Detection and Classification of High Energy Cosmic Rays Using TinyML

Cosmic rays (CR) and cosmic ray ensembles (CRE) can reveal new insights about the universe. Scientists need low-cost devices that can detect and record CRE events over large areas. We built a small, cheap, and energy-efficient device that can do this using a TinyML model to identify three types of cosmic ray traces: Worm, Spot, and Line. The model has been successfully deployed in hardware and has been tested using three test-bench scenarios. The results show a successful detection and identification for all cosmic ray traces. The detected trace information can be then sent on the Internet. Our device can help expand the cosmic ray observatory network and can be used for space missions and IoT applications.