Enhancing the effectiveness of physical measurements and scientific project management through big data and artificial intelligence

Abstract

Modern scientific experiments in high-energy physics, astrophysics, and quantum mechanics generate enormous amounts of data that require accurate and real-time processing. The integration of Big Data and artificial intelligence technologies is transforming the management of large-scale scientific projects by enabling the automation of data collection, processing, and analysis. The use of machine learning and deep learning algorithms enhances measurement precision, optimizes resource utilization, and allows for adaptive responses to changing experimental conditions. In projects such as CERN and LIGO, these technologies ensure real-time monitoring, anomaly detection, and risk prediction, which are crucial for achieving reliable results. The paper also discusses challenges, including the high demand for computational power and concerns regarding data security. Overall, the integration of Big Data and AI significantly improves the efficiency, accuracy, and adaptability of scientific research and project management, paving the way for future breakthroughs in precision and performance.