Shaw-IAU Workshops by the OAE

Citizen scientists have automated the Cameras for Allsky Meteor Surveillance (CAMS) data network, so data is automatically downloaded from the cameras, prepped for triangulation, and analyzed. Additionally, an ML algorithm replicates the scientists thought process to sift through the video captured each night to identify meteor showers with results published on the NASA CAMS Meteor Shower Portal. The open source portal not only aids in effective communication of ideas and results to a diverse audience but is a useful interactive educational tool used to explore meteor shower activity from the previous night globally and encourages citizen scientists to develop an interest in space science. Learn how to reuse the open source code for your datasets and explore meteor showers!

Biography:

Siddha Ganju, an AI researcher who Forbes featured in their under30 list, is a Self-Driving Architect at Nvidia. Mentoring researchers at NASA’s AI accelerator called Frontier Development Labs as a Machine Learning lead, her contributions on meteor detection with AI helped automate the recognition of meteor showers (a significantly human intensive task). Additionally, by growing interest in citizen scientists globally, the network of cameras in this project has now increased 6x in multiple new countries, and thus detected the highest number of meteors in NASA’s 62-year history. The quick turnaround time of discovery ultimately led to the discovery of multiple meteor showers and the first-ever instrumental evidence of the Grigg-Mellish comet

Watch a video for this poster (external link)