Particle Tracking in a Cloud Chamber Using a Stereoscopic Camera System

Although the cloud chamber is over 100 years old, it remains a useful way of being able to visually see radiation. Radiation such as alpha and beta particles can be seen using a supersaturated alcohol vapor, the benefit of a medium such as supersaturated alcohol vapor is how energy is deposited by particles. Higher energy deposition provides a bigger cloud track, allowing a viewer to differentiate between an alpha particle which has a bigger particle track and an electron which has thinner particle track. Additionally, other particles like muons may be seen through other factors such as track angle. The visual capabilities of the cloud chamber allow a different method of particle detection that utilizes cameras with an algorithm that tracks and classifies the particle track. This is done with a stereoscopic camera system which allows a 3d view of the cloud chamber, the track characteristics are then able to be determined accurately. 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. This research will allow a deeper understanding of how particles move through a medium with experimental results.