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Open AccessArticle

A Software for RFI Analysis of Radio Environment around Radio Telescope

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Universe 2023, 9(6), 277; https://doi.org/10.3390/universe9060277 - 08 Jun 2023

Cited by 1 | Viewed by 553

Abstract

Radio astronomy uses radio telescopes to detect very faint emissions from celestial objects. However, human-made radio frequency interference (RFI) is currently a common problem faced by most terrestrial radio telescopes, and it is getting worse with the development of the economy and technology. Therefore, it is essential to monitor and evaluate interference during the planning, construction, and operation stages of the radio telescope and protect the quiet radio environment around the radio astronomical site. In this paper, we present a software for an RFI analysis of the radio environment around the telescope. In this software, information has been collected, including the location of the site; the technical specifications, such as aperture and the frequency range of the radio telescopes; and the terrain around the site. The software and its modules are composed of telescope, geographic, and meteorological databases, and analysis modules of terrestrial and space-based RFI. Combined with the propagation characteristics of radio waves, we can analyze and evaluate RFI on the ground and in space around the radio telescope. The feasibility of the software has been proved by the experimental implementation of the propagation properties and RFI source estimation. With this software, efficient technical support can be expected for protecting the radio environment around the telescope, as well as improving site selection for planned radio astronomical facilities. Full article

(This article belongs to the Special Issue New Discoveries in Astronomical Data)

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Open AccessArticle

The Analysis and Verification of IMT-2000 Base Station Interference Characteristics in the FAST Radio Quiet Zone

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Universe 2023, 9(6), 248; https://doi.org/10.3390/universe9060248 - 24 May 2023

Viewed by 474

Abstract

In this study, we aim to analyze the electromagnetic interference (EMI) regarding the Five-hundred-meter Aperture Spherical radio Telescope (FAST) caused by base stations in the International Mobile Telecommunications-2000 (IMT-2000) frequency band. By analyzing the frequency bands used by the transmitting and receiving devices and the surrounding environmental parameters and utilizing an approach to predicting radio wave propagation loss that is based on deterministic methods, we conclude by comparing the predicted received power at the FAST with its interference protection threshold. Our analysis demonstrates that, currently, only 55.31% of IMT-2000 base stations in the FAST radio quiet zone (RQZ) meet the protection threshold. Additionally, this article verifies the applicability and accuracy of the radio wave propagation model used in the research based on field strength measurements. Overall, this study provides valuable insights for improving the electromagnetic environment surrounding FAST and reducing the EMI caused by mobile communication base stations. It also provides corresponding analysis methods and useful suggestions for analyzing electromagnetic radiation interference in other radio telescopes. Full article

(This article belongs to the Special Issue New Discoveries in Astronomical Data)

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Open AccessArticle

Self-Supervised Learning for Solar Radio Spectrum Classification

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Universe 2022, 8(12), 656; https://doi.org/10.3390/universe8120656 - 14 Dec 2022

Viewed by 836

Abstract

Solar radio observation is an important way to study the Sun. Solar radio bursts contain important information about solar activity. Therefore, real-time automatic detection and classification of solar radio bursts are of great value for subsequent solar physics research and space weather warnings. Traditional image classification methods based on deep learning often require considerable training data. To address insufficient solar radio spectrum images, transfer learning is generally used. However, the large difference between natural images and solar spectrum images has a large impact on the transfer learning effect. In this paper, we propose a self-supervised learning method for solar radio spectrum classification. Our method uses self-supervised training with a self-masking approach in natural language processing. Self-supervised learning is more conducive to learning the essential information about images compared with supervised methods, and it is more suitable for transfer learning. First, the method pre-trains using a large amount of other existing data. Then, the trained model is fine-tuned on the solar radio spectrum dataset. Experiments show that the method achieves a classification accuracy similar to that of convolutional neural networks and Transformer networks with supervised training. Full article

(This article belongs to the Special Issue New Discoveries in Astronomical Data)

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