Year: 2025
Type: conference-paper
Abstract: LHAASO-KM2A is a pivotal facility for studying cosmic rays through extensive air shower detection. However, accurately classifying cosmic ray components (e.g., protons, helium nuclei, and heavy nuclei) remains challenging due to overlapping shower signatures and background noise. In this proceeding, we proposes a deep learning-based method to enhance the classification accuracy of cosmic ray components using KM2A simulation data. Current results demonstrate that the proposed method achieves a higher classification accuracy compared to the conventional method.
DOI: 10.22323/1.501.0323
Authors: Hongkui Lv, Weiyan Zhang, Xiaopeng Zhang, Min Zha
Institutions: Institute of High Energy Physics, Hebei Normal University
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414448836
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