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Coronal loops are bright, filamentary structures formed by thermal plasmas constrained by the sun's magnetic field. Studying coronal loops provides insights into magnetic fields and their role in coronal heating processes. We propose a new automatic coronal loop detection method to optimize the problem of existing algorithms in detecting low-intensity coronal loops. Our method employs a line-Gaussian filter to enhance the contrast between coronal loops and background pixels, facilitating the detection of low-intensity ones. Following the detection of coronal loops, each loop is extracted using a method based on approximate local direction. Compared with the classical automatic detection method, Oriented Coronal Curved Loop Tracing (OCCULT), and its improved version, OCCULT-2, the proposed method demonstrates superior accuracy and completeness in loop detection. Furthermore, testing with images from the Transition Region and Coronal Explorer (TRACE) at 173 Å, the Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO) at 193 Å, and the High-Resolution Coronal Imager (Hi-C) at 193 Å and 172 Å confirms the robust generalization capabilities of our method. Statistical analysis of the cross-section width of coronal loops shows that most of the loop widths are resolved in Hi-C images. [ABSTRACT FROM AUTHOR]

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Coronal loops are bright, filamentary structures formed by thermal plasmas constrained by the sun’s magnetic field. Studying coronal loops provides insights into magnetic fields and their role in coronal heating processes. We propose a new automatic coronal loop detection method to optimize the problem of existing algorithms in detecting low-intensity coronal loops. Our method employs a line-Gaussian filter to enhance the contrast between coronal loops and background pixels, facilitating the detection of low-intensity ones. Following the detection of coronal loops, each loop is extracted using a method based on approximate local direction. Compared with the classical automatic detection method, Oriented Coronal Curved Loop Tracing (OCCULT), and its improved version, OCCULT-2, the proposed method demonstrates superior accuracy and completeness in loop detection. Furthermore, testing with images from the Transition Region and Coronal Explorer (TRACE) at 173 Å, the Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO) at 193 Å, and the High-Resolution Coronal Imager (Hi-C) at 193 Å and 172 Å confirms the robust generalization capabilities of our method. Statistical analysis of the cross-section width of coronal loops shows that most of the loop widths are resolved in Hi-C images.

Due to the massive increase in astronomical images (such as James Web, Solar Dynamic Observatory, and Solar Orbiter), automatic image description is essential for solar and astronomical. Zernike moments (ZMs) are unique due to the orthogonality and completeness of Zernike polynomials (ZPs); hence, ZMs are valuable for converting a two-dimensional image to a one-dimensional series of complex numbers. The magnitude of ZMs is rotation invariant, and by applying image normalization, scale and translation invariants can be made, which are helpful properties for describing solar and astronomical images. The lower-order ZMs express the overall shape of the objects of an image, and the higher-order ZMs provide more details of the objects and delicate structures within an image. In this Python package, available at GitHub and PyPI, we describe the characteristics of ZMs via several examples of solar (large and small scale) features, astronomical, and human face images. These independent and unique properties of ZMs can describe the structure and morphology of objects in an image. Hence, ZMs are helpful in machine learning to identify and track the features of several. [ABSTRACT FROM AUTHOR]

Imaging through turbulence has been the subject of many research papers in a variety of fields, including defence, astronomy, earth observations, and medicine. The main goal of such research is usually to recover the original, undisturbed image, in which the impact of spatially dependent blurring induced by the phase modulation of the light wavefront is removed. The number of turbulence-disturbed image databases available online is small, and the datasets usually contain repeating types of ground objects (cars, buildings, ships, chessboard patterns). In this article, we present a database of solar images in widely varying turbulence conditions obtained from the SUTO-Solar patrol station recorded over a period of more than a year. The dataset contains image sequences of distinctive yet randomly selected fragments of the solar chromosphere and photosphere. Reference images have been provided with the data using computationally intensive image recovery with the latest multiframe blind deconvolution technique, which is widely accepted in solar imaging. The presented dataset will be extended in the next few years as new image sequences are routinely acquired each sunny day at the SUTO-Solar station. [ABSTRACT FROM AUTHOR]

We present a new model for the probability that the disturbance storm time (Dst) index exceeds −100 nT, with a lead time between 1 and 3 days. Dst provides essential information about the strength of the ring current around the Earth caused by the protons and electrons from the solar wind, and it is routinely used as a proxy for geomagnetic storms. The model is developed using an ensemble of Convolutional Neural Networks that are trained using Solar and Heliospheric Observatory (SoHO) images (Michelson Doppler Imager, Extreme ultraviolet Imaging Telescope, and Large Angle and Spectrometric Coronagraph). The relationship between the SoHO images and the solar wind has been investigated by many researchers, but these studies have not explicitly considered using SoHO images to predict the Dst index. This work presents a novel methodology to train the individual models and to learn the optimal ensemble weights iteratively, by using a customized class‐balanced mean square error (CB‐MSE) loss function tied to a least‐squares based ensemble. The proposed model can predict the probability that Dst < −100 nT 24 hr ahead with a True Skill Statistic (TSS) of 0.62 and Matthews Correlation Coefficient (MCC) of 0.37. The weighted TSS and MCC is 0.68 and 0.47, respectively. An additional validation during non‐Earth‐directed CME periods is also conducted which yields a good TSS and MCC score. Plain Language Summary: Geomagnetic storms pose one of the most severe space weather risks to our space borne and ground‐based electronic instruments, such as GNSS and radio transmission systems. Dst is one of the most accurate geomagnetic storm indicators. Hence, those storm can be predictable if Dst can be forecasted. Currently, the best Dst model can only predict Dst in several hours. In this study, we present a machine learning based ensemble method to predict the Dst 1–3 days in advance from solar images. Key Points: A new disturbance storm time (Dst) probability model developed from Solar and Heliospheric Observatory (SoHO) iwmages using Convolutional Neural Networks with a least‐squares based ensemble technique is proposedThe proposed model can well forecast Dst probability at least 1 day ahead during strong storm periodsThe proposed model can capture the signature of strong storm events from SoHO images [ABSTRACT FROM AUTHOR]

We analyze the multiconjugate adaptive-optics (MCAO) systems and identify the specific features of the conjugation of adaptive mirrors with turbulent layers for ground-based solar telescopes. The optimal size of the field of view for a solar telescope operating under average atmospheric conditions is calculated to be 10 arcsec. Recommendations are given for the development of MCAO systems for ground-based solar telescopes. We suggested the concept of a system for determining 3D wavefront distortions for the Large Solar Telescope LST-3, as well as for the contour of recording phase distortions in the Large Solar Vacuum Telescope. [ABSTRACT FROM AUTHOR]

Imaging through turbulence has been the subject of many research papers in a variety of fields, including defence, astronomy, earth observations, and medicine. The main goal of such research is usually to recover the original, undisturbed image, in which the impact of spatially dependent blurring induced by the phase modulation of the light wavefront is removed. The number of turbulence-disturbed image databases available online is small, and the datasets usually contain repeating types of ground objects (cars, buildings, ships, chessboard patterns). In this article, we present a database of solar images in widely varying turbulence conditions obtained from the SUTO-Solar patrol station recorded over a period of more than a year. The dataset contains image sequences of distinctive yet randomly selected fragments of the solar chromosphere and photosphere. Reference images have been provided with the data using computationally intensive image recovery with the latest multiframe blind deconvolution technique, which is widely accepted in solar imaging. The presented dataset will be extended in the next few years as new image sequences are routinely acquired each sunny day at the SUTO-Solar station.

The paper presents the results from a multi-year effort to develop and validate image processing methods for selecting the best physical models based on solar image observations. The approach consists of selecting the physical models based on their agreement with coronal holes extracted from the images. Ultimately, the goal is to use physical models to predict geomagnetic storms. We decompose the problem into three subproblems: (i) coronal hole segmentation based on physical constraints, (ii) matching clusters of coronal holes between different maps, and (iii) physical map classification. For segmenting coronal holes, we develop a multi-modal method that uses segmentation maps from three different methods to initialize a level-set method that evolves the initial coronal hole segmentation to the magnetic boundary. Then, we introduce a new method based on Linear Programming for matching clusters of coronal holes. The final matching is then performed using Random Forests. The methods were carefully validated using consensus maps derived from multiple readers, manual clustering, manual map classification, and method validation for 50 maps. The proposed multi-modal segmentation method significantly outperformed SegNet, U-net, Henney-Harvey, and FCN by providing accurate boundary detection. Overall, the method gave a 95.5% map classification accuracy. [ABSTRACT FROM AUTHOR]

Modern methods for altitude profiling of atmospheric turbulence are discussed. A method for estimating the altitudes of atmospheric layers characterized by strong turbulence, which is an alternative to the Slodar method, is suggested. The method is based on estimation of the spatiotemporal cross-covariances between local wavefront slopes on subapertures spaced in the telescope field of view and a known angular shift of an object observed due to the Sun motion. Turbulence characteristics are estimated for the Big Solar Vacuum Telescope site. [ABSTRACT FROM AUTHOR]

The Solar Dynamics Observatory (SDO) mission captures thousands of images of the Sun per day, motivating the need for efficient and effective storage, representation, and search over a massive repository of data. This work investigates the general-purpose image parameter data produced by the SDO Feature Finding Team’s trainable module, which operates at a fixed six minute cadence over all AIA channels. The data contains ten numerical measures computed for each image cell over a 64 × 64 grid for each image. We analyze all available data and metadata produced over the first three years and present comprehensive statistics and outliers while validating the cleanliness and usability of the data source for future research. We then utilize a database of automated solar event reports to create large-scale region-labeled datasets available to the public. We highlight the new-found potential for data-driven discovery by presenting several best-case labeling scenarios that establish a baseline for comparing machine learning classification and attribute (image parameter) evaluation results. Future work focuses on continued dataset curation and spatiotemporal data mining. [ABSTRACT FROM AUTHOR]

Large-scale data mining is often aided with graphic visualizations to facilitate a better understanding of the data and results. This is especially true for visual data and highly detailed data too complex to be easily understood in raw forms. In this work, we present several of our recent interdisciplinary works in data mining solar image repositories and discuss the over-arching need for effective visualizations of data, metadata, and results along the way. First, we explain the complex characteristics and overwhelming abundance of image data being produced by NASA’s Solar Dynamics Observatory (SDO). Then we discuss the wide scope of solar data mining and highlight visual results from work in data labeling, classification, and clustering. Lastly, we present an overview of the first-ever Content-Based Image Retrieval (CBIR) system for solar images, and conclude with a brief look at the direction of our future research. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we make an overview of a methodology for the automatic retrieval of images with coronal loops from the solar image data captured by the extreme-ultraviolet imaging telescope (EIT) onboard the spacecraft SOHO (Solar and Heliospheric Observatory). Our image retrieval system provides relevant data to astrophysicists who need such data to study the coronal heating problem. As part of building this system, we investigated various image preprocessing techniques, image based features, and classifiers to automatically detect coronal loops and to indicate their locations on the images. Despite many challenges related to the coronal loop characteristic, we obtained promising results, namely, 78% precision and 80% recall in loop retrieval. [Copyright &y& Elsevier]