## 5 Conclusions and Discussion  
In this paper, we propose a CNN-LSTM based automatic classification network for multi-type with multi-energy charged particle identification, which extracts more discriminative local features from the waveforms and filters out noise using the CNN module, while also capturing long-term dependencies through the LSTM module to obtain global features with a stronger generalization capability. The experimental results demonstrate that the proposed classification network can efficiently identify multiple types of particles and effectively classify particles with unseen energy levels, showing a strong classification accuracy and robustness. This method can not only be applied to improve particle identification performance in complex and unknown space environments, but is also used in automated detection systems in radiation detection and protection fields, as well as in nuclear medicine areas such as radionuclide imaging to enhance treatment precision.  
Our approach also has limitations. For instance, our CNN-LSTM model requires 0.09G FLOPs for processing waveform samples, with the number of model parameters being 0.31M. Although a single Nvidia 1080 Ti GPU can efficiently process 3500 samples in parallel, the computational cost presents a challenge when deploying the model on remote devices, such as detection payloads in space environments. Future plans include achieving practical deployment by designing acceleration and lightweight algorithms on FPGA platforms.  
Future directions for optimization include:  
1. The proposed network can identify particles with unseen energy levels. Future work could further explore combining the energy level estimation task with the particle identification task. This could be achieved through the development of hybrid approaches that combine traditional physics-based models with deep-learning techniques.  
2. Deep-learning based methods require large datasets for training, but obtaining experimental datasets faces challenges such as high costs, long time cycles, and limited types and energy levels. In the future, simulation based on physical principles could be explored to generate various required datasets, allowing the creation of such datasets on a single PC.  
Figure 6: The 3D visualization of the feature vectors extracted from the particle samples by (**a**) SVM-ovr, (**b**) LSTM, and (**c**) CNN-LSTM networks.  
**Author Contributions:** Conceptualization, X.M. and X.Z.; methodology, X.M. and X.Z.; software, X.M. and C.B.; validation, X.M., C.B., Z.W. and S.Z.; formal analysis, X.M. and X.Z.; investigation, X.M., C.B. and Z.W.; resources, C.B.; data curation, X.M. and C.B.; writing--original draft preparation, X.M. and X.Z.; writing--review and editing, X.Z.; visualization, C.B., Z.W. and S.Z.; supervision, Y.S.; project administration, X.Z. and Y.S.; funding acquisition, C.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Convolutional Neural Network-based Algorithmic Recognition Algorithm for Particle Waveform Signals on Starboard FPGA Platforms, grant number 42204180.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors would like to thank National Space Science Center, Chinese Academy of Sciences and University of Chinese Academy of Sciences for their collaborative work in contributing to this study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.