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Research on the detection of solar events has been conducted over many years. Recently, deep learning and data-driven approaches have been applied to solar event recognition. In this study, we present solar event detection using deep-learning-based object detection methods for real-time space weather monitoring. First, we construct a new object detection dataset using imaging data obtained by the Solar Dynamics Observatory with bounding boxes as labels for three representative features: coronal holes, sunspots, and prominences. Second, we train two representative object detection models: the Single Shot MultiBox Detector (SSD) and the Faster Region-based Convolutional Neural Network (R-CNN) using the new dataset. The results show that both models perform similarly well for coronal hole and sunspot detection. For prominence detection, the SSD and Faster R-CNN exhibited relatively low performance. This study demonstrates that deep-learning-based object detection can successfully detect multiple types of solar events, and it may be extended to detect other solar events. In addition, we provide the dataset for further achievements of object detection studies in solar physics.

We report on the Balloonborne Investigation of Temperature and Speed of Electrons in the corona (BITSE) mission launched recently to observe the solar corona from about 3 Rs to 15 Rs at four wavelengths (393.5, 405.0, 398.7, and 423.4 nm). The BITSE instrument is an externally occulted single stage coronagraph developed at NASA's Goddard Space Flight Center in collaboration with the Korea Astronomy and Space Science Institute (KASI). BITSE used a polarization camera that provided polarization and total brightness images of size 1024 x 1024 pixels. The Wallops Arc Second Pointing (WASP) system developed at NASA's Wallops Flight Facility (WFF) was used for Sun-pointing. The coronagraph and WASP were mounted on a gondola provided by WFF and launched from the Fort Sumner, New Mexico station of Columbia Scientific Balloon Facility (CSBF) on September 18, 2019. BITSE obtained 17,060 coronal images at a float altitude of about 128,000 feet (39 km) over a period of about 4 hrs. BITSE flight software was based on NASA's core Flight System, which was designed to help develop flight quality software. We used EVTM (Ethernet Via Telemetry) to download science data during operations; all images were stored onboard using flash storage. At the end of the mission, all data were recovered and analyzed. Preliminary analysis shows that BITSE imaged the solar minimum corona with the equatorial streamers on the east and west limbs. The narrow streamers observed by BITSE are in good agreement with the geometric properties obtained by SOHO coronagraphs in the overlapping physical domain. In spite of the small signal-to-noise ratio (about 14) we were able to obtain the temperature and flow speed of the western steamer region in the range 4 to 7 Rs as: For the equatorial streamer on the west limb, we obtained a temperature of 1.0 +/- 0.3 MK and a flow speed of about 260 km/s with a large uncertainty interval., 40 pages, 25 figures, 4 tables, 3 electronic supplements, to appear in Solar Physics

The core Flight System (cFS), developed by NASA, is a reusable software framework and a set of pluggable software applications that take advantage of the rich heritage of NASA’s successful space missions. We applied the cFS to the development of telescope control software for the observation of the 2017 total solar eclipse. Four main modules were developed: imaging control, mechanism control, data handling, and automated observation. Other modules, such as communication and scheduler, were reused from the cFS. Using an integrated observation system, we successfully observed the total solar eclipse, in which the linearly polarized brightness of the solar corona and sky background were measured at four different wavelengths. In this study, we demonstrated the usefulness of the cFS to develop telescope control software through an eclipse observation system, the so-called DICE (DIagnostic Coronagraph Experiment) mission. Our experience and knowledge of the cFS were expanded to a flight software BITSE (Balloon-borne Investigation of Temperature and Speed of Electrons in the corona), the high-altitude scientific balloon mission in 2019. We plan to apply this approach to future solar coronagraph observations, such as CODEX (COronal Diagnostic EXperiment), on the International Space Station. We expect that the cFS can also be applied in telescope control software for ground-and space-based observations.

The conventional skill scores for evaluating flare forecast models do not take into account the effect of various cost-loss ratios. For the first time, we have applied a decision making based on skill scores to a flare forecast model in cost-loss ratio situations. For this study, we consider a flare forecast model which provides daily solar flare probabilities from 2011 to 2014. The skill scores are computed through contingency tables as a function of probability threshold (Pth) for the decision making. A value score (VS) is calculated as a function of cost-loss ratio (C/L) and Pth, which are linearly correlated with each other. The maximum values of VS are 0.57, 0.37, and 0.61 for C-, M-, and X-class flare, respectively. We expect that this study would provide a guideline to determine C/L and Pth for the better decision making in similar forecast models.

The goal of the SunPy project is to facilitate and promote the use and development of community-led, free, and open source data analysis software for solar physics based on the scientific Python environment. The project achieves this goal by developing and maintaining the sunpy core package and supporting an ecosystem of affiliated packages. This paper describes the first official stable release (version 1.0) of the core package, as well as the project organization and infrastructure. This paper concludes with a discussion of the future of the SunPy project. NSFNational Science Foundation (NSF) [AST-1715122]; DIRAC Institute in the Department of Astronomy at the University of Washington; STFC studentshipScience & Technology Facilities Council (STFC) [ST/N504336/1]; STFC grantScience & Technology Facilities Council (STFC) [ST/N000692/1]; Google; NumFocus; Solar Physics Division of the American Astronomical Society; Space program The following individuals recognize support for their personal contributions. B.M.S. is supported by the NSF grant AST-1715122 and acknowledges support from the DIRAC Institute in the Department of Astronomy at the University of Washington. The DIRAC Institute is supported through generous gifts from the Charles and Lisa Simonyi Fund for Arts and Sciences, and the Washington Research Foundation. D.S. was supported by STFC studentship ST/N504336/1 and STFC grant ST/N000692/1.; We acknowledge financial contributions from Google as part of the Google Summer of Code program and from the Space program. We acknowledge financial contributions from NumFocus for improving the usability of SunPy's Data Downloader. Additionally, we acknowledge current and future funding from the Solar Physics Division of the American Astronomical Society for SunPy workshops and tutorials at annual meetings.; This work has made use of data from the European Space Agency (ESA) mission Gaia,80 processed by the Gaia Data Processing and Analysis Consortium (DPAC).81 Funding for the DPAC has been provided by national institutions, in particular, the institutions participating in the Gaia Multilateral Agreement.

We have developed a set of daily maximum flare-flux forecast models for strong flares (M- and X-class) using multiple linear regression (MLR) and artificial neural network (ANN) methods. Our input parameters are solar-activity data from January 1996 to December 2013 such as sunspot area, X-ray maximum, and weighted total flare flux of the previous day, as well as mean flare rates of McIntosh sunspot group (Zpc) and Mount Wilson magnetic classifications. For a training dataset, we used 61 events each of C-, M-, and X-class from January 1996 to December 2004. For a testing dataset, we used all events from January 2005 to November 2013. A comparison between our maximum flare-flux models and NOAA model based on true skill statistics (TSS) shows that the MLR model for X-class and the average of all flares ( $\mathrm{M}{+}\mathrm{X}$ -class) are much better than the NOAA model. According to the hitting fraction (HF), which is defined as a fraction of events satisfying the condition that the absolute differences of predicted and observed flare flux on a logarithm scale are smaller than or equal to 0.5, our models successfully forecast the maximum flare flux of about two-thirds of the events for strong flares. Since all input parameters for our models are easily available, the models can be operated steadily and automatically on a daily basis for space-weather services.

Lithium-ion secondary batteries have been generally used as promising power sources for hybrid electric vehicles due to their high energy density. Conventional liquid organic electrolytes have a number of inherent limitations and drawbacks. Solvent of electrolytes has safety problems for batteries in large scale devices due to its flammability.

Korea Astronomy and Space Science Institute (KASI) has been developing the Camera Lens System (CLS) for the Total Solar Eclipse (TSE) observation. In 2016 we have assembled a simple camera system including a camera lens, a polarizer, bandpass filters, and CCD to observe the solar corona during the Total Solar Eclipse in Indonesia. Even we could not obtain the satisfactory result in the observation due to poor environment, we obtained some lessons such as poor image quality due to ghost effect from the lens system. For 2017 TSE observation, we have studied and adapted the compact coronagraph design proposed by NASA. The compact coronagraph design dramatically reduces the volume and weight and can be used for TSE observation without an external occulter which blocks the solar disk. We are in developing another camera system using the compact coronagraph design to test and verify key components including bandpass filter, polarizer, and CCD, and it will be used for the Total Solar Eclipse (TSE) in 2017. We plan to adapt this design for a coronagraph mission in the future. In this report we introduce the progress and current status of the project and focus on optical engineering works including designing, analyzing, testing, and building for the TSE observation.

Korea Astronomy and Space Science Institute, Daejeon 305-348, KoreaE-mail: jhbaek@kasi.re.kr(Received September 06, 2013; Accepted September 27, 2013)ABSTRACT We have developed a data integration system for ground-based space weather facilities in Korea Astronomy and Space Science Institute (KASI). The data integration system is necessary to analyze and use ground-based space weather data efficiently, and consists of a server system and data monitoring systems. The server system consists of servers such as data acquisition server or web server, and storage. The data monitoring systems include data collecting and processing applications and data display monitors. With the data integration system we operate the Space Weather Monitoring Lab (SWML) where real-time space weather data are displayed and our ground-based observing facilities are monitored. We expect that this data integration system will be used for the highly efficient processing and analysis of the current and future space weather data at KASI.Key words: space weather; data integration system; ground-based observational system: solar telescope, magnetometer, VHF radar

[1] We have made a comparison of near-real time Kp forecast models based on neural network (NN) and support vector machine (SVM) algorithms. For this, we consider four models as follows: (1) a NN model using solar wind data, (2) a SVM model using solar wind data, (3) a NN model using solar wind data and preliminary Kp values from ground-based magnetometers, and (4) a SVM model using the same input data used in model 3. For the comparison, we estimate the correlation coefficients and the RMS errors, and the mean absolute errors between the observed Kp and the predicted one. As a result, we find that model 3 shows the best performance. The correlation coefficients, the RMS error, and the mean absolute error of model 3 are 0.93, 0.48, and 0.61, respectively. For the forecast evaluation of high magnetic activity occurrence for the four models, we present contingency tables and their statistical parameters such as probability of detection yes, false alarm ratio, bias, critical success index, and true skill statistic. From a comparison of these statistical parameters, we find that model 3 is superior to the other models for the forecasting of high magnetic activities (Kp ≥ 6).

We have compared the KOrea Multi-Purpose SATellite-1 (KOMPSAT-1) drag derived density with the MSISE model (NRLMSISE-00 and MSISE-90) density during strong solar and/or geomagnetic activities. It is well known that there are two major mechanisms to induce satellite drag caused by atmospheric density enhancement: the heating by solar EUV radiation and joule heating associated with local geomagnetic current enhancements during geomagnetic storms. For this work we select five events dominated by the radiation effect and/or the geomagnetic effect. For these events we compared the satellite drag derived density with the MSISE model density. The major results can be summarized as follows. (1) The density predicted from the MSISE models during radiation dominated periods are comparable to the drag derived density but the MSISE model density during strong geomagnetic storms is significantly underestimated when the MSISE model density is compared to the drag derived density, by about two times for the NRLMSISE-00 model. (2) The ratios of the KOMPSAT-1 (around 685 km) drag derived density to the MSISE model density during a strong geomagnetic storm are abruptly enhanced (up to a factor of about 8 for the MSISE-90 model and about 3 for the NRLMSISE-00 model), which are much larger than previous estimates from low altitude (around 400 km) satellites. (3) There is a possible correlation between daily drag enhancement and daily Dst variation. We note that there is a remarkable difference in daily drag enhancement although solar and geomagnetic activities are quite similar to each other. We suggest that such a difference should be explained by the accumulation of solar radiation effect depending on solar activity cycle.

We examine the atmospheric drag on the low earth-orbiting satellite, KOMPSAT-1 in a sun-synchronous orbit at ~685-km altitude starting in 1999, during a 3-month (October-December) period in 2003. This 3-month interval includes the October 29-30 and November 20 magnetic superstorms and weak to moderate storms. We observed that the daily KOMPSAT-1 drag acceleration transiently responses to transient storm-time disturbances. That is, there is an one-to-one correspondence between the KOMPSAT-1 drag accelerations and the storm events. We find that the drag acceleration correlates strongly with the level of geomagnetic disturbance. This indicates that the trajectory of KOMPSAT-1 is significantly perturbed during extremely disturbed intervals because of atmospheric density increase. The main contributor to the density increase is Joule heating associated with the geomagnetic activity rather than the solar EUV radiation, as reported by previous studies. We suggest that understanding how the upper atmosphere responses to the geomagnetic-associated heating is important to predict space weather impacts on low earth-orbiting satellites.

For 478 centrally located sunspots observed in the optical continuum with Solar Dynamics Observatory/Helioseismic Magnetic Imager, we perform seismological diagnostics of the physical parameters of umbral photospheres. The new technique is based on the theory of slow magnetoacoustic waves in a non-isothermally stratified photosphere with a uniform vertical magnetic field. We construct a map of the weighted frequency of three-minute oscillations inside the umbra and use it for the estimation of the Alfven speed, plasma-beta, and mass density within the umbra. We find the umbral mean Alfven speed ranges between 10.5 and 7.5 km s−1 and is negatively correlated with magnetic field strength. The umbral mean plasma-beta is found to range approximately between 0.65 and 1.15 and does not vary significantly from pores to mature sunspots. The mean density ranges between (1–6) × 10−4 kg m−3 and shows a strong positive correlation with magnetic field strength.

[1] In this study we have examined the occurrence probability of solar proton events (SPEs) and their peak fluxes depending on coronal mass ejection (CME) parameters, linear speed (V), angular width (AW), and location (L). For this we used the NOAA SPE list and their associated CME data from 1997 to 2006. We found that the probability strongly depends on CME speed and angular width as follows. The highest association (36.1%) is found for the full halo CMEs with V ≥ 1500 kms−1 but the lowest association (0.9%) is found for the partial halo CMEs with 400 kms−1 ≤ V < 1000 kms−1. The SPE occurrence probabilities are different as much as 4.9 to 23 times according to CME speed and 1.6 to 6.5 times to angular width. The probabilities depending on CME speed and location increase from the eastern region to the western region and with speed. We have also examined the relationship between CME speed and SPE flux as well as its dependence on angular width (partial halo and full halo), longitude (east, center, and west) and direction parameter (