ENHANCING REAL-TIME SIGNAL PROCESSING IN HIGH-ENERGY PHYSICS EXPERIMENTS USING MACHINE LEARNING TECHNIQUES

Abstract

Real-time signal processing in high-energy physics (HEP) experiments presents unique challenges due to the high volume and complexity of data generated. The application of machine learning (ML) offers innovative solutions to these challenges, enabling enhanced signal detection, noise reduction, and overall data analysis efficiency. This review paper provides a comprehensive overview of current ML techniques applied to real-time signal processing within HEP, summarizes key methodologies and their practical applications, and identifies future research directions. By analyzing various ML models and their integration into real-time processing frameworks, this paper highlights the advancements and ongoing developments in improving data quality and experimental outcomes.