Post‐correlation radio frequency interference classification methods

By A. Offringa, D. Bruyn, Michael Biehl, S. Zaroubi, G. Bernardi, V. Pandey. 2010

277 citations recorded by the source corpus.

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.

Subfields: Astronomy and Astrophysics, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/f7b11a1bc5da15dcd59e167b0860eba2ea22cb4e

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Citations in the corpus

LOFAR FACET CALIBRATION (2016, methodology): “We performed another round of RFI removal with AOFlagger on the combined 10 subband datasets as this allowed the detection of lower-level RFI.”
FIRST SEASON MWA EOR POWER SPECTRUM RESULTS AT REDSHIFT 7 (2016, methodology): “For this step we use the COTTER package (Offringa et al. 2015), which in turn calls the AOFLAGGER36 to perform RFI flagging (Offringa et al. 2010).”
An Overview of CHIME, the Canadian Hydrogen Intensity Mapping Experiment (2022, background): “This figure-of-merit is fed to a sum-threshold algorithm (Offringa et al. 2010) in frequency-time space which outputs a single mask for all baseline stacks.”
Reionization and the Cosmic Dawn with the Square Kilometre Array (2012, background): “Several examples of such automatic data-flagging systems exist (Offringa et al. 2010; Pearson 2002), but there are situations where manual intervention is still required for excising very subtle errors which escape automatic RFI excision.”
CASA, the Common Astronomy Software Applications for Radio Astronomy (2022, background): “RFI is often limited to well-defined ranges in frequency or time, and the corresponding visibilities can be discarded by flagging the data (e.g., Middelberg 2006; Offringa et al. 2010).”
LOFAR: The LOw-Frequency ARray (2013, methodology): “Subsequently, it flags line-shaped features with the SumThreshold method (Offringa et al. 2010).”
LOFAR 150-MHz observations of the Boötes field: catalogue and source counts (2016, methodology): “The initial pre-processing of the data was carried out using the Radio Observatory pipeline and consisted of RFI excision (using AOFlagger; Offringa et al. 2010; Offringa, van de Gronde & Roerdink 2012), flagging the noisy first channel and last three channels of each SB, and averaging in time and…”
wsclean: an implementation of a fast, generic wide-field imager for radio astronomy (2014, methodology): “…were: i) preprocessing of the observation with the Cotter preprocessing pipeline, which includes time averaging and RFI flagging with the AOFLAGGER (Offringa et al. 2010, 2012b); ii) calibration using Hydra A without direction dependence using a custom implementation of the RTS fullpolarisation…”