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The frequencies of interest for redshifted 21 cm observations are heavily affected by terrestrial radio-frequency interference (RFI). We identify the McGill Arctic Research Station (MARS) as a new RFI-quiet site and report its RFI occupancy using 122 hours of data taken with a prototype antenna station developed for the Array of Long-Baseline Antennas for Taking Radio Observations from the Sub-Antarctic. Using an RFI flagging process tailored to the MARS data, we find an overall RFI occupancy of 1.8% averaged over 20-125 MHz. In particular, the FM broadcast band (88-108 MHz) is found to have an RFI occupancy of at most 1.6%. The data were taken during the Arctic summer, when degraded ionospheric conditions and an active research base contributed to increased RFI. The results quoted here therefore represent the maximum-level RFI environment at MARS.
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We present the first survey of radio frequency interference (RFI) at the future site of the low frequency Square Kilometre Array (SKA), the Murchison Radio-astronomy Observatory (MRO), that both temporally and spatially resolves the RFI. The survey is conducted in a 1 MHz frequency range within the FM band, designed to encompass the closest and strongest FM transmitters to the MRO (located in Geraldton, approximately 300 km distant). Conducted over approximately three days using the second iteration of the Engineering Development Array in an all-sky imaging mode, we find a range of RFI signals. We are able to categorise the signals into: those received directly from the transmitters, from their horizon locations; reflections from aircraft (occupying approximately 13% of the observation duration); reflections from objects in Earth orbit; and reflections from meteor ionisation trails. In total we analyse 33,994 images at 7.92 s time resolution in both polarisations with angular resolution of approximately 3.5 deg., detecting approximately forty thousand RFI events. This detailed breakdown of RFI in the MRO environment will enable future detailed analyses of the likely impacts of RFI on key science at low radio frequencies with the SKA.
Radio frequency interference (RFI) detection and excision are key steps in the data-processing pipeline of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Because of its high sensitivity and large data rate, FAST requires more accurate and efficient RFI flagging methods than its counterparts. In the last decades, approaches based upon artificial intelligence (AI), such as codes using convolutional neural networks (CNNs), have been proposed to identify RFI more reliably and efficiently. However, RFI flagging of FAST data with such methods has often proved to be erroneous, with further manual inspections required. In addition, network construction as well as preparation of training data sets for effective RFI flagging has imposed significant additional workloads. Therefore, rapid deployment and adjustment of AI approaches for different observations is impractical to implement with existing algorithms. To overcome such problems, we propose a model called RFI-Net. With the input of raw data without any processing, RFI-Net can detect RFI automatically, producing corresponding masks without any alteration of the original data. Experiments with RFI-Net using simulated astronomical data show that our model has outperformed existing methods in terms of both precision and recall. Besides, compared with other models, our method can obtain the same relative accuracy with fewer training data, thus reducing the effort and time required to prepare the training data set. Further, the training process of RFI-Net can be accelerated, with overfittings being minimized, compared with other CNN codes. The performance of RFI-Net has also been evaluated with observing data obtained by FAST and the Bleien Observatory. Our results demonstrate the ability of RFI-Net to accurately identify RFI with fine-grained, high-precision masks that required no further modification.
The Murchison Widefield Array (MWA) is a new low-frequency interferometric radio telescope built in Western Australia at one of the locations of the future Square Kilometre Array (SKA). We describe the automated radio-frequency interference (RFI) detection strategy implemented for the MWA, which is based on the AOFlagger platform, and present 72-231-MHz RFI statistics from 10 observing nights. RFI detection removes 1.1% of the data. RFI from digital TV (DTV) is observed 3% of the time due to occasional ionospheric or atmospheric propagation. After RFI detection and excision, almost all data can be calibrated and imaged without further RFI mitigation efforts, including observations within the FM and DTV bands. The results are compared to a previously published Low-Frequency Array (LOFAR) RFI survey. The remote location of the MWA results in a substantially cleaner RFI environment compared to LOFARs radio environment, but adequate detection of RFI is still required before data can be analysed. We include specific recommendations designed to make the SKA more robust to RFI, including: the availability of sufficient computing power for RFI detection; accounting for RFI in the receiver design; a smooth band-pass response; and the capability of RFI detection at high time and frequency resolution (second and kHz-scale respectively).
In radio astronomy, reference signals from auxiliary antennas that receive only the radio frequency interference (RFI) can be modified to model the RFI environment at the astronomy receivers. The RFI can then be canceled from the astronomy signal paths. However, astronomers typically only require signal statistics. If the RFI statistics are changing slowly, the cancellation can be applied to the signal correlations at a much lower rate than is required for standard adaptive filters. In this paper we describe five canceler setups; precorrelation and postcorrelation cancelers that use one or two reference signals in different ways. The theoretical residual RFI and added noise levels are examined and are demonstrated using microwave television RFI at the Australia Telescope Compact Array. The RFI is attenuated to below the system noise, a reduction of at least 20 dB. While dual-reference cancelers add more reference noise than single-reference cancelers, this noise is zero-mean and only adds to the system noise, decreasing the sensitivity. The residual RFI that remains in the output of single-reference cancelers (but not dual-reference cancelers) sets a nonzero noise floor that does not act like random system noise and may limit the achievable sensitivity. Thus, dual-reference cancelers often result in superior cancellation. Dual-reference precorrelation cancelers require a double-canceler setup to be useful and to give equivalent results to dual-reference postcorrelation cancelers.
We introduce a new pipeline for analyzing and mitigating radio frequency interference (RFI), which we call Sky-Subtracted Incoherent Noise Spectra (SSINS). SSINS is designed to identify and remove faint RFI below the single baseline thermal noise by employing a frequency-matched detection algorithm on baseline-averaged amplitudes of time-differenced visibilities. We demonstrate the capabilities of SSINS using the Murchison Widefield Array (MWA) in Western Australia. We successfully image aircraft flying over the array via digital television (DTV) reflection detected using SSINS and summarize an RFI occupancy survey of MWA Epoch of Reionization data. We describe how to use SSINS with new data using a documented, publicly available implementation with comprehensive usage tutorials.
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