Jin-Yi Lee

A flux rope eruption on 2017 September 10 provides unique observations of the plasma sheet beneath the rising flux rope. The plasma sheet is likely in a nonequilibrium state in terms of both ionization and the electron distribution function. We trace the evolution of a blob in the plasma sheet using observations from the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory. We investigate the heating of plasma sheet material in the presence of non-Maxwellian electron distributions and nonequilibrium ionization. Our models compute time-dependent ion fractions, incorporating impulsive heating to various peak temperatures, continuous heating rates, and κ values that represent the non-Maxwellian distribution. The statistically preferred models constrain the effective impulsive heating temperature to above 20 MK. High-temperature solutions are permitted only for very low κ values, indicating that suprathermal electrons play a significant role. Impulsive heating dominates the energy budget, with continuous heating contributing approximately 6%–50% of the initial impulsive energy input.

An important aspect of solar energetic particle (SEP) events is their source populations. Elemental abundance enhancements of impulsive SEP events, originating in presumed coronal reconnection episodes, can be fitted to steep power laws of A/Q, where A and Q are the atomic mass and ionic charge. Since thermal electron energies are enhanced and nonthermal electron distributions arise in the reconnection process, we might expect that ionic charge states Q would be increased through ionization interactions with those electron populations during the acceleration process. The temperature estimated from the SEPs corresponds to the charge state during the acceleration process, while the actual charge state measured in situ may be modified as the SEPs pass through the corona. We examine whether the temperature estimation from the A/Q would differ with various kappa values in a kappa function representing high-energy tail deviating from a Maxwellian velocity distribution. We find that the differences in the A/Q between a Maxwellian and an extreme kappa distribution are about 10-30%. We fit power-law enhancement of element abundances as a function of their A/Q with various kappa values. Then, we find that the derived source region temperature is not significantly affected by whether or not the electron velocity distribution deviates from a Maxwellian, i.e., thermal, distribution. Assuming that electrons are heated in the acceleration region, the agreement of the SEP charge state during acceleration with typical active region temperatures suggests that SEPs are accelerated and leave the acceleration region in a shorter time than the ionization time scale.

An important aspect of solar energetic particle (SEP) events is their source populations. Elemental abundance enhancements of impulsive SEP events, originating in presumed coronal reconnection episodes, can be fitted to steep power laws of A/Q, where A and Q are the atomic mass and ionic charge. Since thermal electron energies are enhanced and nonthermal electron distributions arise in the reconnection process, we might expect that ionic charge states Q would be increased through ionization interactions with those electron populations during the acceleration process. The temperature estimated from the SEPs corresponds to the charge state during the acceleration process, while the actual charge state measured in situ may be modified as the SEPs pass through the corona. We examine whether the temperature estimation from the A/Q would differ with various κ values in a κ function representing high-energy tail deviating from a Maxwellian velocity distribution. We find that the differences in the A/Q between a Maxwellian and an extreme κ distribution are about 10%–30%. We fit power-law enhancement of element abundances as a function of their A/Q with various κ values. Then, we find that the derived source region temperature is not significantly affected by whether or not the electron velocity distribution deviates from a Maxwellian, i.e., thermal, distribution. Assuming that electrons are heated in the acceleration region, the agreement of the SEP charge state during acceleration with typical active region temperatures suggests that SEPs are accelerated and leave the acceleration region in a shorter time than the ionization timescale.

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.

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).

우주기술과 응용(Journal of Space Technology and Applications; J. Space Technol. Appl. 이하 JSTA )은 한국의 우주과학 및 우주 응용 분야에서 가장 권위 있는 학술단체인 한국우주과학회에서 Journal of Astronomy and Space Sciences 와 더불어 발행되고 있는 공식적인 정기 간행물이다. JSTA 는 2021년 5월 창간호를 시작으로 연 4회 분기별로 발간되고 있으며, JSTA 의 논문과 기고문 은 한국어 또는 영어로 게재 가능하며, 각 논문과 기고문에는 고유의 DOI가 부여된다. JSTA 에 게재 되는 모든 논문과 기고문은 Open Access 정책에 따라 JSTA 홈페이지(https://www.jstna.org)를 통해 누구나 전문을 무료로 열람할 수 있다.

We address the question of which combination of channels can best translate other channels in ultraviolet (UV) and extreme UV (EUV) observations. For this, we compare the image translations among the nine channels of the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO) using a deep-learning (DL) model based on conditional generative adversarial networks. In this study, we develop 170 DL models: 72 models for single-channel input, 56 models for double-channel input, and 42 models for triple-channel input. All models have a single-channel output. Then we evaluate the model results by pixel-to-pixel correlation coefficients (CCs) within the solar disk. Major results from this study are as follows. First, the model with 131 Å shows the best performance (average CC = 0.84) among single-channel models. Second, the model with 131 and 1600 Å shows the best translation (average CC = 0.95) among double-channel models. Third, among the triple-channel models with the highest average CC (0.97), the model with 131, 1600, and 304 Å is suggested in that the minimum CC (0.96) is the highest. Interestingly, they represent coronal, upper photospheric, and chromospheric channels, respectively. Our results may be used as a secondary perspective in addition to primary scientific purposes in selecting a few channels of an UV/EUV imaging instrument for future solar satellite missions.

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.