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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]

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]

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]

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]