Systematic performance of the ASKAP Fast Radio Burst search algorithm

Detecting fast radio bursts (FRBs) requires software pipelines to search for dispersed single pulses of emission in radio telescope data. In order to enable an unbiased estimation of the underlying FRB population, it is important to understand the algorithm efficiency with respect to the search parameter space and thus the survey completeness. The Fast Real-time Engine for Dedispersing Amplitudes (FREDDA) search pipeline is a single pulse detection pipeline designed to identify radio pulses over a large range of dispersion measures (DM) with low latency. It is used on the Australian Square Kilometre Array Pathfinder (ASKAP) for the Commensal Real-time ASKAP Fast Transients (CRAFT) project . We utilise simulated single pulses in the low- and high-frequency observation bands of ASKAP to analyse the performance of the pipeline and infer the underlying FRB population. The simulation explores the Signal-to-Noise Ratio (S/N) recovery as a function of DM and the temporal duration of FRB pulses in comparison to injected values. The effects of intra-channel broadening caused by dispersion are also carefully studied in this work using control datasets. Our results show that for Gaussian-like single pulses, $> 85 \%$ of the injected signal is recovered by pipelines such as FREDDA at DM < 3000 $\mathrm{pc\ cm^{-3}}$ using standard boxcar filters compared to an ideal incoherent dedispersion match filter. Further calculations with sensitivity implies at least $\sim 10\%$ of FRBs in a Euclidean universe at target sensitivity will be missed by FREDDA and HEIMDALL, another common pipeline, in ideal radio environments at 1.1 GHz.