京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第19359号 / 理博第4121号 / 新制||理||1593(附属図書館) / 32373 / 新制||理||1593 / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)教授 一本 潔, 教授 柴田 一成, 准教授 野上 大作 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

Yes / The variation of solar irradiance is one of the natural forcing mechanisms of the terrestrial climate. Hence, the time-dependent solar irradiance is an important input parameter for climate modelling. The solar surface magnetic field is a powerful proxy for solar irradiance reconstruction. The analyses of data obtained with the Michelson Doppler Imager (MDI) on board the SOHO mission are therefore useful for the identification of solar surface magnetic features to be used in solar irradiance reconstruction models. However, there is still a need for automated technologies that would enable the identification of solar activity features from large databases. To achieve this we present a series of enhanced segmentation algorithms developed to detect and calculate the area coverages of specific magnetic features from MDI intensitygrams and magnetograms. These algorithms are part of the Automated Solar Activity Prediction (ASAP) tool. The segmentation algorithms allow us to identify the areas on the solar disk covered by magnetic elements inside and outside boundaries of active regions. Depending on their contrast properties, magnetic features within an active region boundary are classified as sunspot umbra and penumbra, or faculae. Outside an active region boundary magnetic elements are identified as network. We present the detailed steps involved in the segmentation process and provide the area coverages of the segmented MDI intensitygrams and magnetograms. The feature segmentation was carried out on daily intensitygrams and magnetograms from April 21, 1996 to April 11, 2011. This offers an exciting opportunity to undertake further investigations that benefit from solar features segmentations, such as solar irradiance reconstruction, which we plan to investigate in the future.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第19503号 / 理博第4163号 / 新制||理||1598(附属図書館) / 32539 / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)教授 柴田 一成, 教授 一本 潔, 教授 嶺重 慎 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

This paper studies the optical properties of a GaN Laser Diode (LD). Through simulation, the GaN LD is optimized for the best optical confinement factor. It is found that there are optimal thicknesses of each layer in the diode that yield the highest optical confinement factor. There is a strong relationship between the optical confinement factor and lasing threshold—a higher optical confinement factor results in a lower lasing threshold. Increasing optical confinement improves lasing efficiency. Blue LDs are important to the future of lighting sources as they represent the final color in the RGB spectrum that does not have a high efficiency solution. The modeled GaN LD emits blue light at around ~450nm. Each layer of the GaN LD is drawn in a model simulation program called LaserMOD created by RSOFT Design Group, Inc. By properly modifying the properties of each layer, an accurate model of the GaN LD is created and then simulated. This paper describes the steps taken to properly model and optimize the GaN LD in the 1D and 2D models.

yes / The surface of the sun often shows visible sunspots which are located in magnetically active regions of the Sun, and whose number is an indicator of the Sun’s magnetic activity. The detection and classification of sunspots are useful techniques in the monitoring and prediction of solar activity. The automated detection of sunspots from digital images is complicated by their irregularities in shape and variable contrast and intensity compared with their surrounding area. The main aim of this paper is to detect sunspots using images from the Solar Diameter Imager and Surface Mapper (SODISM) on the PICARD satellite and calculate their filling factors. A comparison over time with sunspot numbers obtained using images from the SOHO satellite is also presented.

yes / This paper presents a hybrid system for automatic detection and McIntosh-based classification of sunspot groups on SOHO/MDI white-light images using active-region data extracted from SOHO/MDI magnetogram images. After sunspots are detected from MDI white-light images they are grouped/clustered using MDI magnetogram images. By integrating image-processing and neural network techniques, detected sunspot regions are classified automatically according to the McIntosh classification system. Our results show that the automated grouping and classification of sunspots is possible with a high success rate when compared to the existing manually created catalogues. In addition, our system can detect and classify sunspot groups in their early stages, which are usually missed by human observers. / EPSRC

No / A crucial challenge to successful flare prediction is forecasting periods that transition between "flare-quiet" and "flare-active." Building on earlier studies in this series in which we describe the methodology, details, and results of flare forecasting comparison efforts, we focus here on patterns of forecast outcomes (success and failure) over multiday periods. A novel analysis is developed to evaluate forecasting success in the context of catching the first event of flare-active periods and, conversely, correctly predicting declining flare activity. We demonstrate these evaluation methods graphically and quantitatively as they provide both quick comparative evaluations and options for detailed analysis. For the testing interval 2016-2017, we determine the relative frequency distribution of two-day dichotomous forecast outcomes for three different event histories (i.e., event/event, no-event/event, and event/no-event) and use it to highlight performance differences between forecasting methods. A trend is identified across all forecasting methods that a high/low forecast probability on day 1 remains high/low on day 2, even though flaring activity is transitioning. For M-class and larger flares, we find that explicitly including persistence or prior flare history in computing forecasts helps to improve overall forecast performance. It is also found that using magnetic/modern data leads to improvement in catching the first-event/first-no-event transitions. Finally, 15% of major (i.e., M-class or above) flare days over the testing interval were effectively missed due to a lack of observations from instruments away from the Earth-Sun line.