Tracking Islands on Smectic Bubbles using Machine Learning*

The Observation and Analysis of Smectic Islands in Space (OASIS) mission was a series of experiments on the International Space Station probing the dynamics of smectic islands on curved fluid bubbles in microgravity. The analysis of the motion of these islands through traditional particle tracking technologies is challenging due to uneven lighting and geometric distortion. We apply modern advances in deep learning to identify and track these islands in a novel way. Our Neural Network uses a modified U-Net [Ronneberger, et al., Medical Image Computing and Computer-Assisted Intervention, pp 234-241 (2015)] image segmentation net to generate masks that classify our objects. Segmented masks provide a simplified representation of the objects on screen and a detection layer easily predicts locations of each island. The model is trained using synthetic data generated using the 3D rendering software Blender. This technique shows promise for a variety of problems including improving the detection of topological defects in liquid crystals [Green et al., Soft Matter (2020)].