The radio environment of the 21 Centimeter Array: RFI detection and mitigation

TL;DR

This paper presents effective RFI detection and mitigation techniques for the 21 Centimeter Array, crucial for low frequency radio observations targeting cosmic dawn and reionization, using statistical and innovative visibility correlation methods.

Contribution

It introduces a statistical RFI identification approach and the visibility correlation coefficient to improve RFI suppression in low frequency radio interferometry.

Findings

State-of-the-art techniques effectively mitigate RFI in 21CMA data.

Visibility correlation coefficient enhances suppression of time-varying RFI.

Mitigation methods enable more accurate cosmic signal detection.

Abstract

Detection and mitigation of radio frequency interference (RFI) is the first and also the key step for data processing in radio observations, especially for ongoing low frequency radio experiments towards the detection of the cosmic dawn and epoch of reionization (EoR). In this paper we demonstrate the technique and efficiency of RFI identification and mitigation for the 21 Centimeter Array (21CMA), a radio interferometer dedicated to the statistical measurement of EoR. For terrestrial, man-made RFI, we concentrate mainly on a statistical approach by identifying and then excising non-Gaussian signatures, in the sense that the extremely weak cosmic signal is actually buried under thermal and therefore Gaussian noise. We also introduce the so-called visibility correlation coefficient instead of conventional visibility, which allows a further suppression of rapidly time-varying RFI. Finally, we briefly discuss removals of the sky RFI, the leakage of sidelobes from off-field strong radio sources with time-invariant power and a featureless spectrum. It turns out that state of the art technique should allow us to detect and mitigate RFI to a satisfactory level in present low frequency interferometer observations such as those acquired with the 21CMA, and the accuracy and efficiency can be greatly improved with the employment of low-cost, high-speed computing facilities for data acquisition and processing.