Post-correlation radio frequency interference classification methods

TL;DR

This paper compares various post-correlation radio frequency interference classification methods, highlighting the SumThreshold method as the most accurate, fast, and robust, suitable for automated RFI mitigation in large data sets.

Contribution

The paper introduces and evaluates the SumThreshold method, a new iterative RFI classification technique that outperforms existing methods in accuracy and robustness.

Findings

SumThreshold achieves 95% recognition accuracy in simulations.

SumThreshold has approximately 0.1% false positive rate.

It outperforms other mitigation techniques like SVD-based methods.

Abstract

We describe and compare several post-correlation radio frequency interference classification methods. As data sizes of observations grow with new and improved telescopes, the need for completely automated, robust methods for radio frequency interference mitigation is pressing. We investigated several classification methods and find that, for the data sets we used, the most accurate among them is the SumThreshold method. This is a new method formed from a combination of existing techniques, including a new way of thresholding. This iterative method estimates the astronomical signal by carrying out a surface fit in the time-frequency plane. With a theoretical accuracy of 95% recognition and an approximately 0.1% false probability rate in simple simulated cases, the method is in practice as good as the human eye in finding RFI. In addition it is fast, robust, does not need a data model before it can be executed and works in almost all configurations with its default parameters. The method has been compared using simulated data with several other mitigation techniques, including one based upon the singular value decomposition of the time-frequency matrix, and has shown better results than the rest.