scholarly journals Radio Frequency Interference Mitigation and Statistics in the Spectral Observations of FAST

2021 ◽  
Author(s):  
Chuan-Peng Zhang ◽  
Jin-Long Xu ◽  
Jie Wang ◽  
Yingjie Jing ◽  
Ziming Liu ◽  
...  
Keyword(s):  
Data Processing ◽  
Radio Frequency ◽  
Radio Telescope ◽  
Radio Astronomy ◽  
Interference Mitigation ◽  
Statistical Data ◽  
Radio Frequency Interference ◽  
Test Results ◽  
Baseline Fitting ◽  
Statistical Results

Abstract In radio astronomy, radio frequency interference (RFI) becomes more and more serious for radio observational facilities. The RFI always influences the search and study of the interesting astronomical objects. Mitigating the RFI becomes an essential procedure in any survey data processing. Five-hundred-meter Aperture Spherical radio Telescope (FAST) is an extremely sensitive radio telescope. It is necessary to find out an effective and precise RFI mitigation method for FAST data processing. In this work, we introduce a method to mitigate the RFI in FAST spectral observation and make a statistics for the RFI using ∼300 hours FAST data. The details are as follows. Firstly, according to the characteristics of FAST spectra, we propose to use the ArPLS algorithm for baseline fitting. Our test results show that it has a good performance. Secondly, we flag the RFI with four strategies, which are to flag extremely strong RFI, flag long-lasting RFI, flag polarized RFI, and flag beam-combined RFI, respectively. The test results show that all the RFI above a preset threshold could be flagged. Thirdly, we make a statistics for the probabilities of polarized XX and YY RFI in FAST observations. The statistical results could tell us which frequencies are relatively quiescent. With such statistical data, we are able to avoid using such frequencies in our spectral observations. Finally, based on the ∼300 hours FAST data, we got an RFI table, which is the most complete database currently for FAST.

scholarly journals RFI Survey for the Giant Metrewave Radio Telescope in India

1991 ◽  
Vol 112 ◽  
pp. 190-193
Author(s):  
G. Swarup ◽  
T.L. Venkatasubramani
Keyword(s):  
Radio Frequency ◽  
Radio Telescope ◽  
Radio Astronomy ◽  
Radio Frequency Interference ◽  
Frequency Range ◽  
Extragalactic Astronomy ◽  
Low Level ◽  
Highly Sensitive ◽  
Set Up

ABSTRACTA Giant Meterwave Radio Telescope (GMRT) is being set up at Khodad about 80 km north of Pune in India for operation in the frequency range of about 30 to 1500 MHz. It is to be completed by 1992 and is being designed to investigate many outstanding problems in the fields of galactic and extragalactic astronomy. We present here measurements of man-made radio frequency interference (RFI) conducted at the GMRT site in 1985 and 1988. It is seen that highly sensitive radio astronomy observations can still be made at selected bands in the above frequency range because of the relatively low level of RFI in India. However, this advantage may not remain for more than a decade or two.

Low-frequency technology for a lunar interferometer

2020 ◽  
Vol 379 (2188) ◽  
pp. 20190575 ◽  
Author(s):  
Kristian Zarb Adami ◽  
I. O. Farhat
Keyword(s):  
Radio Frequency ◽  
Signal Detection ◽  
Radio Astronomy ◽  
Lunar Surface ◽  
Low Frequency ◽  
Radio Interferometer ◽  
Radio Frequency Interference ◽  
The Moon ◽  
Single Antenna ◽  
Global Signal

This work sketches a possible design architecture of a low-frequency radio interferometer located on the lunar surface. The design has evolved from single antenna experiments aimed at the global signal detection of the epoch of reionization (EoR) to the square kilometre array (SKA) which, when complete, will be capable of imaging the highly red-shifted H 1 -signal from the cosmic dawn through to the EoR. However, due to the opacity of the ionosphere below 10 MHz and the anthropogenic radio-frequency interference, these terrestrial facilities are incapable of detecting pre-ionization signals and the moon becomes an attractive location to build a low-frequency radio interferometer capable of detecting such cosmological signals. Even though there are enormous engineering challenges to overcome, having this scientific facility on the lunar surface also opens up several new exciting possibilities for low-frequency radio astronomy. This article is part of a discussion meeting issue ‘Astronomy from the Moon: the next decades’.

scholarly journals Stochastic calibration of radio interferometers

2020 ◽  
Vol 493 (4) ◽  
pp. 6071-6078 ◽  
Author(s):  
Sarod Yatawatta
Keyword(s):  
Data Processing ◽  
Radio Frequency ◽  
Computational Cost ◽  
Spatial Localization ◽  
Radio Frequency Interference ◽  
Radio Telescopes ◽  
Data Rates ◽  
Data Volume ◽  
Radio Transients ◽  
Processing Steps

ABSTRACT With ever-increasing data rates produced by modern radio telescopes like LOFAR and future telescopes like the SKA, many data-processing steps are overwhelmed by the amount of data that needs to be handled using limited compute resources. Calibration is one such operation that dominates the overall data processing computational cost; none the less, it is an essential operation to reach many science goals. Calibration algorithms do exist that scale well with the number of stations of an array and the number of directions being calibrated. However, the remaining bottleneck is the raw data volume, which scales with the number of baselines, and which is proportional to the square of the number of stations. We propose a ‘stochastic’ calibration strategy where we read only in a mini-batch of data for obtaining calibration solutions, as opposed to reading the full batch of data being calibrated. None the less, we obtain solutions that are valid for the full batch of data. Normally, data need to be averaged before calibration is performed to accommodate the data in size-limited compute memory. Stochastic calibration overcomes the need for data averaging before any calibration can be performed, and offers many advantages, including: enabling the mitigation of faint radio frequency interference; better removal of strong celestial sources from the data; and better detection and spatial localization of fast radio transients.

scholarly journals Deep learning improves identification of Radio Frequency Interference

2020 ◽  
Vol 499 (1) ◽  
pp. 379-390
Author(s):  
Alireza Vafaei Sadr ◽  
Bruce A Bassett ◽  
Nadeem Oozeer ◽  
Yabebal Fantaye ◽  
Chris Finlay
Keyword(s):  
Deep Learning ◽  
Radio Frequency ◽  
Transfer Learning ◽  
Radio Astronomy ◽  
Real Data ◽  
Radio Frequency Interference ◽  
Time Frequency ◽  
Amplitude Data ◽  
Important Challenge ◽  
Telescope Arrays

ABSTRACT Flagging of Radio Frequency Interference (RFI) in time–frequency visibility data is an increasingly important challenge in radio astronomy. We present R-Net, a deep convolutional ResNet architecture that significantly outperforms existing algorithms – including the default MeerKAT RFI flagger, and deep U-Net architectures – across all metrics including AUC, F1-score, and MCC. We demonstrate the robustness of this improvement on both single dish and interferometric simulations and, using transfer learning, on real data. Our R-Net model’s precision is approximately $90{{\ \rm per\ cent}}$ better than the current MeerKAT flagger at $80{{\ \rm per\ cent}}$ recall and has a 35 per cent higher F1-score with no additional performance cost. We further highlight the effectiveness of transfer learning from a model initially trained on simulated MeerKAT data and fine-tuned on real, human-flagged, KAT-7 data. Despite the wide differences in the nature of the two telescope arrays, the model achieves an AUC of 0.91, while the best model without transfer learning only reaches an AUC of 0.67. We consider the use of phase information in our models but find that without calibration the phase adds almost no extra information relative to amplitude data only. Our results strongly suggest that deep learning on simulations, boosted by transfer learning on real data, will likely play a key role in the future of RFI flagging of radio astronomy data.

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