Speaker
Description
The Dark Matter Particle Explorer experiment allows for $\gamma$-ray detection up to TeV energies, with an unprecedented energy resolution of about 1\%, which makes it a unique instrument for $\gamma$-ray physics at these energies. A deep-learning tool for track reconstruction has already been developed for electrons and ions. We used this tool on $\gamma$-ray samples to assess its efficiency on trajectory reconstruction up to 10 TeV. Preliminary results using a deep-learning model trained on electron samples and applied to $\gamma$-ray samples already show very promising results and bring the prospect of this new tool in high energy $\gamma$-ray study. The efficiency of the $\gamma$ selection by the deep-learning method is compared to the classical Kalman-filter-based techniques, and a preliminary source study based on deep-learning is shown.
| Collaboration(s) | DAMPE Collaboration |
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