A hybrid deep learning model for optimizing particle identification systems

This article presents a novel machine learning approach for enhancing particle identification (PID) systems in high-energy physics (HEP) experiments. The proposed method utilizes a hybrid model that combines a deep neural network (DNN) and a random forest regressor (RFR), leveraging their complementary strengths. This approach achieves robust performance, leading to significantly improved particle discrimination and cleaner data for physics analysis. Our evaluation demonstrates a marked increase in PID system precision, highlighting the model's potential to optimize PID tasks in complex high-energy physics settings. By improving identification efficiency and reducing misidentification rates, this hybrid deep learning model offers valuable advancements for the field of particle physics.