Enhancing Particle Detector Construction: Deep Learning Quality Control Applications

Abstract

This paper explores the integration of deep learning techniques for enhancing quality control (QC) in particle detector construction. Particle detectors are critical components in high-energy physics experiments, necessitating rigorous QC measures to ensure their reliability and accuracy. Traditional QC methods often rely on manual inspection, which can be time-consuming and prone to human error. In response, this study investigates the application of deep learning algorithms for automating defect detection and classification tasks in particle detector construction. By leveraging neural networks trained on labeled datasets, deep learning offers the potential to streamline the QC process, improve efficiency, and enhance overall detector performance. Through experimental validation and comparative analysis, this paper demonstrates the efficacy of deep learning in advancing particle detector construction QC, paving the way for more efficient and reliable detector systems in high-energy physics research.