Design and Application of an Onboard Particle Identification Platform Based on Convolutional Neural Networks
| Autor: | Chaoping Bai, Xin Zhang, Shenyi Zhang, Yueqiang Sun, Xianguo Zhang, Ziting Wang, Shuai Zhang |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Applied Sciences, Vol 14, Iss 15, p 6628 (2024) |
| Druh dokumentu: | article |
| ISSN: | 14156628 2076-3417 |
| DOI: | 10.3390/app14156628 |
| Popis: | Space radiation particle detection plays a crucial role in scientific research and engineering practice, especially in particle species identification. Currently, commonly used in-orbit particle identification techniques include telescope methods, electrostatic analysis time of flight (ESA × TOF), time-of-flight energy (TOF × E), and pulse shape discrimination (PSD). However, these methods usually fail to utilize the full waveform information containing rich features, and their particle identification results may be affected by the random rise and fall of particle deposition and noise interference. In this study, a low-latency and lightweight onboard FPGA real-time particle identification platform based on full waveform information was developed utilizing the superior target classification, robustness, and generalization capabilities of convolutional neural networks (CNNs). The platform constructs diversified input datasets based on the physical features of waveforms and uses Optuna and Pytorch software architectures for model training. The hardware platform is responsible for the real-time inference of waveform data and the dynamic expansion of the dataset. The platform was utilized for deep learning training and the testing of the historical waveform data of neutron and gamma rays, and the inference time of a single waveform takes 4.9 microseconds, with an accuracy rate of over 97%. The classification expectation FOM (figure-of-merit) value of this CNN model is 133, which is better than the traditional pulse shape discrimination (PSD) algorithm’s FOM value of 0.8. The development of this platform not only improves the accuracy and efficiency of space particle discrimination but also provides an advanced tool for future space environment monitoring, which is of great value for engineering applications. |
| Databáze: | Directory of Open Access Journals |
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