Post-correlation radio frequency interference classification methods

• Published in: Monthly notices of the Royal Astronomical Society Vol. 405; no. 1; pp. 155 - 167
• Main Authors: Offringa, A. R., de Bruyn, A. G., Biehl, M., Zaroubi, S., Bernardi, G., Pandey, V. N.
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 11-06-2010; Wiley-Blackwell; Oxford University Press
• Subjects: Astronomy; Classification; Earth, ocean, space; Exact sciences and technology; instrumentation: interferometers; methods: data analysis; Radio astronomy; radio continuum: general; Radio telescopes; techniques: interferometric; radio continuum: general; methods: data analysis; instrumentation: interferometers; techniques: interferometric; Continuum; Data analysis; Correlations; Classification; Probability; Models; Iterative methods; Singular value decomposition
• ISSN: 0035-8711; 1365-2966
• DOI: 10.1111/j.1365-2966.2010.16471.x

We describe and compare several post-correlation radio frequency interference (RFI) classification methods. As data sizes of observations grow with new and improved telescopes, the need for completely automated, robust methods for RFI 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 per cent recognition and an approximately 0.1 per cent 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.