• 천문학회보
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• 제38권1호
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• pp.65.1-65.1
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• 2013

The 2011 August 09 Flare is one of the largest X-ray flares of Sunspot Cycle 24 to attract a lot of attention for its various activities detected in coronal images. In this study we concern ourselves mostly on information of high energy electrons produced during this flare provided by hard X ray data from the Reuven Ramaty High-Energy Solar Spectroscopic Imager (RHESSI) and radio data from the Korean Solar Radio Burst Locator (KSRBL) and Ondrejov. EUV images obtained by the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamic Observatory are used to provide the context of magnetic reconnection. In our results, (1) HXR spectra have a rich spectral morphology. Initially it could be fit by one thermal component (T~30MK) and one single power law nonthermal spectrum, but later a better fit could be made by introducing an additional thermal component (T~55 MK). (2) Time delays between the KSRBL burst and the RHESSI hard X-ray emission were found which are more obvious at low frequencies and insignificant at high frequencies. (3) The HXR source lies in the core of the quadrupolar active region. In our interpretation based on AIA 94 A images, the outer part of the active region erupted to be blown out, leaving the intense hard X-ray emission concentrated in the core. We relate the appearance of the second thermal component to the evolution of the AIA 171 and 94 A images. The time delays of microwave peaks to HXR peaks are interpreted as indicating presence of trapped electrons in larger closed magnetic loops. With these result we conclude that the hard X ray and microwaves are due to impulsive acceleration in the low and high heights and a sigmoidal reconnection scenario.

• 천문학회보
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• 제41권1호
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• pp.82-82
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• 2016

To find the relationship between solar flares and halo CMEs using stereoscopic observations, we investigate 182 flare-associated halo CMEs among 306 front-side halo CMEs from 2009 to 2013. We have determined the 3D parameters (radial speed and angular width) of these CMEs by applying StereoCAT to multi-spacecraft data (SOHO and STEREO). For this work, we use flare parameters (peak flux and fluence) taken from GOES X-ray flare list and 2D CME parameters (projected speed, apparent angular width, and kinetic energy) taken from CDAW SOHO LASCO CME catalog. Major results from this study are as follows. First, the relationship between flare peak flux (or fluence) and CME speed is almost same for both 2D and 3D cases. Second, there is a possible correlation between flare fluence and CME width, which is more evident in 3D case than 2D one. Third, the flare fluence is well correlated with CME kinetic energy (CC=0.63). Fourth, there is an upper limit of CME kinetic energy for a given flare fluence (or peak flux). For example, a possible CME kinetic energy ranges from 1030.6 to 1033 erg for a given X1.0 class flare. Our results will be discussed in view of the physical mechanism of solar eruptions.

• 천문학회보
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• 제46권1호
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• pp.42.1-42.1
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• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 1999년도 한국우주과학회보 제8권2호
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• pp.85-85
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• 1999

No Abstract, See Full Text

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2008년도 한국우주과학회보 제17권1호
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• pp.24.3-24.3
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• 2008

• 천문학회보
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• 제33권2호
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• pp.36.1-36.1
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• 2008

• 천문학논총
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• 제30권2호
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• pp.559-563
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• 2015

Monitor of All-sky X-ray Image (MAXI) is a Japanese X-ray all-sky surveyer mounted on the International Space Station (ISS). It has been scanning the whole sky since 2009 during every 92-minute ISS rotation. X-ray transients are quickly found by the real-time nova-search program. As a result, MAXI has issued 133 Astronomer's Telegrams and 44 Gamma-ray burst Coordinated Networks so far. MAXI has discovered six new black holes (BH) in 4.5 years. Long-term behaviors of the MAXI BHs can be classified into two types by their outbursts; a fast-rise exponential-decay type and a fast-rise flat-top one. The slit camera is suitable for accumulating data over a long time. MAXI issued a 37-month catalog containing 500 sources above a ~0.6 mCrab detection limit at 4-10 keV in the region ${\mid}{b}{\mid}$ > $10^{\circ}$. The SSC instrument utilizing an X-ray CCD has detected diffuse soft X-rays extending over a large solid angle, such as the Cygnus super bubble. MAXI/SSC has also detcted a Ne emission line from the rapid soft X-ray nova MAXI J0158-744. The overall shapes of outbursts in Be X-ray binaries (BeXRB) are precisely observed with MAXI/GSC. BeXRB have two kinds of outbursts, a normal outburst and a giant one. The peak dates of the subsequent giant outbursts of A0535+26 repeated with a different period than the orbital one. The Be stellar disk is considered to either have a precession motion or a distorted shape. The long-term behaviors of low-mass X-ray binaries (LMXB) containing weakly magnetized neutron stars are investigated. Transient LMXBs (Aql X-1 and 4U 1608-52) repeated outbursts every 200-1000 days, which is understood by the limit-cycle of hydrogen ionization states in the outer accretion disk. A third state (very dim state) in Aql X-1 and 4U 1608-52 was interpreted as the propeller effect in the unified picture of LMXB. Cir X-1 is a peculiar source in the sense that its long-term behavior is not like typical LMXBs. The luminosity sometimes decreases suddenly at periastron. It might be explained by the stripping of the outer accretion disk by a clumpy stellar wind. MAXI observed 64 large flares from 22 active stars (RS CVns, dMe stars, Argol types, young stellar objects) over 4 years. The total energies are $10^{34}-10^{36}$ erg $s^{-1}$. Since MAXI can measure the spectrum (temperature and emission measure), we can estimate the size of the plasma and the magnetic fields. The size sometimes exceeds the size of the star. The magnetic field is in the range of 10-100 gauss, which is a typical value for solar flares.

• 천문학회보
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• 제36권2호
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• pp.97.1-97.1
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• 2011

In this study we have examined the probability of solar proton events (SPEs) and their peak fluxes depending on flare (flux, longitude and impulsive time) and CME parameters (linear speed, longitude, and angular width). For this we used the NOAA SPE list and their associated flare data from 1976 to 2006 and CME data from 1997 to 2006. We find that about 3.5% (1.9% for M-class and 21.3% for X-class) of the flares are associated with SPEs. It is also found that this fraction strongly depends on longitude; for example, the fraction for $30W^{\circ}$ < L < $90W^{\circ}$ is about three times larger than that for $30^{\circ}E$ < L < $90^{\circ}E$. The SPE probability with long duration (${\geq}$ 0.3 hours) is about 2 (X-class flare) to 7 (M-class flare) times larger than that for flares with short duration (< 0.3 hours). In case of halo CMEs with V ${\geq}$ 1500km/s, 36.1% are associated with SPEs but in case of partial halo CME ($120^{\circ}$ ${\leq}$ AW < $360^{\circ}$) with 400 km/s ${\leq}$ V < 1000 km/s, only 0.9% are associated with SPEs. The relationships between X-ray flare peak flux and SPE peak flux are strongly dependent on longitude and impulsive time. The relationships between CME speed and SPE peak flux depend on longitude as well as direction parameter. From this study, we suggest a new SPE forecast method with three-steps: (1) SPE occurrence probability prediction according to the probability tables depending on flare and CME parameters, (2) SPE flux prediction from the relationship between SPE flux and flare (or CME) parameters, and (3) SPE peak time.

• 천문학회보
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• 제39권1호
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• pp.75.2-75.2
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• 2014

We have developed a set of daily solar flare peak flux forecast models using the multiple linear regression (MLR), the auto regression (AR), and artificial neural network (ANN) methods. We consider input parameters as solar activity data from January 1996 to December 2013 such as sunspot area, X-ray flare peak flux, weighted total flux $T_F=1{\times}F_C+10{\times}F_M+100{\times}F_X$ of previous day, mean flare rates of a given McIntosh sunspot group (Zpc), and a Mount Wilson magnetic classification. We compute the hitting rate that is defined as the fraction of the events whose absolute differences between the observed and predicted flare fluxes in a logarithm scale are ${\leq}$ 0.5. The best three parameters related to the observed flare peak flux are as follows: weighted total flare flux of previous day (r=0.5), Mount Wilson magnetic classification (r=0.33), and McIntosh sunspot group (r=0.3). The hitting rates of flares stronger than the M5 class, which is regarded to be significant for space weather forecast, are as follows: 30% for the auto regression method and 69% for the neural network method.

• 천문학회보
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• 제44권1호
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• pp.51.2-51.2
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• 2019

Multi-wavelength observations become very popular in astronomy. Even though there are some correlations among different sensor images, it is not easy to translate from one to the other one. In this study, we apply a deep learning method for image-to-image translation, based on conditional generative adversarial networks (cGANs), to solar images. To examine the validity of the method for scientific data, we consider several different types of pairs: (1) Generation of SDO/EUV images from SDO/HMI magnetograms, (2) Generation of backside magnetograms from STEREO/EUVI images, (3) Generation of EUV & X-ray images from Carrington sunspot drawing, and (4) Generation of solar magnetograms from Ca II images. It is very impressive that AI-generated ones are quite consistent with actual ones. In addition, we apply the convolution neural network to the forecast of solar flares and find that our method is better than the conventional method. Our study also shows that the forecast of solar proton flux profiles using Long and Short Term Memory method is better than the autoregressive method. We will discuss several applications of these methodologies for scientific research.