A novel unsupervised learning for subatomic particle identification of J/ψ and upsilon

In high-energy physics experiments, the accurate differentiation of subatomic particles is crucial. However, traditional particle identification methods exhibit significant limitations when it comes to handling high-dimensional data, complex features and data labeling. Consequently, there is a pressing need for more advanced techniques to enhance identification accuracy. While many researchers have employed supervised learning algorithms in particle physics experiments, the label generation process is often time-consuming. Therefore, we opted to utilize unsupervised learning methods for direct classification of the collected data. This paper presents a novel unsupervised classification model, DeepSVD-GMM, which integrates singular value decomposition, deep neural networks and Gaussian mixture models. In the experimental results section, due to the lack of existing literature on the application of unsupervised learning in the classification of J/[Formula: see text] and Upsilon, we compared our proposed method with classical unsupervised classification models. The results indicate that our method significantly outperforms other models in terms of classification accuracy. This advancement provides new insights and tools for data analysis in high-energy physics experiments, showcasing the significant potential of advanced feature extraction and unsupervised classification techniques.