Il-Hyun Cho

Quasiperiodic disturbances in EUV emission intensity, propagating along polar plumes in the solar corona, are frequently observed. Analysis of these propagating disturbances in the 171 Å bandpass of the Solar Dynamics Observatory/Atmospheric Imaging Assembly reveals that the wave spectrum evolves with height. Near the footpoints, the dominant spectral peak corresponds to oscillation periods of 12–15 minutes. As the disturbances propagate upward, the shorter-period spectral component diminishes, while a longer-period component of 20–30 minutes becomes dominant. Theoretical modeling of coronal slow magnetoacoustic waves effectively reproduces this spectral evolution. The model incorporates field-aligned thermal conduction and the back reaction of wave-induced perturbations on local thermal equilibrium. Coronal slow waves, driven by a broadband source with a peak at a shorter period, show a decrease in shorter-period spectral power with height, while the 20–30 minute spectral power increases due to thermal overstability. Analysis of the dispersion relations reveals that, given the chosen model parameters, the most rapidly growing spectral component corresponds to a 22.5 minute period, which aligns well with the observational findings.

We study properties of intensity disturbances along polar coronal rays that are associated with plumes below. For this, we draw azimuth–time images of extreme ultraviolet (EUV) emission of 171 Å band observed by the SDO/AIA and white light (WL) observed by the SOHO/LASCO C2 in 2020 July. From the azimuth–time image, we define two tracks in which the EUV intensities were recurrently enhanced during two weeks. The two EUV tracks are rooted at 78.°8 and 81.°4 latitudes, but their projected azimuth angles are changed with time as the Sun rotates. Coherent WL tracks at different altitudes are determined by scaling the azimuth angles of the EUV tracks, accounting for the effect of inclination of coronal rays. From this, we construct time–distance images of WL intensities along WL tracks, whose projected azimuth angle changes along time and altitude, but the intensities are correlated with the EUV intensities measured below. The time–distance images of WL show repeated and inclined intensity features. The propagation speeds in the altitude range 2.3–6 solar radii are calculated to be 159 ± 8 km s⁻¹ and 300 ± 24 km s⁻¹. The EUV and WL intensities are found to be coherent at 1–2 day periods. It is also found that dynamic burst events along the EUV track are responsible for the enhanced emission. We conclude that the variation of the WL intensity along the polar coronal rays is related with the evolution of the EUV intensity below.

Plumes are bright structures in coronal holes extending from the solar surface into the corona and are considered as a possible source of the solar wind. Plumes are thought to be rooted in strong unipolar photospheric flux patches (network/plage region). The magnetic activities at the base of plumes may play a crucial role in producing outflows and propagating disturbances (PDs). However, the role of photospheric/chromospheric activities (e.g., jets/spicules) at the base of plumes and their connection to PDs is poorly understood. Using high-resolution observations of a plume taken on 2020 July 23 with the 1.6 m Goode Solar Telescope (GST), Interface Region Imaging Spectrograph (IRIS), and the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory, we analyzed chromospheric/transition region activities at the base of the plume and their connection to outflows/PDs in the plume. The GST Visible Imaging Spectrometer images reveal repetitive spicules with blueshifted emission (pseudo-Doppler maps) at the plume’s footpoint. In addition, the photospheric magnetograms provide evidence of mixed polarities at the base of the plume. The IRIS Mg ii k Dopplergrams show strong blueshifted emission (∼50 km s⁻¹) and a high brightness temperature (Mg ii k2 line) at the footpoint of the plume. The long-period PDs (P ≈ 20–25 minutes) along the plume (AIA 171 Å) match the periodicity of spicules in the chromospheric images, suggesting a close connection between the spicules and the PDs. We suggest that the interchange reconnection between the closed and open flux of the coronal bright point at the plume’s footpoint is the most likely candidate to produce upflows and associated PDs along the plume.

In this study, we suggest a pixel-to-pixel image translation method among similar types of filtergrams such as solar extreme-ultraviolet (EUV) images. For this, we consider a deep-learning model based on a fully connected network in which all pixels of solar EUV images are independent of one another. We use six-EUV-channel data from the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO), of which three channels (17.1, 19.3, and 21.1 nm) are used as the input data and the remaining three channels (9.4, 13.1, and 33.5 nm) as the target data. We apply our model to representative solar structures (coronal loops inside of the solar disk and above the limb, coronal bright point, and coronal hole) in SDO/AIA data and then determine differential emission measures (DEMs). Our results from this study are as follows. First, our model generates three EUV channels (9.4, 13.1, and 33.5 nm) with average correlation coefficient values of 0.78, 0.89, and 0.85, respectively. Second, our model generates the solar EUV data with no boundary effects and clearer identification of small structures when compared to a convolutional neural network–based deep-learning model. Third, the estimated DEMs from AI-generated data by our model are consistent with those using only SDO/AIA channel data. Fourth, for a region in the coronal hole, the estimated DEMs from AI-generated data by our model are more consistent with those from the 50 frames stacked SDO/AIA data than those from the single-frame SDO/AIA data.

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).

The propagating intensity disturbances (PIDs) in plumes are still poorly understood, and their identity (magnetoacoustic waves or flows) remains an open question. We investigate PIDs in five plumes located in the northern polar coronal hole observed during the 2017 total solar eclipse. Three plumes are associated with coronal bright points, jets, and macrospicules at their base (active plumes), and the other two plumes are not (quiet plumes). The electron temperature at the base of the plumes is obtained from the filter ratio of images taken with the X-ray Telescope on board Hinode and the passband ratio around 400 nm from an eclipse instrument, the Diagnostic Coronagraph Experiment. The phase speed (v _r ), frequency (ω), and wavenumber (k) of the PIDs in the plumes are obtained by applying a Fourier transformation to the spacetime (r − t plane) plots in images taken with the Atmospheric Imaging Assembly (AIA) in three different wavelength channels (171, 193, and 211 Å). We found that the PIDs in the higher-temperature AIA channels, 193 and 211 Å, are faster than that of the cooler AIA 171 Å channel. This tendency is more significant for the active plumes than the quiet ones. The observed speed ratio (∼1.3) between the AIA 171 and 193 Å channels is similar to the theoretical value (1.25) of a slow magnetoacoustic wave. Our results support the idea that PIDs in plumes represent a superposition of slow magnetoacoustic waves and plasma outflows that consist of dense cool flows and hot coronal jets.

In this study, we suggest a new method to estimate the mass of a halo coronal mass ejection (CME) using synthetic CMEs. For this, we generate synthetic CMEs based on two assumptions: (1) the CME structure is a full ice cream cone, and (2) the CME electron number density follows a power-law distribution (ρ _cme = ρ ₀ r ⁻ⁿ ). The power-law exponent n is obtained by minimizing the rms error between the electron number density distributions of an observed CME and the corresponding synthetic CME at a position angle of the CME leading edge. By applying this methodology to 56 halo CMEs, we estimate two kinds of synthetic CME masses. One is a synthetic CME mass that considers only the observed CME region (M _cme1), the other is a synthetic CME mass that includes both the observed CME region and the occulted area (M _cme2). From these two cases, we derive conversion factors that are the ratio of a synthetic CME mass to an observed CME mass. The conversion factor for M _cme1 ranges from 1.4 to 3.0 and its average is 2.0. For M _cme2, the factor ranges from 1.8 to 5.0 with an average of 3.0. These results imply that the observed halo CME mass can be underestimated by about 2 times when we consider the observed CME region, and about 3 times when we consider the region including the occulted area. Interestingly these conversion factors have a very strong negative correlation with angular widths of halo CMEs.

Slow magnetoacoustic waves in a static background provide a seismological tool to probe the solar atmosphere in the analytic frame. By analyzing the spatiotemporal variation of the electron number density of plume structure in coronal holes above the limb for a given temperature, we find that the density perturbations accelerate with supersonic speeds in the distance range from 1.02 to 1.23 solar radii. We interpret them as slow magnetoacoustic waves propagating at about the sound speed with accelerating subsonic flows. The average sonic height of the subsonic flows is calculated to be 1.27 solar radii. The mass flux of the subsonic flows is estimated to be 44.1% relative to the global solar wind. Hence, the subsonic flow is likely to be the nascent solar wind. In other words, the evolution of the nascent solar wind in plumes at the low corona is quantified for the first time from imaging observations. Based on the interpretation, propagating density perturbations present in plumes could be used as a seismological probe of the gradually accelerating solar wind.

Image super-resolution is a technique of enhancing the resolution of an image where a high-resolution (HR) image is reconstructed from a low-resolution (LR) image. In this Letter, we apply two novel deep learning models (residual attention model and progressive GAN model) for enhancing Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) magnetograms. For this, we consider line-of-sight (LOS) magnetograms taken by SDO/HMI as output and their degraded ones with 4 × 4 binning as input. Deep learning networks try to find internal relationships between LR and HR images from the given input and the corresponding output image. We consider SDO/HMI magnetograms from 2014 May to August for training, from 2014 October to December for validation, and 2015 January to March for test. We find that the deep learning models generate higher-quality results than the bicubic interpolation in terms of visual aspects and metrics. We apply this model to a full-resolution SDO/HMI magnetogram and then compare the generated magnetogram with the corresponding Hinode/The Solar Optical Telescope Narrowband Filtergrams (NFI) magnetogram. This comparison shows that the generated magnetogram is consistent with the Hinode one with a high correlation (CC: 0.94) and a high similarity (SSIM: 0.93), which are better than the bicubic method.