A hybrid deep learning model for optimizing particle identification systems

**Particle Identification (PID)** is crucial for high-energy physics (**HEP**) experiments, allowing for accurate classification of particles produced in a HEP event. Effective PID is vital for understanding fundamental physics. However, precise PID is challenging due to complex detector responses, overlapping particle signatures, and the need for efficient data processing. This presentation introduces a hybrid deep learning model specifically designed for optimizing PID systems, combining the strengths of Neural Networks (**NN**) and Random Forest (**RF**) algorithms. The traditional likelihood approach for charged particle PID, while valuable, faces limitations due to discrepancies between modeled and actual data, statistical fluctuations, and computational demands. To overcome these challenges, advanced machine learning techniques are being explored to enhance PID accuracy. A previous study, **"Optimization of the PID Algorithms at the Belle II Experiment"**, utilized **Deep Neural Networks (DNN)** and **Random Forest Regressors (RFR)** separately to optimize the PID system specifically for the Belle II Experiment. Although these models achieved promising results individually, each has its limitations: DNNs excel at capturing complex relationships but can be sensitive to noise, while RFs are robust but may struggle with intricate patterns. This presentation overviews the recent paper that combines these two models into a robust **hybrid model** using Monte Carlo simulations for a conventional HEP experiment inspired by the Belle II experiment. This approach enhances the PID system’s generalization capabilities, making it adaptable to various experimental conditions. The hybrid model leverages DNNs for feature extraction and adaptability and RFs for robustness and interpretability. This combination improves predictive performance, enhances generalization, and provides valuable insights into feature importance, resulting in a powerful and efficient PID system.