Particle Tracking in a Cloud Chamber Using a Stereoscopic Camera System

The cloud chamber is a useful tool to visually see radiation. Cameras and an algorithm can be used to detect tracks and classify particle tracks within the cloud chamber. This is done with a stereoscopic camera system, which allows a 3d view of the cloud chamber. Track characteristics can then able to be accurately determined. This project used a Raspberry Pi 5 along with a stereoscopic camera system over a diffusion cloud chamber. Multiple videos were taken for two hours each. The code employed instance segmentation to map out the particle tracks and then used 3d reconstruction with the stereoscopic camera system to visualize the particles in a 3d plane. To classify particles, Linear Energy Transfer (LET) was utilized to differentiate between alpha and beta particles. After classification of the particles, their characteristics will be determined, such as track length, angle, and energy deposition. Additionally, Geant4 will be used to generate data to train an AI to accomplish the goal of tracking, finding characteristics, and identifying the particle. The AI is planned to extend to identify Physics phenomena like Compton scattering or delta rays. This research will allow a deeper understanding of how particles move through a medium with experimental results.