Neural Networks in Particle Detector Quality Assurance: A Deep Learning Approach

Abstract

This paper explores the application of neural networks in the quality assurance process of particle detector construction, employing a deep learning approach. Particle detectors play a crucial role in high-energy physics experiments, necessitating stringent quality control measures to ensure their reliability and accuracy. Traditional quality assurance methods often rely on manual inspection and testing, which can be time-consuming and prone to human error. In contrast, deep learning offers a promising alternative by leveraging large datasets to train neural networks capable of automated defect detection and classification. This paper discusses the development and implementation of deep learning models tailored to the specific challenges of particle detector quality control, including the identification of defects such as faulty wiring, damaged components, and misalignments. Through experimental validation and comparative analysis, we demonstrate the efficacy of deep learning in enhancing the efficiency and effectiveness of quality assurance processes in particle detector construction. Additionally, we explore potential avenues for further research and development to maximize the benefits of neural networks in advancing quality control standards within the field of high-energy physics.