A deployed real-time end-to-end deep learning algorithm for fast radio burst detection

Context. Over the past decade, fast radio bursts (FRBs) have attracted substantial interest in the field of astrophysics due to their extremely energetic nature, drawing considerable speculation regarding the mechanisms that are behind these fast transient events. To further our understanding of FRBs, it is essential to develop fast and efficient analysis pipelines to recover more of these events in radio astronomy observations. Aims. We developed a fast end-to-end deep learning based FRB detection pipeline capable of handling ~100 Gb/s of real-time data throughput without applying dedispersion techniques. Methods. We introduced a modified masked ResNet-38 model designed for FRB detection tasks. Using synthetic injections, we demonstrated that our trained end-to-end model matches and surpasses current established pipelines (on injections) with a 7% gain in accuracy without the need for dedispersion or radio frequency interference masking. We deployed this model in a real-time setting at the Allen Telescope Array. Utilizing Nvidia Holoscan, a new GPU-accelerated sensor processing platform along with model optimizations, our pipeline successfully executed an end-to-end FRB detection on beam-formed spectrograms. Results. We report that our end-to-end pipeline achieves a latency of 150× faster than real-time production constraints compared to current state-of-the-art dedispersion + ML assisted FRB search pipelines at the Allen Telescope Array, which is three times slower than real-time constraints. We demonstrate the full functionality of our pipeline by successfully recovering giant pulses from PSR B0531+21 in a real-time setting as well as from FRB20240114<ns0:sc>A</ns0:sc> in an offline setting. This study highlights the promise of future real-time deep-learning-accelerated radio astronomy.