Solar Images Research Articles

Image Feature Extraction and Matching of Augmented Solar Images in Space Weather

Image Feature Extraction and Matching of Augmented Solar Images in Space Weather

Self-Supervised Learning for Solar Radio Spectrum Classification

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.

The physics of solar spectral imaging observations in dm-cm wavelengths and the application on space weather

Recently, several new solar radio telescopes have been put into operation and provided spectral-imaging observations with much higher resolutions in decimeter (dm) and centimeter (cm) wavelengths. These telescopes include the Mingantu Spectral Radioheliograph (MUSER, at frequencies of 0.4–15 GHz), the Expanded Owens Valley Solar Array (EOVSA, at frequencies of 1–18 GHz), and the Siberian Radio Heliograph (SRH, at frequencies of 3–24 GHz). These observations offer unprecedented opportunities to study solar physics and space weather, especially to diagnose the coronal magnetic fields, reveal the basic nature of solar eruptions and the related non-thermal energy release, particle accelerations and propagation, and the related emission mechanisms. These results might be the important input to the space weather modeling for predicting the occurrence of disastrous powerful space weather events. In order to provide meaningful reference for other solar physicists and space weather researchers, this paper mainly focus on discussing the potential scientific problems of solar radio spectral-imaging observations in dm-cm wavelengths and its possible applications in the field of space weather. These results will provide a helpful reference for colleagues to make full use of the latest and future observation data obtained from the above solar radio telescopes.

Modeling Solar Eclipses at Extreme Ultra Violet Wavelengths and the Effects of Nonuniform Eclipse Shadow on the Ionosphere‐Thermosphere System

AbstractThe impacts of solar eclipses on the ionosphere‐thermosphere system particularly the composition, density, and transport are studied using numerical simulation and subsequent model‐data comparison. We introduce a newly developed model of a solar eclipse mask (shadow) at extreme ultraviolet (EUV) wavelengths—PyEclipse—that computes the corresponding shadowing as a function of space, time, and wavelength of the input solar image. The current model includes interfaces for Solar Dynamics Observatory and Geostationary Operational Environmental Satellites EUV telescopes providing solar images at nine different wavelengths. We show the significance of the EUV eclipse shadow spatial variability and that it varies significantly with wavelength owing to the highly variable solar coronal emissions. We demonstrate geometrical differences between the EUV eclipse shadow compared to a geometrically symmetric simplification revealing changes in occultation vary ±20%. The EUV eclipse mask is validated with in situ solar flux measurements by the PRoject for Onboard Autonomy 2/Large Yield Radiometer instrument suite showing the model captures the morphology and amplitudes of transient variability while the modeled gradients are slower. The effects of spatially EUV eclipse masks are investigated with Global Ionosphere Thermosphere Model for the 21 August 2017 eclipse. The results reveal that the modeled EUV eclipse mask, in comparison with the geometrically symmetric approximation, causes changes in the Total Electron Content in order of ±20%, 5%–20% in F‐region plasma drift, and 20%–30% in F‐region neutral winds.

Automated Sunspot Detection as an Alternative to Visual Observations

We developed an automated method for sunspot detection using digital white-light solar images to achieve a performance similar to that of visual drawing observations in sunspot counting. To identify down to small, isolated spots correctly, we pay special attention to the accurate derivation of the quiet-disk component of the Sun, which is used as a reference to identify sunspots using a threshold. This threshold is determined using an adaptive method to process images obtained under various conditions. To eliminate the seeing effect, our method can process multiple images taken within a short time. We applied the developed method to digital images captured at three sites and compared the detection results with those of visual observations. We conclude that the proposed sunspot detection method has a similar performance to that of visual observation. This method can be widely used by public observatories and amateurs as well as professional observatories as an alternative to hand-drawn visual observation for sunspot counting.

Forward fitting STIX visibilities

Aims. We seek to determine to what extent the problem of forward fitting visibilities measured by the Spectrometer/Telescope Imaging X-rays (STIX) on board Solar Orbiter becomes more challenging with respect to the same problem in the case of previous hard X-ray solar imaging missions. In addition, we aim to identify an effective optimization scheme for parametric imaging for STIX. Methods. This paper introduces a global search optimization for forward-fitting STIX visibilities and compares its effectiveness with respect to the standard simplex-based optimization used so far for the analysis of visibilities measured by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI). We made this comparison by considering experimental visibilities measured by both RHESSI and STIX, as weel as synthetic visibilities generated by accounting for the STIX signal formation model. Results. We found that among the three global search algorithms for parametric imaging, particle swarm optimization (PSO) exhibits the best performances in terms of both stability and computational effectiveness. This method is as reliable as the simplex method in the case of RHESSI visibilities. However, PSO is significantly more robust when applied to STIX simulated and experimental visibilities. Conclusions. A standard optimization based on local search of minima is not effective enough for forward-fitting the few visibilities sampled by STIX in the spatial frequency plane. Therefore, more sophisticated optimization schemes based on global search must be introduced for parametric imaging in the case of the Solar Orbiter X-ray telescope. The forward-fitting routine based on PSO proved to be significantly robust and reliable, and it could be considered as an effective candidate tool for parametric imaging in the STIX context.

Rome Precision Solar Photometric Telescope: precision solar full-disk photometry during solar cycles 23–25

The Rome Precision Solar Photometric Telescope (Rome/PSPT) is a ground-based telescope engaged in precision solar photometry. It has a 27-year database of full-disk images of the photosphere and chromosphere beginning in 1996 and continuing to 2022. The solar images have been obtained daily, weather permitting, with approximately 2 arcsec/pixel scale in Ca II K line at 393.3 nm, G-band at 430.6 nm, and continuum in the blue and red parts of the spectrum at 409.4 nm and 607.2 nm, respectively. Regular observations were also performed at the green continuum at 535.7 nm for a period of about 18 months. Since the first-light, Rome/PSPT operations have been directed at understanding the source of short-and long-term solar irradiance changes, spanning from 1 min to several months, and from 1 year to a few solar cycles, respectively. However, Rome/PSPT data have also served to study a variety of other topics, including the photometric properties of solar disk features and of the supergranulation manifested by the chromospheric network. Moreover, they have been unique in allowing to connect series of historical and modern full-disk solar observations, especially the Ca II K line data. Here, we provide an overview of the Rome/PSPT telescope and of the solar monitoring carried out with it from its first light to the present, across solar cycles 23–25. We also briefly describe the main results achieved with Rome/PSPT data, and give an overview of new results being derived with the whole time series of observations covering the period 1996–2022.

Geological, multispectral, and meteorological imaging results from the Mars 2020 Perseverance rover in Jezero crater.

Perseverance's Mastcam-Z instrument provides high-resolution stereo and multispectral images with a unique combination of spatial resolution, spatial coverage, and wavelength coverage along the rover's traverse in Jezero crater, Mars. Images reveal rocks consistent with an igneous (including volcanic and/or volcaniclastic) and/or impactite origin and limited aqueous alteration, including polygonally fractured rocks with weathered coatings; massive boulder-forming bedrock consisting of mafic silicates, ferric oxides, and/or iron-bearing alteration minerals; and coarsely layered outcrops dominated by olivine. Pyroxene dominates the iron-bearing mineralogy in the fine-grained regolith, while olivine dominates the coarse-grained regolith. Solar and atmospheric imaging observations show significant intra- and intersol variations in dust optical depth and water ice clouds, as well as unique examples of boundary layer vortex action from both natural (dust devil) and Ingenuity helicopter-induced dust lifting. High-resolution stereo imaging also provides geologic context for rover operations, other instrument observations, and sample selection, characterization, and confirmation.

Solar X-ray and EUV imager on board the FY-3E satellite

The solar X-ray and Extreme Ultraviolet Imager (X-EUVI), developed by the Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (CIOMP), is the first space-based solar X-ray and Extreme ultraviolet (EUV) imager of China loaded on the Fengyun-3E (FY-3E) satellite supported by the China Meteorological Administration (CMA) for solar observation. Since started work on July 11, 2021, X-EUVI has obtained many solar images. The instrument employs an innovative dual-band design to monitor a much larger temperature range on the Sun, which covers 0.6–8.0 nm in the X-ray region with six channels and 19.5 nm in the EUV region. X-EUVI has a field of view of 42′, an angular resolution of 2.5″ per pixel in the EUV band and an angular resolution of 4.1″ per pixel in the X-ray band. The instrument also includes an X-ray and EUV irradiance sensor (X-EUVS) with the same bands as its imaging optics, which measures the solar irradiance and regularly calibrates the solar images. The radiometric calibration of X-EUVS on the ground has been completed, with a calibration accuracy of 12%. X-EUVI is loaded on the FY-3E satellite and rotates relative to the Sun at a uniform rate. Flat-field calibration is conducted by utilizing successive rotation solar images. The agreement between preliminarily processed X-EUVI images and SDO/AIA and Hinode/XRT images indicates that X-EUVI and the data processing algorithm operate properly and that the data from X-EUVI can be applied to the space weather forecast system of CMA and scientific investigations on solar activity.

An Algorithm to Extract the Boundary and Center of EUV Solar Image Based on Sobel Operator and FLICM

An algorithm to extract the disk boundary and center of EUV solar image using the Sobel operator, Fuzzy Local Information C-Means Clustering algorithm (FLICM), and the least square circle fitting method is proposed in this paper. The Sobel operator can determine the solar disk boundary preliminarily, and then the image is processed further using the FLICM algorithm. After the background is removed based on the clustered image and the boundary points can be highlighted, these points are fitted using the least square circle fitting method as the final boundary circle. The solar data used in this paper was from the observation of the Solar Dynamics Observatory Atmospheric Imaging Assembly (SDO/AIA) instrument. The 2523 19.3 nm solar images covering solar minimum, moderate solar activity, and more active suns were calculated using the proposed algorithm to analyze the accuracy statistically. The statistical comparison results demonstrate that the method is accurate and effective. This method can support the processing of solar EUV images and serve the operational system of a space weather forecast.

Improving the Spatial Resolution of Solar Images Based on an Improved Conditional Denoising Diffusion Probability Model

The quality of solar images plays an important role in the analysis of small events in solar physics. Therefore, the improvement of image resolution based on super-resolution (SR) reconstruction technology has aroused the interest of many researchers. In this paper, an improved conditional denoising diffusion probability model (ICDDPM) based on the Markov chain is proposed for the SR reconstruction of solar images. This method reconstructs high-resolution (HR) images from low-resolution images by learning a reverse process that adds noise to HR images. To verify the effectiveness of the method, images from the Goode Solar Telescope at the Big Bear Solar Observatory and the Helioseismic and Magnetic Imager (HMI) on the Solar Dynamics Observatory are used to train a network, and the spatial resolution of reconstructed images is 4 times that of the original HMI images. The experimental results show that the performance based on ICDDPM is better than the previous work in subject judgment and object evaluation indexes. The reconstructed images of this method have higher subjective vision quality and better consistency with the HMI images. And the structural similarity and rms index results are also higher than the compared method, demonstrating the success of the resolution improvement using ICDDPM.

Efficient and Refined Deep Convolutional Features Network for the Crack Segmentation of Solar Cell Electroluminescence Images

High-quality and fast crack segmentation in solar cell electroluminescence images with heterogeneously textured background disturbance is challenging. We propose an end-to-end Efficient and Refined Deep Convolutional Features Network (ERDCF-Net) for precise and efficient crack segmentation. Firstly, we design a lightweight, efficient deep convolutional features (EDCF) encoder by adopting multilevel deep convolutional features to generate a discriminative feature representation of cracks. Moreover, some complicated background disturbance is restrained by reusing rich crack and background features. Secondly, we present a refined side output (RS) decoder by automatically refining side outputs from the EDCF encoder. The experiments demonstrate that the proposed network excellently performs on the solar cell cracks (MIoU of 92.82%, F - measure of 93.58%, and 89 FPS) and open crack datasets (MIoU of 85.6%, F - measure of 84.1%, and 90 FPS).

Optimal Defocus for Phase Diversity Wave Front Retrieval

Phase diversity techniques are widely employed in solar astronomy to evaluate and correct the aberrations stemming from atmospheric turbulences, the telescope, and its instruments. The method uses information provided by a pair of images. One of them is usually focused while the other one is defocused. The amount of defocus to be induced is somehow arbitrary, though. In this work we carry out a series of numerical experiments with artificial solar images to investigate the performance of phase diversity for different choices of the relative defocus among the two images. The experiments allow us to determine the amount of defocus that produces the best wave front restoration when changing: (1) the number of Zernike polynomials of the retrieved and/or incident wave fronts; (2) the signal-to-noise ratio of the images; (3) the amplitude of the incident aberrations; and (4) the observed scene. We find a correlation between the amount of defocus needed for optimal restorations and the number of Zernike polynomials employed in the optimization. Values larger than the typically accepted choice of 1λ (peak to peak) are obtained in most cases.

Modeling of Solar Wind Disturbances Associated with Coronal Mass Ejections and Verification of the Forecast Results

Solar wind (SW) disturbances associated with coronal mass ejections (CMEs) cause significant geomagnetic storms, which may lead to the malfunction or damage of sensitive on-ground and space-based critical infrastructure. CMEs are formed in the solar corona, and then propagate to the Earth through the heliosphere as Interplanetary CME (ICME) structures. We describe the main principles in development with the online, semi-empirical system known as the Space Monitoring Data Center (SMDC) of the Moscow State University, which forecasts arrival of ICMEs to Earth. The initial parameters of CMEs (speeds, startup times, location of the source) are determined using data from publicly available catalogs based on solar images from space telescopes and coronagraphs. After selecting the events directed to Earth, the expected arrival time and speed of ICMEs at the L1 point are defined using the Drag-Based model (DBM), which describes propagation of CMEs through the heliosphere under interaction with the modeled quasi-stationary SW. We present the test results of the ICME forecast in the falling phase of Cycle 24 obtained with the basic version of SMDC in comparison with results of other models, its optimization and estimations of the confidence intervals, and probabilities of a successful forecast.

The flat-field method based on rotated images for FY-3E/X-EUVI

An algorithm to calculate the flat-field coefficient based on the series of rotated images captured by the solar X-ray and Extreme Ultraviolet Imager (X-EUVI) onboard the Fengyun-3E satellite is proposed in this article. The method includes determination of the solar disk centers and radiuses, calculation of the rotation angles, coordinate transformation to expand the solar disk into rectangles, and derivation of the flat matrix using the KLL algorithm. The accuracy of determination of the solar disk center and radius tested by the Hough gradient method and the least-squares method is at sub-pixel, and the precision of the calculated rotation angle based on the log-polar transform is less than 0.025°. Since the X-EUVI rotates relative to the Sun in real time, multiple rotating images can be obtained and used for flat-field calibration at any time, and the tested accuracy is estimated at 0.79–3.42%. This flat-field method will provide reference and support for solar image processing and research.

SUTO-Solar Through-Turbulence Open Image Dataset.

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.

Predicting CME arrival time through data integration and ensemble learning

The Sun constantly releases radiation and plasma into the heliosphere. Sporadically, the Sun launches solar eruptions such as flares and coronal mass ejections (CMEs). CMEs carry away a huge amount of mass and magnetic flux with them. An Earth-directed CME can cause serious consequences to the human system. It can destroy power grids/pipelines, satellites, and communications. Therefore, accurately monitoring and predicting CMEs is important to minimize damages to the human system. In this study we propose an ensemble learning approach, named CMETNet, for predicting the arrival time of CMEs from the Sun to the Earth. We collect and integrate eruptive events from two solar cycles, #23 and #24, from 1996 to 2021 with a total of 363 geoeffective CMEs. The data used for making predictions include CME features, solar wind parameters and CME images obtained from the SOHO/LASCO C2 coronagraph. Our ensemble learning framework comprises regression algorithms for numerical data analysis and a convolutional neural network for image processing. Experimental results show that CMETNet performs better than existing machine learning methods reported in the literature, with a Pearson product-moment correlation coefficient of 0.83 and a mean absolute error of 9.75 h.

Forecasting the Geomagnetic Activity Several Days in Advance Using Neural Networks Driven by Solar EUV Imaging

AbstractMany models of the near‐Earth's space environment (radiation belts, ionosphere, upper atmosphere, etc.) are driven by geomagnetic indices, representing the state of disturbance of the Earth's magnetosphere. Over the past decade, machine learning‐based methods for forecasting geomagnetic indices from near‐Earth solar wind parameters have become popular in the space weather community. These methods often prove to be very accurate and skilled. However, these approaches have the notable drawback of being effective in an operational context only for limited forecasting horizons (often up to a couple of hours ahead at best). In order to increase this prediction horizon, we introduce SERENADE, a novel deep learning‐based proof‐of‐concept model using images delivered by the Atmospheric Imaging Assembly instrument onboard the Solar Dynamics Observatory spacecraft to directly provide probabilistic forecasts of the daily maximum of the geomagnetic index Kp up to a few days ahead. We show in particular that SERENADE is able to capture information on the geomagnetic dynamics from solar imaging alone. In addition, despite it being a prototypical model, our model is more accurate in most situations than three empirical baseline models. However, the model still shows some strong limitations inherent to its structure and the used data set, which could be the focus of future works. This opens the way to a better mid‐to‐long term data‐driven magnetospheric modeling within space weather and geophysical pipelines.

Exploring the Limits of Synthetic Creation of Solar EUV Images via Image-to-image Translation

The Solar Dynamics Observatory (SDO), a NASA multispectral decade-long mission that has been daily producing terabytes of observational data from the Sun, has been recently used as a use case to demonstrate the potential of machine-learning methodologies and to pave the way for future deep space mission planning. In particular, the idea of using image-to-image translation to virtually produce extreme ultraviolet channels has been proposed in several recent studies, as a way to both enhance missions with fewer available channels and to alleviate the challenges due to the low downlink rate in deep space. This paper investigates the potential and the limitations of such a deep learning approach by focusing on the permutation of four channels and an encoder–decoder based architecture, with particular attention to how morphological traits and brightness of the solar surface affect the neural network predictions. In this work we want to answer the question: can synthetic images of the solar corona produced via image-to-image translation be used for scientific studies of the Sun? The analysis highlights that the neural network produces high-quality images over 3 orders of magnitude in count rate (pixel intensity) and can generally reproduce the covariance across channels within a 1% error. However, the model performance drastically diminishes in correspondence to extremely high energetic events like flares, and we argue that the reason is related to the rareness of such events posing a challenge to model training.

Generation of Solar Coronal White-light Images from SDO/AIA EUV Images by Deep Learning

Low coronal white-light observations are very important to understand low coronal features of the Sun, but they are rarely made. We generate Mauna Loa Solar Observatory (MLSO) K-coronagraph like white-light images from the Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) EUV images using a deep learning model based on conditional generative adversarial networks. In this study, we used pairs of SDO/AIA EUV (171, 193, and 211 Å) images and their corresponding MLSO K-coronagraph images between 1.11 and 1.25 solar radii from 2014 to 2019 (January to September) to train the model. For this we made seven (three using single channels and four using multiple channels) deep learning models for image translation. We evaluate the models by comparing the pairs of target white-light images and those of corresponding artificial intelligence (AI)–generated ones in October and November. Our results from the study are summarized as follows. First, the multiple channel AIA 193 and 211 Å model is the best among the seven models in view of the correlation coefficient (CC = 0.938). Second, the major low coronal features like helmet streamers, pseudostreamers, and polar coronal holes are well identified in the AI-generated ones by this model. The positions and sizes of the polar coronal holes of the AI-generated images are very consistent with those of the target ones. Third, from AI-generated images we successfully identified a few interesting solar eruptions such as major coronal mass ejections and jets. We hope that our model provides us with complementary data to study the low coronal features in white light, especially for nonobservable cases (during nighttime, poor atmospheric conditions, and instrumental maintenance).