will be then extended to the next generation High Energy Cosmic Radiation Detection (HERD) experiment. The research strategy is designed to reduce drastically the dominant uncertainties of the cosmic-ray measurements in space, related to the particle type/direction identification and modeling of hadronic interactions in the detector. As a result of the project, cosmic ray spectra will be directly measured in space in TeV–PeV energy range with qualitatively higher precision, opening up an unprecedented opportunities for new discoveries.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/851103 |
| Start date: | 01-02-2020 |
| End date: | 30-11-2025 |
| Total budget - Public funding: | 1 499 097,00 Euro - 1 499 097,00 Euro |
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Original description
The DArk Matter Particle Explorer (DAMPE) mission has recently marked a new epoch in astroparticle physics, extending the direct measurements of cosmic ray spectra beyond a TeV with unprecedented energy resolution. With this project, based on my leadership position in DAMPE and its unique data, I propose to fundamentally improve the precision of direct cosmic ray measurements at the highest energies – in the TeV–PeV range, using for the first time a state-of-the-art artificial intelligence approach. The project will help to solve the century-long problem of cosmic-ray origin at such high energies and its effects on the Universe composition. It will study the cosmic-ray spectrum close to the region of a mysterious decline, so-called “knee”, and shed light on the nature of Dark Matter through the discovery of characteristic fine structures in cosmic-ray and gamma-ray spectra. To achieve this, based on my expertise I propose: i) to develop the TeV–PeV cosmic-ray track reconstruction and identification techniques, using a deep learning or similar artificial intelligence approach; ii) to set up a unique research programme to iteratively improve the precision of hadronic Monte-Carlo models in this rarely explored energy domain, based on the available DAMPE data and data from future experiments. The developed results will be applied to the processing of DAMPE data at the first stage, and will be then extended to the next generation High Energy Cosmic Radiation Detection (HERD) experiment. The research strategy is designed to reduce drastically the dominant uncertainties of the cosmic-ray measurements in space, related to the particle type/direction identification and modeling of hadronic interactions in the detector. As a result of the project, cosmic ray spectra will be directly measured in space in TeV–PeV energy range with qualitatively higher precision, opening up an unprecedented opportunities for new discoveries.Status
SIGNEDCall topic
ERC-2019-STGUpdate Date
27-04-2024
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