Gamma-ray trajectory reconstruction using Deep Learning methods
Year: 2025
Type: conference-paper
Abstract: 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. Diffuse $\gamma$-ray analysis requires a precise tracking method in order to identify the incoming particle through the different detectors, as well as defining its origin in space. A deep-learning tool for track reconstruction has already been developed for electrons and ions. In this analysis we used this tool trained on electron samples on $\gamma$-ray samples to assess its efficiency on trajectory reconstruction up to 10~TeV. Preliminary results show very promising tracking precision 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 original track of simulated events.
DOI: 10.22323/1.501.0579
Authors: Hugo Valentin Boutin, "Andrii Tykhonov", "Xin Wu"
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414449312
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