AI as a Detector: Lessons in Real Time Pulsar Discovery
The Universe isn't always so quiet: neutron stars, fast radio bursts, and potentially alien civilizations emit bursts of electromagnetic energy - radio transients - into the unknown. In some cases, these emissions, like with pulsars, are constant and periodic; but in others, like with fast radio bursts, they're short in duration and infrequent. Classical detection surveys typically rely on dedispersion techniques and human-crafted signal processing filters to remove noise and highlight a signal of interest. But what if we're missing something?
In this talk we will introduce a workflow to avoid classical processing all together. By feeding RF samples directly from the telescope's digitizers into GPU computing, we can train an AI model to serve as a detector -- not only enabling real time performance, but also making decisions directly on raw spectrogram data, eliminating the need for classical processing. We will demonstrate how each step of the pipeline works - from AI model training and data curation to real-time inferencing at scale. Our hope is that this new sensor processing architecture can simplify development, democratize science, and process increasingly large amounts of data in real time.
