AI-AUGMENTED IOT SYSTEMS FOR PRECISION MEASUREMENT AND DATA MANAGEMENT IN HIGH-ENERGY PHYSICS

Abstract

This study investigates the use of Artificial Intelligence (AI)-augmented Internet of Things (IoT) systems to improve precision measurement and data management within high-energy physics (HEP) laboratories. HEP experiments are characterized by high data volumes and demand for accuracy, necessitating advanced solutions for real-time data handling. Despite the potential of AI-integrated IoT for such environments, their efficiency, adoption, and suitability remain underexplored. This research employed a quantitative approach, surveying 500 HEP lab employees to assess the efficacy and user satisfaction of these systems. Results indicated that 86% of respondents utilized AI-IoT systems for data processing, often handling over 10 GB per experiment. However, satisfaction levels were neutral, with a mode of 3, and a t-test showed no significant difference from the hypothesized mean satisfaction score. Reliability analysis revealed low internal consistency (Cronbach’s alpha = 0.098), indicating varied user experiences. While normality tests failed, a moderate positive correlation emerged between AI system effectiveness and operational efficiency. Findings highlight AI-IoT's promise in enhancing data precision and management, but underscore the need for improved system integration and user training to optimize satisfaction and reliability in HEP contexts.