###### Abstract  
Particle identification is a critical component of space environment detection. In this study, we propose a CNN-LSTM based multi-class automatic particle classification network, which employs 1-D CNN to extract more discriminative local features of waveforms produced by particle detector, and utilizes the ability of LSTM to capture long-range dependencies for extracting global features with stronger generalization capability. The proposed network can effectively classify the waveforms from multi-type with multi-energy charged particles, achieving an accuracy of 91.72% in simultaneously classifying H, B, C, O, and Al ions across three distinct energy levels. Furthermore, the proposed network achieves an accuracy of 73.60% in classifying particles with unseen energy levels during training, significantly surpassing the accuracy of previous methods. Extensive analysis experiments demonstrate the robustness and effectiveness of the proposed network. particle identification; pulse shape discrimination; particle detectors; deep neural network +
Footnote †: journal: Applied sciences  
Article  
nonlinear transformations. [PERSON] et al. [11] utilized fully connected neural network (Fc-NN) and recurrent neural network (RNN) to identify neutron and gamma-ray pulse shapes and to recover overlapping waveforms. Building on this, [PERSON] et al. [12] further implemented direct pile-up pulse height estimation using multitasking convolutional neural networks (CNNs) and multi-layer perceptron networks (MLPs). [PERSON] et al. [13] introduced Long Short-Term Memory (LSTM) networks for nuclear pulse parameter estimation of overlapping Gaussian pulses. [PERSON] et al. [14] utilized simulation data generated by Geant4 to assist in training CNN, successfully achieving high-precision identification of protons and electrons. [PERSON] et al. [15] introduced the self-organizing feature map (SOM), a clustering neural network, to categorize pulse signals from a phoswich detector. In practical applications, similar types and energies of charged particles increase waveform similarity, thus, complicating classification tasks. Figure 1 illustrates a subset of waveforms collected using the acquisition system described in Section 3.2. In Figure 1, the waveforms of five ion types (H, B, C, O, and Al) at 2.5 MeV are presented. The waveform similarity between different particles is relatively high, particularly between B and C ions, which have adjacent atomic numbers. Figure 1b shows the waveforms of B ions at 2.5, 3.75, and 5 MeV. In this case, the waveform similarity is lower, even with significant amplitude differences. The higher inter-type similarity, compared with the lower intra-type similarity, presents a significant challenge for particle identification. Furthermore, at lower particle energies, environmental noise significantly impacts the waveform, requiring more robust denoising capabilities in feature extraction methods to capture meaningful information. To address this issue, we propose a CNN-LSTM based automatic classification network for multi-type with multi-energy charged particle identification. Initially, we employ a One-Dimensional Convolutional Neural Network (1D-CNN) to extract more discriminative local features from the input waveforms and filter out environmental noise. The processed features are subsequently input into a LSTM module to capture long-term dependencies, thereby extracting more generalized global features. This approach enhances the model's ability to analyze the temporal characteristics of the waveforms. Ultimately, the extracted features are passed to the classifier for final classification. In contrast to existing methods that primarily perform binary classification, our network leverages the CNN-LSTM architecture to extract more discriminative features and employs a one-vs-rest combinational classifier to identify multiple types of particles with multiple energy levels from a single network. This capability allows the network to handle more complex real-world scenarios.