AI-Powered Analysis for Hadronic Particle Detection in ALICE Open Data

The integration of Artificial Intelligence (AI) techniques, particularly Machine Learning (ML), is revolutionizing data analysis in high-energy physics experiments. This study presents the development and implementation of an ad-vanced AI-driven analysis environment for hadronic particle detection using open data from the ALICE experiment at CERN. The research focuses on lever-aging ML algorithms, including Deep Learning, ensemble methods, anomaly de-tection, to enhance particle classification and identification. The methodology involves preprocessing and analyzing data from lead-lead (Pb-Pb) collisions, ap-plying supervised and unsupervised ML techniques to optimize particle detection accuracy. The study explores the use of neural networks for particle classification and prediction, aiming to improve the identification of subatomic interactions in extreme conditions. Additionally, real-time inference through Kafka integration enhances data processing efficiency. Results demonstrate that AI-based ap-proaches significantly improve particle classification performance compared to traditional methods. The findings highlight the potential of ML to refine experi-mental analysis, reduce uncertainties, and provide new insights into fundamental interactions in particle physics. This research underscores the transformative role of AI in modern high-energy physics, paving the way for future advancements in data-driven scientific discovery.