Автоматизированный анализ траекторий заряженных частиц в камере Вильсона с использованием методов компьютерного зрения

Abstract

This paper presents a software complex for automated analysis of charged particle tracks in a Wilson cloud chamber. The developed application combines computer vision methods and machine learning to detect tracks, identify their parameters, and visualize complete particle trajectories from the emission source to the end of the visible track. The software is implemented in Python using the YOLO neural network model and provides a user-friendly interface for interactive work with experimental data.