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![(a) Full-disk Hα\documentclass[12pt]{minimal} \usepackage{amsmath}...](publication/325122471/figure/fig6/AS:962170363867157@1606410699489/a-Full-disk-Hadocumentclass12ptminimal-usepackageamsmath-usepackagewasysym_Q320.jpg)
In this article, an automated solar flare detection method applied to both full-disk and local high-resolution H\(\upalpha\) images is proposed. An adaptive gray threshold and an area threshold are used to segment the flare region. Features of each detected flare event are extracted, e.g. the start, peak, and end time, the importance class, and the...
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... Figure 2 shows the schematic diagram of the flare detection and feature extraction method. For a regular PC, it takes about 5 s and 1 s to execute the whole process for each high- resolution image and full-disk image, respectively, and it achieves near real-time processing. The method mainly consists of three steps: pre-processing, the flare segmenting process, and post-processing. In the following, the three steps are described in ...
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The post-flare loop is a typical dynamic process of solar flares. In the past, a manual method was used to recognize the strand structure of loops. With higher spatial resolving power, the post-flare loop consists of more bundles of strands. For fast and reliable post-flare-loop recognition, an automated post-flare-loop detection method applied to...
Citations
... Solar Demon (Kraaikamp and Verbeeck, 2015) uses SDO/AIA images to detect flares, dimming, and EUV waves by tracking the increase/decrease in intensity in regions that are identified by thresholds. Yang et al. (2018) and Pötzi, Veronig, and Temmer (2018) also used similar region-based methods on H α images for real-time flare detection. The RHESSI Flare Finder (Lin et al., 2002) searches for microflares as local maxima in the 6 -12 keV count-rate timelines. ...
Aditya-L1 is India’s first observatory-class solar space mission to study the Sun from the Lagrange L1 point. The Solar Ultra-Violet Imaging Telescope (SUIT) is one of the payloads onboard Aditya-L1. SUIT is an off-axis Ritchey–Chrétien (RC) telescope, which images the Sun onto a 4k×4k CCD covering a field-of-view of 1.5 R⊙ with a plate scale of \(0.7''\) pixel−1. One of the primary objectives of SUIT is to study the early evolution of solar flares with high temporal cadence in the near-UV wavelengths (200 – 400 nm). The SUIT onboard intelligence was developed to achieve this objective. The complete intelligence algorithm is divided into several sub-modules, each working on a specific aspect of intelligence. These are: the HEL1OS flare-trigger module: generates flare trigger using HEL1OS hard X-ray data, the flare-localization module: locates the flare on the SUIT full-disc images, the Region of Interest (RoI) tracking module: accounts for the shift in RoI coordinates caused by rotation of the Sun, auto-exposure control module: adjusts the exposure time depending upon the flare intensity for better contrast. In this article, these onboard-intelligence modules are explained in detail. The working principles of these modules are tested using available data from various existing missions and also using synthetic data, and the obtained results are presented. The modules are implemented in hardware using an Actel RTAX 2000S FPGA and are tested using a laboratory setup. From the testing, it is found that flares are successfully localized in a mean time of 40 seconds from the GOES soft X-ray catalog start time. Also, a temporal cadence of under three seconds for a single-filter flare RoI image is achieved.
... Solar Demon (Kraaikamp and Verbeeck, 2015) uses SDO/AIA images to detect flares, dimming and EUV waves by tracking increase/decrease in intensity in regions that are identified by thresholds. Yang et al. (2018) and Pötzi, Veronig, and Temmer (2018) also use similar region-based methods on H α images for real-time flare detection. The RHESSI Flare finder (Lin 2003) searches for microflares as local maxima in the 6 -12 keV count-rate timelines. ...
Aditya-L1 is India’s first observatory class Solar space mission to study the Sun from Lagrangian 1 position. The Solar Ultra-Violet Imaging Telescope (SUIT) is one of the payloads on-board Aditya-L1. SUIT is an off-axis Ritchey-Chretien (RC) telescope, which images the Sun on to a 4k x 4k CCD covering a field-of-view of 1.5Ro with a plate scale of 0.7'/pixel. One of the primary objectives of SUIT is to study the early evolution of solar flares with high time cadence in Near UV wavelength (200 nm – 400 nm). The SUIT on-board intelligence is developed to achieve this objective. The complete intelligence algorithm is divided into several sub-modules each working on a specific aspect of intelligence. These are, HEL1OS flare trigger module: generates flare trigger using HEL1OS hard X-ray data, flare localization module: locates flare on the SUIT full-disc images, Region of Interest (RoI) tracking module: accounts for shift in RoI coordinates caused due to rotation of the Sun, auto-exposure control module: adjusts the exposure time depending upon the flare intensity for better contrast. In this paper, these on-board intelligence modules are explained in detail. The working principles of these modules are tested using available data from various existing missions and also using synthesized data and the obtained results are presented. The modules are implemented in hardware using Actel RTAX 2000S FPGA and are tested using laboratory setup. From the testing, it is found that, flares are successfully localized in a mean time of 40 seconds from the GOES soft X-ray catalog start time. Also, a time cadence of under 3 seconds for a single filter flare RoI images is achieved.
... The post-flare-loop detection is carried out in the red-wing image, as shown in the right image of Figure 1. However, there are some dark points within the flaring region to be removed in the line-center image (Yang et al., 2018). This can be achieved by implementing ...
... As stated at the beginning of this section, post-flare loops have an arched shape with their feet forming the flaring region, so the blending orientation during the curve growing could be determined in advance to make the curve-growing results more reliable. The first step is to segment the flaring region from the Hα line-center image shown in the left image of Figure 1, and it could be achieved by the gray and area-threshold method (Yang et al., 2018). Owing to the same recording time and field-of-view, the segmented flaring area in the Hα line-center image could be labeled directly on the Hα red-wing image, which is the red area in Figure 3b. ...
The post-flare loop is a typical dynamic process of solar flares. In the past, a manual method was used to recognize the strand structure of loops. With higher spatial resolving power, the post-flare loop consists of more bundles of strands. For fast and reliable post-flare-loop recognition, an automated post-flare-loop detection method applied to high-resolution Hα\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\upalpha $\end{document} red-wing images is proposed in this article. In this method, straight lines are detected from the edge-detection result, and then a curve-growing procedure is carried out on the basis of the detected straight lines. Finally, the grown curves are screened to eliminate isolated and intersected curves. To our knowledge, our research is the first trial to detect post-flare loops on the solar disk. Experimental results with the high-resolution Hα\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\upalpha $\end{document} red-wing images from the Goode Solar Telescope (GST) have verified that the proposed method could detect post-flare loops effectively in all their developing phases. Based on the recognition results, the automatically computed loop widths (118±35 km) are consistent with the manually derived results (124±19 km). With the development of observing instrumentation, the proposed method may recognize each strand of the loops and the measured loop widths could be used to verify flare models.
Rapid and proficient data retrieval is an essential component of modern astronomical research. In this paper, we address the challenge of retrieving astronomical image content by leveraging state-of-the-art deep learning techniques. We have designed a retrieval model, HybridVR, that integrates the capabilities of the deep learning models ResNet50 and VGG16 and have used it to extract key features of solar activity and solar environmental characteristics from observed images. This model enables efficient image matching and allows for content-based image retrieval (CBIR). Experimental results demonstrate that the model can achieve up to 98% similarity during CBIR while exhibiting adaptability and scalability. Our work has implications for astronomical research, data management, and education, and it can contribute to optimizing the utilization of astronomical image data. It also serves as a useful example of the application of deep learning technology in the field of astronomy.
This paper presents a new approach for the regression of the center coordinates and radius of the solar disk in H solar full-disk images by using a Deep Convolutional Neural Network. We use ∼100,000 original H solar full-disk images obtained from Huairou Solar Observing Station as the experimental data set. The data set includes two parts: The original image and three numeric values (center coordinates and radius). In order to deal with the uneven distribution of the solar disk position in the original image, we randomly shift the solar disk during image preprocessing. Furthermore, data augmentation is also used to increase the robustness of the model. By evaluating the model with R-square and relative error, the center coordinates and the radius of the solar disk are proved to be effectively regressed. The data sets we constructed and source code are available as open source on GitHub. © 2020. The American Astronomical Society. All rights reserved.
