Real-Time Particle Visualization in an Electric-Cooled Cloud Chamber Using Machine Learning

Abstract This paper presents the development and implementation of an electric-cooled cloud chamber designed to visualize alpha and beta particle tracks. Traditional cloud chambers rely on dry ice for cooling, which can be cumbersome and impractical for extended use. Our approach employs a Peltier device coupled with a commercial CPU cooler, providing a stable, large field of view, and long-lasting cooling environment. Additionally, we have integrated a real-time visualization system utilizing OpenCV and machine learning with TensorFlow. This system accurately identifies and labels particle tracks, enhancing the educational experience. The cloud chamber operates seamlessly with consumer laptops, such as the MacBook Air with an M1 chip, making it accessible and convenient for educational purposes. Initial qualitative results demonstrate the system's effectiveness. Future work will focus on refining detection accuracy and exploring additional applications. This advancement not only improves the usability of cloud chambers but also offers new possibilities for interactive scientific education.