| Citation: | BAI C P，ZHANG S Y，ZHANG X，et al. Spaceborne particle identification platform and application based on convolutional neural network[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（4）：1313-1323 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0171
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Accurate identification of space radiation particles is crucial for both scientific research and engineering applications. Existing particle identification methods, including detector telescope methods, electrostatic analysis time-of-flight methods, time-of-flight energy methods, and waveform analysis energy methods, have achieved good results in practical applications. However, by leveraging the powerful feature extraction and classification capabilities of convolutional neural networks (CNN), it is expected to further enhance the precision of particle energy measurement and species identification. Based on common space environment detection payloads, this paper proposes a method to build an on-orbit CNN-based particle identification platform for particle species identification. The platform first constructs a multidimensional input dataset, with model training and weight extraction completed through software platforms, and waveform inference and dataset expansion carried out on the hardware platform. The established identification platform is used to train and test neutron and gamma waveform data obtained from actual tests, and the identification accuracy of both the software and hardware platforms is analyzed, completing the platform verification. The establishment and application of this identification platform provide a new approach and method for future particle measurement and identification in space environment detection, with significant engineering practical implications.
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