New Discoveries in Astronomical Data

A special issue of Universe (ISSN 2218-1997). This special issue belongs to the section "Astroinformatics and Astrostatistics".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 5975

Special Issue Editors

National Astronomical Observatories, Chinese Academy of Sciences, Beijing, China
Interests: astroinformatics and astrostatistics
National Astronomical Observatories, Chinese Academy of Sciences, Beijing, China
Interests: astronomical data processing; spectral analysis; data mining

Special Issue Information

Dear Colleagues,

With the increase in astronomical data from ground- and space-based telescopes (e.g., SDSS, LAMOST, ZTF, Pan-STARRS, FAST, WISE, GAIA, JWST), astronomy enters a big data era. It is a great challenge for astronomers to handle and analyze such big data due to the complexity, heterogeneities, high dimension and massive volume of astronomical data. New data processing techniques and methods are needed and developing. Various feature extraction and feature selection methods are in bloom. Machine learning and deep learning have become the main tools to handle astronomical big data. Moreover, the coming of multi-messenger astronomy and time domain astronomy leads to more new astronomical discoveries. Special, rare and even new objects are present continuously.

Prof. Dr. Yanxia Zhang
Prof. Dr. A-Li Luo
Guest Editors

Manuscript Submission Information

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Keywords

  • machine learning in astronomy
  • discoveries: from radio to gamma-rays
  • deep learning
  • astrostatistics and astroinformatics
  • data analysis: methods
  • statistical: astronomical data bases

Published Papers (5 papers)

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Research

22 pages, 22260 KiB  
Article
Molecular-Clump Detection Based on an Improved YOLOv5 Joint Density Peak Clustering
by Jin-Bo Hu, Yao Huang, Sheng Zheng, Zhi-Wei Chen, Xiang-Yun Zeng, Xiao-Yu Luo and Chen Long
Universe 2023, 9(11), 480; https://doi.org/10.3390/universe9110480 - 11 Nov 2023
Viewed by 1102
Abstract
The detection and analysis of molecular clumps can lead to a better understanding of star formation in the Milky Way. Herein, we present a molecular-clump-detection method based on improved YOLOv5 joint Density Peak Clustering (DPC). The method employs a two-dimensional (2D) detection and [...] Read more.
The detection and analysis of molecular clumps can lead to a better understanding of star formation in the Milky Way. Herein, we present a molecular-clump-detection method based on improved YOLOv5 joint Density Peak Clustering (DPC). The method employs a two-dimensional (2D) detection and three-dimensional (3D) stitching strategy to accomplish the molecular-clump detection. In the first stage, an improved YOLOv5 is used to detect the positions of molecular clumps on the Galactic plane, obtaining their spatial information. In the second stage, the DPC algorithm is used to combine the detection results in the velocity direction. In the end, the clump candidates are positioned in the 3D position-position-velocity (PPV) space. Experiments show that the method can achieve a high recall of 98.41% in simulated data made up of Gaussian clumps added to observational data. The efficiency of the strategy has also been demonstrated in experiments utilizing observational data from the Milky Way Imaging Scroll Painting (MWISP) project. Full article
(This article belongs to the Special Issue New Discoveries in Astronomical Data)
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16 pages, 5811 KiB  
Article
A Meteor Detection Algorithm for GWAC System
by Yicong Chen, Guangwei Li, Cuixiang Liu, Bo Qiu, Qianqian Shan and Mengyao Li
Universe 2023, 9(11), 468; https://doi.org/10.3390/universe9110468 - 30 Oct 2023
Viewed by 1149
Abstract
Compared with the international meteor surveillance systems, the ground wide angle camera (GWAC) system exhibits characteristics such as images with the resolution of 4K × 4K and single-site observation. These characteristics present challenges for meteor detection in the GWAC system. Consequently, this paper [...] Read more.
Compared with the international meteor surveillance systems, the ground wide angle camera (GWAC) system exhibits characteristics such as images with the resolution of 4K × 4K and single-site observation. These characteristics present challenges for meteor detection in the GWAC system. Consequently, this paper proposes a new meteor detection algorithm for the GWAC system on the base of the solely mini-GWAC system data algorithm. The new algorithm consists of the following key steps: (1) to compare differences between adjacent frames, applying block-based image binarization thresholds, and incorporating median filtering to reduce noise; (2) to adopt the probabilistic Hough transform (PHT) to identify moving objects and cluster them based on the origin moment of the line segments, while assessing the credibility of clustering; (3) to introduce the so-called maximum disappearance frame for moving objects in the tracking algorithm, enhancing the ability to track multi-frame moving objects. The utilization of the line segment inclination angle of the moving object as the direction of movement facilitates the tracking of multiple moving objects, thereby reducing the probability of mistakenly selecting single-frame moving objects; (4) to leverage the light curves of single-frame moving objects to select meteors to enhance the accuracy of meteor detection. Comparative experiments demonstrate that our proposed algorithm processes each frame image in just 0.39 s, achieving an accuracy of 89.8% in the dataset of 5856 adjacent frames. The experimental results indicate that the algorithm achieved an accuracy of 90.27% when applied in the meteor detection of the image data captured by the GWAC system from Dec. 10th to 19th in 2019 and 2021, obtaining excellent detection results. Full article
(This article belongs to the Special Issue New Discoveries in Astronomical Data)
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12 pages, 1567 KiB  
Article
A Software for RFI Analysis of Radio Environment around Radio Telescope
by Yu Wang, Haiyan Zhang, Jian Wang, Shijie Huang, Hao Hu and Cheng Yang
Universe 2023, 9(6), 277; https://doi.org/10.3390/universe9060277 - 08 Jun 2023
Cited by 1 | Viewed by 903
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. [...] Read more.
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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14 pages, 14019 KiB  
Article
The Analysis and Verification of IMT-2000 Base Station Interference Characteristics in the FAST Radio Quiet Zone
by Jian Wang, Yibo Zhao, Cheng Yang, Yafei Shi, Yulong Hao, Haiyan Zhang, Jianmin Sun and Dingling Luo
Universe 2023, 9(6), 248; https://doi.org/10.3390/universe9060248 - 24 May 2023
Cited by 2 | Viewed by 733
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 [...] Read more.
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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13 pages, 3632 KiB  
Article
Self-Supervised Learning for Solar Radio Spectrum Classification
by Siqi Li, Guowu Yuan, Jian Chen, Chengming Tan and Hao Zhou
Universe 2022, 8(12), 656; https://doi.org/10.3390/universe8120656 - 14 Dec 2022
Cited by 1 | Viewed by 1084
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. [...] Read more.
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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