Evaluating the effectiveness of radio frequency interference removal algorithms for single pulse searches

TL;DR

This study evaluates various RFI removal algorithms' effectiveness in enhancing the detection of single pulses in radio astronomy data, crucial for real-time searches in next-generation telescopes.

Contribution

It systematically compares the performance of IQRM, SKF, and ZDMF algorithms in simulated RFI environments for single pulse detection.

Findings

IQRM, SKF, and ZDMF vary in effectiveness depending on RFI conditions.

Certain algorithms improve pulse recovery but may introduce false positives.

Algorithm choice significantly impacts real-time transient detection success.

Abstract

Radio Frequency Interference (RFI), the presence of artificial and/or terrestrial signals in astronomical data, poses a great challenge to the search for pulsars and radio transients, such as Rotating Radio Transients (RRATs) and Fast Radio Bursts (FRBs), by obscuring or distorting the signal of interest and resulting in large numbers of erroneous detections. RFI mitigation algorithms aim to remove this interference and improve the chance of detection of transients, but with the growing number of techniques, selecting the most appropriate method for a given survey can be problematic. The choice of method is particularly important in real-time searches planned for next-generation telescopes such as those of the SKAO, where there is no possibility to reprocess the data. In this paper, we explore the algorithm selection problem by injecting pulses into data which simulates several RFI environments. A set of these files is then cleaned using RFI mitigation algorithms and run through a single pulse search pipeline to analyse the recovery of the injected pulses. We examine the recovery of the injected single pulses with an emphasis on a number of cases spanning a range of pulse brightness, width and dispersion measure. The efficacy and side effects of a few popular RFI excision methods, namely IQRM, SKF, and ZDMF are evaluated.