OPTIMIZING HIGH-ENERGY PHYSICS DATA ANALYSIS WITH MACHINE LEARNING: PROCESSING AND ANOMALY DETECTION

Abstract

In high-energy physics (HEP) experiments, the sheer volume and complexity of data pose significant challenges for real-time analysis and anomaly detection. Machine learning (ML) techniques offer transformative solutions to these challenges, promising enhanced processing efficiency and more accurate anomaly identification. This manuscript presents a comprehensive review of the application of ML in optimizing data analysis within HEP. We explore various ML methodologies, including supervised and unsupervised learning models, deep learning, and ensemble techniques, highlighting their roles in improving data quality and experimental outcomes. The paper also discusses advanced algorithms for real-time data processing and anomaly detection, assessing their effectiveness in handling large-scale HEP datasets. By evaluating current ML models and their integration into existing data processing frameworks, this review identifies key advancements, ongoing challenges, and future research directions in the field. The insights provided aim to contribute to the advancement of data analysis practices in HEP, facilitating more precise and efficient experimental results.