Machine Learning and Instrument Autonomy Group

We are using machine learning methods to enable large-scale radio astronomy data analyses in real-time. Specifically, we focus on the detection and characterization of time-varying sources, one of the major scientific goals of current and planned radio arrays (such as the Square Kilometer Array). Detection and characterization of fast transients is expected to open up a previously unexplored area of astronomy, but it presents a daunting challenge in terms of recording and processing technologies. We designed on-line, adaptive, cost-sensitive algorithms that allocate computational and storage resources 'on the fly' to detect the most promising data using a multi-tier approach to trigger on fast transients.

This project created the V-FASTR radio transient detection system and later led in part to the IMBUE project.