scholarly journals SETI from the Moon: Avoiding Radio Pollution for Future Radio-Astronomy

1998 ◽  
Vol 11 (2) ◽  
pp. 996-999 ◽  
Author(s):  
J. Heidmann
Keyword(s):  
Radio Frequency ◽  
Radio Astronomy ◽  
High Sensitivity ◽  
Radio Frequency Interference ◽  
The Moon ◽  
Lunar Farside

AbstractBecause of the ever increasing human-made radio frequency interference (RFI), we propose to IAU a Resolution for protecting the well singled out lunar farside 100 km diameter SAHA crater from any future RFI for the scientific benefit of the coming decades high-sensitivity radioastronomy at large. Immediate and pragmatic action is strongly recommended. Our strategy, different from the ones of a recent ITU Recommendation, could increase our bargaining possibilities.

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 Protecting Space-Based Radio Astronomy

2001 ◽  
Vol 196 ◽  
pp. 324-334 ◽  
Author(s):  
V. Altunin
Keyword(s):  
Radio Frequency ◽  
Radio Astronomy ◽  
Low Frequency ◽  
Radio Frequency Interference ◽  
Lagrangian Point ◽  
Radio Telescopes ◽  
The Moon ◽  
Microwave Background ◽  
Distant Future ◽  
First Results

This paper outlines some of the radio frequency interference issues related to radio astronomy performed with space-based radio telescopes. Radio frequency interference that threatens radio astronomy observations from the surface of Earth will also degrade observations with space-based radio telescopes. However, any resulting interference could be different than for ground-based telescopes due to several factors. Space radio astronomy observations significantly enhance studies in different areas of astronomy. Several space radio astronomy experiments for studies in low-frequency radio astronomy, space VLBI, the cosmic microwave background and the submillimetre wavelengths have flown already. The first results from these missions have provided significant breakthroughs in our understanding of the nature of celestial radio radiation. Radio astronomers plan to deploy more radio telescopes in Earth orbit, in the vicinity of the L2 Sun-Earth Lagrangian point, and, in the more distant future, in the shielded zone of the Moon.

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.

Implementing RFI Mitigation in Radio Science

2019 ◽  
Vol 08 (01) ◽  
pp. 1940010 ◽  
Author(s):  
Willem A. Baan
Keyword(s):  
Radio Frequency ◽  
Data Acquisition ◽  
Radio Astronomy ◽  
Data Acquisition System ◽  
Detection Methods ◽  
Radio Frequency Interference ◽  
Post Processing ◽  
Science Data ◽  
Radio Science ◽  
Mitigation Methods

This paper presents an overview of methods for mitigating radio frequency interference (RFI) in radio science data. The primary purpose of mitigation is to assist observatories to take useful data outside frequency bands allocated to the Science Services (Radio Astronomy Service (RAS) and Earth Exploration Service (EESS)): mitigation should not be needed within passive bands. Mitigation methods may be introduced at a variety of points within the data acquisition system in order to lessen the RFI intensity and to limit the damage it causes. These methods range from proactive methods to changing the local RFI environment by means of regulatory manners, to pre- and post-detection methods, to various pre-processing methods, and to methods applied at or post-processing.

scholarly journals Deep residual detection of radio frequency interference for FAST

2020 ◽  
Vol 492 (1) ◽  
pp. 1421-1431 ◽  
Author(s):  
Zhicheng Yang ◽  
Ce Yu ◽  
Jian Xiao ◽  
Bo Zhang
Keyword(s):  
Radio Frequency ◽  
Large Data ◽  
High Sensitivity ◽  
Original Data ◽  
Training Data ◽  
Radio Frequency Interference ◽  
Data Sets ◽  
Data Set ◽  
Time Required ◽  
Key Steps

ABSTRACT 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.

scholarly journals Inter-channel uniformity of a microwave sounder in space

2018 ◽  
Vol 11 (7) ◽  
pp. 4005-4014 ◽  
Author(s):  
Martin Burgdorf ◽  
Imke Hans ◽  
Marc Prange ◽  
Theresa Lang ◽  
Stefan A. Buehler
Keyword(s):  
Radio Frequency ◽  
Local Oscillator ◽  
Radio Frequency Interference ◽  
The Moon ◽  
Deep Space ◽  
Calibration Target ◽  
Microwave Sounding Unit ◽  
Obvious Reason ◽  
Microwave Sounding ◽  
Advanced Microwave Sounding Unit

Abstract. We analyzed intrusions of the Moon in the deep space view of the Advanced Microwave Sounding Unit-B on the NOAA-16 satellite and found no significant discrepancies in the signals from the different sounding channels between 2001 and 2008. However, earlier investigations had detected biases of up to 10 K, by using simultaneous nadir overpasses of NOAA-16 with other satellites. These discrepancies in the observations of Earth scenes cannot be due to non-linearity of the receiver or contamination of the deep space view without affecting the signal from the Moon as well. As neither major anomalies of the on-board calibration target nor the local oscillator were present, we consider radio frequency interference in combination with a strongly decreasing gain the most obvious reason for the degrading photometric stability. By means of the chosen example we demonstrate the usefulness of the Moon for investigations of the performance of microwave sounders in flight.

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