ASTROFLOW: A Real-Time End-to-End Pipeline for Radio Single-Pulse Searches

TL;DR

Astroflow is a GPU-accelerated, real-time pipeline for detecting fast radio bursts in radio telescope data, capable of handling large data volumes efficiently and with high sensitivity.

Contribution

It introduces Astroflow, an integrated, high-performance, GPU-based pipeline for real-time single-pulse detection in radio astronomy data, scalable for upcoming large surveys.

Findings

Achieves 10x faster-than-real-time processing on consumer GPUs.

Maintains high sensitivity across various pulse widths and dispersion measures.

Demonstrates feasibility of fully integrated GPU-accelerated search pipeline.

Abstract

Fast radio bursts (FRBs) are extremely bright, millisecond duration cosmic transients of unknown origin. The growing number of wide-field and high-time-resolution radio surveys, particularly with next-generation facilities such as the SKA and MeerKAT, will dramatically increase FRB discovery rates, but also produce data volumes that overwhelm conventional search pipelines. Real-time detection thus demands software that is both algorithmically robust and computationally efficient. We present Astroflow, an end-to-end, GPU-accelerated pipeline for single-pulse detection in radio time-frequency data. Built on a unified C++/CUDA core with a Python interface, Astroflow integrates RFI excision, incoherent dedispersion, dynamic-spectrum tiling, and a YOLO-based deep detector. Through vectorized memory access, shared-memory tiling, and OpenMP parallelism, it achieves 10x faster-than-real-time processing on consumer GPUs for a typical 150 s, 2048-channel observation, while preserving high sensitivity across a wide range of pulse widths and dispersion measures. These results establish the feasibility of a fully integrated, GPU-accelerated single-pulse search stack, capable of scaling to the data volumes expected from upcoming large-scale surveys. Astroflow offers a reusable and deployable solution for real-time transient discovery, and provides a framework that can be continuously refined with new data and models.