  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 datasets. Next generation radio
telescopes such as the SPOTLIGHT project at the GMRT produce data at rates that
mandate real-time processing, as storage of the entire captured dataset 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 which achieves
order-of-magnitude improvements in runtime performance compared to FDAS whilst
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 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, prioritizing datasets
for further analysis and focusing human and subsequent computational resources
on likely binary pulsar signatures.
