Pulscan: Binary Pulsar Detection Using Unmatched Filters on NVIDIA GPUs

The Fourier domain acceleration search (FDAS) and Fourier domain jerk search (FDJS) are proven matched-filtering techniques for detecting binary pulsar signatures in time-domain radio astronomy data sets. Next-generation radio telescopes such as the SPOTLIGHT project at the Giant Metrewave Radio Telescope (GMRT) produce data at rates that mandate real-time processing, as storage of the entire captured data set for subsequent offline processing is infeasible. The computational demands of FDAS and FDJS make them challenging to implement in real-time detection pipelines, requiring costly high-performance computing facilities. To address this, we propose Pulscan, an unmatched-filtering approach that achieves order-of-magnitude improvements in runtime performance compared to FDAS while being able to detect both accelerated and some jerked binary pulsars. We profile the sensitivity of Pulscan using a distribution (N = 10,955) of synthetic binary pulsars (simulated post–radio-frequency interference mitigation) and compare its performance with FDAS and FDJS. Our implementation of Pulscan includes an OpenMP version for multicore CPU acceleration, a version for heterogeneous CPU/GPU environments such as NVIDIA Grace Hopper, and a fully optimized NVIDIA GPU implementation for integration into an AstroAccelerate pipeline, which will be deployed in the SPOTLIGHT project at the GMRT. Our results demonstrate that unmatched filtering in Pulscan can serve as an efficient data reduction step prior to FDAS or FDJS, selecting data sets for further analysis and focusing subsequent computational resources on likely binary pulsar signatures.