## 4 Experimental Results and Analysis
### Comparison Experiments  
In this section, we evaluate our proposed CNN-LSTM network with existing state-of-the-art methods. In addition to testing on multi-type particles with multi-energy scenario. We introduce a more difficult scenario that is more likely to occur in practical situations. Specifically, the energy levels of the test particles' waveforms are unseen during the training phase (i.e., test on particles with unseen energy level scenario). \begin{table}
\begin{tabular}{l l l l l l l l l l l l l l l l l l} \hline \hline \multirow{3}{*}{**Dataset**} & \multirow{3}{*}{**Data Length**} & \multicolumn{10}{c}{**Waveforms**} \\ \cline{3-14}  & & & \multicolumn{3}{c}{**Particle Type**} & \multicolumn{1}{c}{**H**} & \multicolumn{3}{c}{**B**} & \multicolumn{3}{c}{**C**} & \multicolumn{3}{c}{**O**} & \multicolumn{3}{c}{**AI**} & \multicolumn{3}{c}{**Total Number**} \\ \cline{3-14}  & & & **Energy (MeV)** & **1.5** & **2** & **2.5** & **2.5** & **3.75** & **5** & **2.5** & **3.75** & **5** & **2.5** & **3.75** & **5** & **2.5** & **3.75** & **5** \\ \hline Experimental Dataset (EXP Dataset) & 120 ns (600 points) & & 100 & 101 & 102 & 101 & 100 & 101 & 101 & 100 & 100 & 100 & 101 & 88 & 100 & 100 & 1495 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The overview of experimental dataset. The comparison methods include the handcrafted-based \(m_{2}\), \(m_{2}-m_{3}\)[8] methods, as well as learning-based methods such as ANN [22], one-vs-rest SVM (SVM-ovr, a multi-class support vector machine method), RNN [11], and LSTM [13]. **Test on multi-type particles with multi-energy scenario**. In this scenario, for each type of particle with each energy levels in the EXP dataset, we partition 80% of the waveforms for training and 20% for test. The experimental results are shown in Table 2. It can be seen that our CNN-LSTM method achieves the most outstanding performance, with significant improvements in ACC and _Avg_FOM_ metrics compared with previous methods. Besides, the _Avg_FOM_ metric reaches 0.90, indicating that our method can extract discriminative feature vectors for multi-class classification, effectively distinguishing between each pair of particle types. The comparison of the confusion matrices for SVM, LSTM, and CNN-LSTM in Figure 5 further demonstrates that our method significantly enhances the classification accuracy for each particle. This is because the CNN module is better at capturing discriminative local features and filtering out environmental noise, while the LSTM module can process sequential information and extract more comprehensive and generalizable global feature. Thus, our CNN-LSTM method can effectively classify waveforms with similar atomic numbers and energy levels. We also evaluate the Precision, Recall, and F1 score metrics for each network, and extract the confidence intervals through five-fold cross-validation. The detailed results are presented in Tables A1 and A2 of Appendix A, where our network also achieves the best performance, which further validates the effectiveness and robustness of our network. **Test on particles with unseen energy level scenario**. In practical situations, the ability to distinguish particles with unseen energy levels is a crucial capability. To simulate this scenario, we re-partitioned the EXP dataset, using waveforms from two energy levels of each type of particle as the training set and the waveform from the remaining energy level as the test set. The evaluation results are presented in Table 3. Experimental results