Cette thèse a pour objet la modélisation du champ magnétique de la couronne solaire à l'aide du code de reconstruction non linéaire XTRAPOLS, avec une attention particulière pour les environnements des phénomènes éruptifs. Le caractère novateur des études menées porte sur l'aspect sphérique global de la méthode.Trois études principales de cas sont présentées dans cette thèse. La première concerne les évènements éruptifs de février 2011, géoeffectifs, faisant figurer une région active étendue. Nous mettons en évidences plusieurs structures de tubes de flux torsadés, et caractérisons leur lien avec les structures à grande échelle.La deuxième concerne les évènements du 3 et 4 août 2011. Plusieurs régions actives sont présentes sur le disque solaires, et deux d'entre elles présentent une activité éruptive importante.Là encore, nous mettons en évidences des tubes de flux torsadés dans chacune de ces deux régions active,et mettons en lumière les liens topologiques qui existent entre elles.La Troisième concerne une étude faite dans le cadre d'un groupe NLFFF, pour l'étude de la modélisation non linéaire globale de la couronne. La date correspondant à la reconstruction est celle de l'éclipse totale de soleil du 20 mars 2015. Nous discutons ici de l'impact de différents type de données et de modèles utilisés, et soulignons l'importance de la cohérence temporelle et de l'inclusion du courant dans les régions actives.Les travaux présentés dans cette thèse ont donc permis de caractériser l'environnement global des régions actives éruptives et d'étudier les liens entre les éléments à différentes échelles. Nous présentons en guise d'ouverture différente méthode pour étendre la modélisation au delà de la surface source. / The object of this thesis is the modelisation of the magnetic field of the solar corona using the non linear reconstruction code XTRAPOLS, with a special emphasis on eruptive phenomena environments.The innovative nature of the studies we undertook is the spherical global aspect of the method.Three main works are presented in this dissertation. The first one is about the February 2011 geoeffective events, featuring a large active region. We highlight several twisted flux ropes structures,and characterize their relationship with large scale structures.The second work is about the events of August 3rd and 4th. Several active region are present on the disk,and two of them feature a high eruptive activity. Here again, we find twisted flux ropes in each of the active regions, and we highlight the topological relationship between them.The third is a study performed in the context of an NLFFF group, in order to study the non linear modeling of the global corona. The reconstruction is performed at a date corresponding to the total solar eclipse of march 20th 2015. We discuss the impact of the different types of data and models used, and emphasize on the importance of data temporal coherence and of taking into account coronal currents.Thus, the works presented in this dissertation allowed to characterize the global environment of eruptive active regions, to study the relationship between features at different scales. To go further, we present different methods for extending the model beyond the source surface.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第20181号 / 理博第4266号 / 新制||理||1613(附属図書館) / 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, 准教授 浅井 歩 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

Yes / In this work, machine learning algorithms are applied to explore the relation between significant flares and their associated CMEs. The NGDC flares catalogue and the SOHO/LASCO CMEs catalogue are processed to associate X and M-class flares with CMEs based on timing information. Automated systems are created to process and associate years of flares and CMEs data, which are later arranged in numerical training vectors and fed to machine learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Different properties are extracted from all the associated (A) and not-associated (NA) flares representing the intensity, flare duration, duration of decline and duration of growth. Cascade Correlation Neural Networks (CCNN) are used in our work. The flare properties are converted to numerical formats that are suitable for CCNN. The CCNN will predict if a certain flare is likely to initiate a CME after input of its properties. Intensive experiments using the Jack-knife techniques are carried out and it is concluded that our system provides an accurate prediction rate of 65.3%. The prediction performance is analysed and recommendation for enhancing the performance are provided.

No description available.

学位プログラム名: 京都大学大学院思修館 / 京都大学 / 新制・課程博士 / 博士(総合学術) / 甲第23343号 / 総総博第16号 / 新制||総総||3(附属図書館) / 京都大学大学院総合生存学館総合生存学専攻 / (主査)教授 山敷 庸亮, 教授 寶 馨, 准教授 浅井 歩 / 学位規則第4条第1項該当 / Doctor of Philosophy / Kyoto University / DFAM

No description available.

No description available.

Yes / Novel machine-learning and feature-selection algorithms have been developed to study: (i) the flare prediction capability of magnetic feature (MF) properties generated by the recently developed Solar Monitor Active Region Tracker (SMART); (ii) SMART's MF properties that are most significantly related to flare occurrence. Spatio-temporal association algorithms are developed to associate MFs with flares from April 1996 to December 2010 in order to differentiate flaring and non-flaring MFs and enable the application of machine learning and feature selection algorithms. A machine-learning algorithm is applied to the associated datasets to determine the flare prediction capability of all 21 SMART MF properties. The prediction performance is assessed using standard forecast verification measures and compared with the prediction measures of one of the industry's standard technologies for flare prediction that is also based on machine learning - Automated Solar Activity Prediction (ASAP). The comparison shows that the combination of SMART MFs with machine learning has the potential to achieve more accurate flare prediction than ASAP. Feature selection algorithms are then applied to determine the MF properties that are most related to flare occurrence. It is found that a reduced set of 6 MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties.

京都大学 / 新制・課程博士 / 博士(理学) / 甲第24414号 / 理博第4913号 / 新制||理||1702(附属図書館) / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)准教授 浅井 歩, 教授 一本 潔, 教授 横山 央明 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

Spacecraft formation flying (SFF) offers robust observations of multi-scale ionospheric space weather. A number of hardware-in-the-loop (HIL) SFF simulation testbeds based on Global-Navigation-Satellite-Systems (GNSS) have been developed to support GNSS-based SFF mission design, however, none of these testbeds has been directly applied to ionospheric space weather studies. The Virginia Tech Formation Flying Testbed (VTFFTB), a GNSS-based HIL simulation testbed, has been developed in this work to simulate closed-loop real-time low Earth orbit (LEO) SFF scenarios. The final VTFFTB infrastructure consists of three GNSS hardware signal simulators, three multi-constellation multi-band GNSS receivers, three navigation and control systems, an STK visualization system, and an ionospheric remote sensing system. A fleet of LEO satellites, each carrying a spaceborne GNSS receiver for navigation and ionospheric measurements, is simulated in scenarios with ionospheric impacts on the GPS and Galileo constellations. Space-based total electron density (TEC) and GNSS scintillation index S4 are measured by the LEO GNSS receivers in simulated scenarios. Four stages of work were accomplished to (i) build the VTFFTB with a global ionospheric modeling capability, and (ii) apply the VTFFTB to incubate future ionospheric measurement techniques. In stage 1, a differential-TEC method was developed to use space-based TEC measurements from a pair of LEO satellites to determine localized electron density (Ne). In stage 2, the GPS-based VTFFTB was extended to a multi-constellation version by adding the Galileo. Compared to using the GPS constellation only, using both GPS and Galileo constellations can improve ionospheric measurement quality (accuracy, precision, and availability) and relative navigation performance. Sensitivity studies found that Ne retrieval characteristics are correlated with LEO formation orbit, the particular GNSS receivers and constellation being used, as well as GNSS carrier-to-noise density C/N0. In stage 3, the VTFFTB for dual-satellite scenarios was further extended into a 3-satellite version, and then implemented to develop a polar orbit scenario with more fuel-efficient natural motion. In stage 4, a global 4-dimensioanl ionospheric model (TIE-CGM) was incorporated into the VTFFTB to significantly improve the modelling fidelity of multi-scale ionospheric space weather. Equatorial and polar space weather structures (e.g. plasma bubbles, tongues-of-ionization) were successfully simulated in 4-dimensional ionospheric scenarios on the enhanced VTFFTB. The dissertation has demonstrated the VTFFTB is a versatile GNSS-based SFF mission incubator to study ionospheric space weather impacts and develop next-generation multi-scale ionospheric observation missions. / Doctor of Philosophy / Spacecraft formation flying (SFF) is a space mission architecture with a group of spacecraft flying together and working as a team. SFF provides new opportunities for robust, flexible and low-cost observations of various phenomena in the ionized layer of Earth's atmosphere (called the ionosphere). Several hardware SFF simulation platforms based on Global Navigation Satellite Systems (GNSS) have been established to develop GNSS-based SFF missions, however, none of these platforms has ever directly used on-board GNSS receivers to study the impact of space weather on ionospheric density structures. The Virginia Tech Formation Flying Testbed (VTFFTB), a hardware simulation infrastructure using multiple GNSS signals, has been built in this work to emulate realistic SFF scenarios in low altitude orbits. The overall VTFFTB facility comprises three GNSS hardware signal emulators, three GNSS signal receivers, three navigation and control components, a software visualization component, and an ionospheric measurement component. Both Global-Positioning-System (GPS) and Galileo (the European version GNSS) are implemented in the VTFFTB. The objectives of this work are to (i) develop the VTFFTB with a high-fidelity ionospheric modeling capability, and (ii) apply the VTFFTB to incubate future ionospheric measurement techniques with GNSS receivers in space. A fleet of two or three spacecraft, each having a GNSS receiver to navigate and sense the ionosphere is emulated in several space environments. The electron concentration of the ionosphere and the GNSS signal fluctuation are measured by the GNSS receivers from space in simulated scenarios. These measurements are advantageous to study the location, size and structure of irregular ionospheric phenomena nearby the trajectory of spacecraft fleet. The culmination of this study is incorporation of an external global ionospheric model with temporal variations into the VTFFTB infrastructure to model a variety of realistic ionospheric structures and space weather impacts. Equatorial and polar space weather phenomenon were successfully simulated on the VTFFTB to verify a newly developed space-borne electron density measurement technique in the 3-dimensional ionosphere. Overall, it was successfully demonstrated that the VTFFTB is a versatile GNSS-based SFF mission incubator to study multiple kinds of ionospheric space weather impacts and develop next-generation space missions for ionospheric measurements.