## 5 Conclusions  
The research points of this paper mainly include the following aspects:  
1. Based on the starting point, in which the pulse waveforms generated by different particles deposited in the sensor are different, this paper constructs a complete software and hardware convolutional neural network particle identification platform, which includes data preprocessing, software CNN architecture, an FPGA forward inference platform, and a dataset expansion module. The platform can be widely applied to particle identification and can be extended on the basis of traditional methods.
2. With the help of Optuna + Pytorch architecture, a multi-dimensional hyperparameter search of the CNN model is carried out on the multi-angle enhanced waveform data of neutrons and gamma rays, and the optimal hyperparameter combinations are determined through several iterations. The software's CNN model architecture is obtained during this training process. The model is utilized to test the existing test datasets separately, and the test accuracy is better than 97%.
3. With the help of the software model, the weights and bias parameters are solidified in the form of fixed points on the FPGA platform, and the hardware forward inference function is realized by the parallelized accelerated computing architecture. In the actual test, the forward inference of a single-waveform data value takes 4.9 us, and the accuracy is better than 97%. Meanwhile, the FOM parameter is used to compare the results of CNN computation and PSD operation. The value of the FOM parameter of CNN is 133, which is better than that of the FOM parameter of PSD by 0.8, further proving the superiority of the classification ability of CNNs.  
In subsequent studies, we will expand the dataset types and quantities in more dimensions, such as using both time and frequency domains as inputs or expanding the theoretical waveform dataset by simulation and analysis. We will further improve the feature extraction capability of the model's architecture with respect to waveform data, such as adopting CNN + RNN, combining the network voting model and multi-task output model, or expanding the scope of the Optuna searching hyperparameter samples. We will further create combinations with vivado HLS or other AI chips to improve the efficiency and accuracy of building the FPGA accelerated inference platform while considering irradiation resistance. It is reasonable to believe that, with the continuous improvement in IC arithmetic power and resources, the application of deep learning in the field of particle identification will become increasingly mature, and its application in the field of space environment detection will gradually move to the foreground.  
Conceptualization, C.B., X.Z. ([PERSON]), S.Z. ([PERSON]), Y.S., and X.Z. ([PERSON]); funding acquisition, C.B.; investigation, C.B.; methodology, C.B. and X.Z. ([PERSON]); project administration, Y.S. and X.Z. ([PERSON]); resources, S.Z. ([PERSON]); software, C.B.; supervision, X.Z. ([PERSON]) and S.Z. ([PERSON]); validation, C.B., Z.W., and S.Z. ([PERSON]); visualization, C.B.; writing--original draft, C.B.; writing--review and editing, C.B., X.Z. ([PERSON]), and [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This project is supported by National Natural Science Foundation of China (grant no. 42204180).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.