Machine-learning algorithms are applied to explore the relation between significant flares and their associated CMEs. The NGDC flares catalogue and the SOHO/LASCO CME 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 flare and CME 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. Properties representing the intensity, flare duration, and duration of decline and duration of growth are extracted from all the associated (A) and not-associated (NA) flares and converted to a numerical format that is suitable for machine-learning use. The machine-learning algorithms Cascade Correlation Neural Networks (CCNN) and Support Vector Machines (SVM) are used and compared in our work. The machine-learning systems predict, from the input of a flare¿s properties, if the flare is likely to initiate a CME. Intensive experiments using Jack-knife techniques are carried out and the relationships between flare properties and CMEs are investigated using the results. The predictive performance of SVM and CCNN is analysed and recommendations for enhancing the performance are provided. / EPSRC

Made available in DSpace on 2016-03-15T19:35:58Z (GMT). No. of bitstreams: 1 Douglas Felix da Silva.pdf: 5938737 bytes, checksum: aa521c46d45966ffa8b9f1f8067be11c (MD5) Previous issue date: 2016-01-28 / Fundação de Amparo a Pesquisa do Estado de São Paulo / Solar flares are characterized by a sudden release of energy, of magnetic origin, that accelerates particles producing emission throughout the entire electromagnetic spectrum and plasma heating. It is believed that a fraction of these accelerated particles are injected into bipolar magnetic fields. Radiation from these events at radio wavelengths is due to the acceleration of the energetic particles that spiral around magnetic loops. Thus masurements of right and left circularly polarized brightness temperature of three flares at the frequencies of 45 and 90 GHz yield degrees of circular polarization that reached 5 to 40 % and were opposites at 45 and 90 GHz, always being reversed for the events. The interpretation of these results may be associated with the asymmetry of the field strength of magnetic loop legs. The objective of this work is to study the magnetic field configuration and energy distribution of accelerated particles in solar flares. For the study of these solar flares, we use the observations of the telescopes POEMAS (POlarization Emission of Millimeter Solar Activity), that monitor the Sun at 45 and 90 GHz with circular polarization. Observations in radio were complemented with microwaves, using data from the Radio Solar Telescope Network (RSTN) at 1-15 GHz, and high frequency emission, at 212 and 405 GHz, observed by the Solar Submillimeter Telescope (SST). X-ray data were obtained from FERMI and RHESSI telescopes; and the Solar Dynamics Observatory (SDO) provided images at 171 Å and magnetograms of the active regions. To study the interaction between the particles and magnetic field we applied the model developed by Simões (2009). Numerical simulations were performed and produced sources at 45 and 90 GHz in a three dimensional magnetic loop with maximum intensity in opposite polarities of a dipole loop. The simulations also reproduced the degree of polarization and radio spectra observed in each event. Thus, by means of the simulations, we obtained the location of 45 and 90 GHz sources with predominant intensities in opposite magnetic polarities and with reversed degree of polarization. / A explosão solar é caracterizada por uma súbita liberação de energia, de origem magnética, a qual acelera as partículas produzindo emissão em todo o espectro eletromagnético e promovendo o aquecimento do plasma. Acredita-se que uma fração destas partículas não térmicas aceleradas são injetadas em campos magnéticos bipolares. A emissão de radiação proveniente dos eventos na faixa rádio é devida à aceleração das partículas energéticas associada ao movimento em espiral que fazem em torno dos arcos magnéticos. Medidas de temperatura de brilho circularmente polarizada à direita e à esquerda em três explosões solares nas frequências de 45 e 90 GHz apresentaram graus de polarização circular que alcançaram de 5 a 40 % e opostos em 45 e 90 GHz, sempre sendo invertidos para os eventos estudados. Uma interpretação desses resultados pode estar associada com a assimetria de intensidade do campo nos pés do arco magnético. O objetivo do trabalho é estudar a configuração do campo magnético e a distribuição de energia das partículas aceleradas em explosões solares na faixa rádio. Para o estudo das explosões, utilizamos as observações do sistema de telescópios POEMAS (POlarização da Emissão Milimétrica da Atividade Solar), que monitora o Sol em 45 e 90 GHz com medidas de polarização. As observações em rádio foram complementadas em micro-ondas, utilizando os dados da Rede de Radio Telescópios Solares (RSTN), de 1 a 15 GHz, e em altas frequências (212 e 405 GHz) pelo Telescópio Solar Submilimetrico (SST). Na faixa de raio X foram utilizados dados dos telescópios FERMI e RHESSI; enquanto do Solar Dynamics Observatory (SDO) foram obtidas imagens em 171 Å e magnetogramas das região ativas. Para estudar a interação entre as partículas e campo magnético foi aplicado o modelo desenvolvido por Simões (2009). Foram realizadas simulações numéricas que produziram fontes em 45 e 90 GHz num arco magnético em três dimensões, cujas fontes apresentaram máximos de intensidade em polaridades opostas de um arco dipolar. As simulações também reproduziram qualitativamente o grau de polarização observado em cada um dos eventos e também o espectro rádio. Assim, por meio da simulação, obtivemos as possíveis localizações das fontes em 45 e 90 GHz com intensidades predominantes em polaridades opostas e grau de polarização invertido.

Solar transient eruptions are the main cause of interplanetary-magnetospheric disturbances leading to the phenomena known as geomagnetic storms. Eruptive solar events such as coronal mass ejections (CMEs) are currently considered the main cause of geomagnetic storms (GMS). GMS are strong perturbations of the Earth’s magnetic field that can affect space-borne and ground-based technological systems. The solar-terrestrial impact on modern technological systems is commonly known as Space Weather. Part of the research study described in this thesis was to investigate and establish a relationship between GMS (periods with Dst ≤ −50 nT) and their associated solar and interplanetary (IP) properties during solar cycle (SC) 23. Solar and IP geoeffective properties associated with or without CMEs were investigated and used to qualitatively characterise both intense and moderate storms. The results of this analysis specifically provide an estimate of the main sources of GMS during an average 11-year solar activity period. This study indicates that during SC 23, the majority of intense GMS (83%) were associated with CMEs, while the non-associated CME storms were dominant among moderate storms. GMS phenomena are the result of a complex and non-linear chaotic system involving the Sun, the IP medium, the magnetosphere and ionosphere, which make the prediction of these phenomena challenging. This thesis also explored the predictability of both the occurrence and strength of GMS. Due to their nonlinear driving mechanisms, the prediction of GMS was attempted by the use of neural network (NN) techniques, known for their non-linear modelling capabilities. To predict the occurrence of storms, a combination of solar and IP parameters were used as inputs in the NN model that proved to predict the occurrence of GMS with a probability of 87%. Using the solar wind (SW) and IP magnetic field (IMF) parameters, a separate NN-based model was developed to predict the storm-time strength as measured by the global Dst and ap geomagnetic indices, as well as by the locally measured K-index. The performance of the models was tested on data sets which were not part of the NN training process. The results obtained indicate that NN models provide a reliable alternative method for empirically predicting the occurrence and strength of GMS on the basis of solar and IP parameters. The demonstrated ability to predict the geoeffectiveness of solar and IP transient events is a key step in the goal towards improving space weather modelling and prediction.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第22254号 / 理博第4568号 / 新制||理||1656(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 一本 潔, 准教授 浅井 歩, 教授 柴田 一成 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

The works deals with the study of the flare stars, which is a group of stars for which sudden brightening can be observed. The work focuses on a star GJ 3236, which is a low-mass eclipsing binary and on which numerous flares have been observed. For the analysis of this system spectroscopic and photometric data were used, which were obtained at various observatories. Parameters of the binary system have been determined by analysing spectroscopic and photometric data with the program PHOEBE. A total of 241 flares have been detected in the photometric data and for 190 flares, which light curves were not affected by eclipses, released energies were estimated. The set of flares was used for the study of stellar activity of the binary. The energy distribution of observed flares is similar to the flares observed on other flare stars and also on the Sun. This suggests, that the flare mechanism is the same for these stars.

Solar flares and eruptions are manifestations of violent releases of magnetic energy from the solar atmosphere. They are powered by magnetic reconnection, a mechanism in which magnetic field lines change their connectivities to reach a lower-energetic state. Theoretical predictions regarding the generalised three-dimensional magnetic reconnection are imposed by the standard flare model in 3D. In this work we present the results of five peer-reviewed publications in which we focused on different predicted aspects of magnetic reconnection in 3D. We analyse evolution and morphology of seven eruptive flares, primarily using observations of the Atmospheric Imaging Assembly onboard the Solar Dynamics Observatory. In the first publication, (Lörinčík et al., 2019a), we interpreted variations of velocities of slipping flare kernels using the mapping norm of field line connectivity simulated via the model. In Lörinčík et al. (2019b) we showed that the observed conversion of filament strands to flare loops is a signature of the 'ar-rf' reconnection geometry between erupting flux rope and overlying coronal arcades. In another observation (Dudík, Lörinčík et al. (2019)), all constituents of this geometry were successfully identified together with the constituents of the 'rr-rf' geometry between two...

Space weather has become an international issue due to the catastrophic impact it can have on modern societies. Solar flares are one of the major solar activities that drive space weather and yet their occurrence is not fully understood. Research is required to yield a better understanding of flare occurrence and enable the development of an accurate flare prediction system, which can warn industries most at risk to take preventative measures to mitigate or avoid the effects of space weather. This thesis introduces novel technologies developed by combining advances in statistical physics, image processing, machine learning, and feature selection algorithms, with advances in solar physics in order to extract valuable knowledge from historical solar data, related to active regions and flares. The aim of this thesis is to achieve the followings: i) The design of a new measurement, inspired by the physical Ising model, to estimate the magnetic complexity in active regions using solar images and an investigation of this measurement in relation to flare occurrence. The proposed name of the measurement is the Ising Magnetic Complexity (IMC). ii) Determination of the flare prediction capability of active region properties generated by the new active region detection system SMART (Solar Monitor Active Region Tracking) to enable the design of a new flare prediction system. iii) Determination of the active region properties that are most related to flare occurrence in order to enhance understanding of the underlying physics behind flare occurrence. The achieved results can be summarised as follows: i) The new active region measurement (IMC) appears to be related to flare occurrence and it has a potential use in predicting flare occurrence and location. ii) Combining machine learning with SMART¿s active region properties has the potential to provide more accurate flare predictions than the current flare prediction systems i.e. ASAP (Automated Solar Activity Prediction). iii) Reduced set of 6 active region properties seems to be the most significant properties related to flare occurrence and they can achieve similar degree of flare prediction accuracy as the full 21 SMART active region properties. The developed technologies and the findings achieved in this thesis will work as a corner stone to enhance the accuracy of flare prediction; develop efficient flare prediction systems; and enhance our understanding of flare occurrence. The algorithms, implementation, results, and future work are explained in this thesis.

Space weather has become an international issue due to the catastrophic impact it can have on modern societies. Solar flares are one of the major solar activities that drive space weather and yet their occurrence is not fully understood. Research is required to yield a better understanding of flare occurrence and enable the development of an accurate flare prediction system, which can warn industries most at risk to take preventative measures to mitigate or avoid the effects of space weather. This thesis introduces novel technologies developed by combining advances in statistical physics, image processing, machine learning, and feature selection algorithms, with advances in solar physics in order to extract valuable knowledge from historical solar data, related to active regions and flares. The aim of this thesis is to achieve the followings: i) The design of a new measurement, inspired by the physical Ising model, to estimate the magnetic complexity in active regions using solar images and an investigation of this measurement in relation to flare occurrence. The proposed name of the measurement is the Ising Magnetic Complexity (IMC). ii) Determination of the flare prediction capability of active region properties generated by the new active region detection system SMART (Solar Monitor Active Region Tracking) to enable the design of a new flare prediction system. iii) Determination of the active region properties that are most related to flare occurrence in order to enhance understanding of the underlying physics behind flare occurrence. The achieved results can be summarised as follows: i) The new active region measurement (IMC) appears to be related to flare occurrence and it has a potential use in predicting flare occurrence and location. ii) Combining machine learning with SMART's active region properties has the potential to provide more accurate flare predictions than the current flare prediction systems i.e. ASAP (Automated Solar Activity Prediction). iii) Reduced set of 6 active region properties seems to be the most significant properties related to flare occurrence and they can achieve similar degree of flare prediction accuracy as the full 21 SMART active region properties. The developed technologies and the findings achieved in this thesis will work as a corner stone to enhance the accuracy of flare prediction; develop efficient flare prediction systems; and enhance our understanding of flare occurrence. The algorithms, implementation, results, and future work are explained in this thesis.

Coronal Mass Ejections (CMEs) and solar flares are energetic events taking place at the Sun that can affect the space weather or the near-Earth environment by the release of vast quantities of electromagnetic radiation and charged particles. Solar active regions are the areas where most flares and CMEs originate. Studying the associations among sunspot groups, flares, filaments, and CMEs is helpful in understanding the possible cause and effect relationships between these events and features. Forecasting space weather in a timely manner is important for protecting technological systems and human life on earth and in space. The research presented in this thesis introduces novel, fully computerised, machine learning-based decision rules and models that can be used within a system design for automated space weather forecasting. The system design in this work consists of three stages: (1) designing computer tools to find the associations among sunspot groups, flares, filaments, and CMEs (2) applying machine learning algorithms to the associations' datasets and (3) studying the evolution patterns of sunspot groups using time-series methods. Machine learning algorithms are used to provide computerised learning rules and models that enable the system to provide automated prediction of CMEs, flares, and evolution patterns of sunspot groups. These numerical rules are extracted from the characteristics, associations, and time-series analysis of the available historical solar data. The training of machine learning algorithms is based on data sets created by investigating the associations among sunspots, filaments, flares, and CMEs. Evolution patterns of sunspot areas and McIntosh classifications are analysed using a statistical machine learning method, namely the Hidden Markov Model (HMM).

Aktive galaktische Kerne (AGN) gehören zu den besten Quellkandidaten der hochenergetischen kosmischen Strahlung. Es wird erwartet, dass hochenergetische Neutrinos durch Interaktion der kosmischen Strahlung mit Materie oder Photonfeldern in der Nähe der Quellen erzeugt werden. Der resultierende Neutrinofluss kann dieselbe Zeitvariabilität aufweisen wie elektromagnetische Strahlung die von diesen Quellen emittiert wird. Diese Zeitvariabiltät kann in Neutrinoanalysen zusätzlich zu Energie-und Ortsinformationen benutzt werden, um die Detektionswahrscheinlichkeit zu erhöhen. Im Rahmen dieser Arbeit werden zwei neue Methoden entwickelt, welche benutzt werden um nach Neutrino-flares in Aktiven Galaktischen Kernen zu suchen: Die Multi-flare und Multi-flare-Stacking-Methode. Die Multi-flare-Methode ist so entworfen, dass sie nicht nur sensitiv auf einen hellen Flare ist, sondern auch auf weitere schwächere Flares welche normalerweise individuell nicht detektiert werden können. Die Multi-Flare-Methode benötigt keine Zeitkoinzidenz mit Ausbrüchen im elektromagnetischen Spektrum. Sie ist auch sensitiv auf unkorrelierter Neutrinoemission mit unterschiedlicher Dauer der einzelnen Flares, was in einigen Emissionsmodellen vorkommt. Die Multi-Flare-Stacking-Methode ist eine Erweiterung der Multi-Flare-Methode auf zusätzliche Quellen. In ihr werden mehrere schwache, variable Quellen, welche individuell zu schwach sein können um detektiert zu werden, zusammen mit der Stackingmethode analysiert. Die beiden Analysemethoden werden auf eine vorselektierte Liste von Aktiven Galaktischen Kernen angewandt. Hierfür werden drei Jahre Daten des IceCube Neutrinoteleskops verwendet (Mai 2009-June-2012). Kein statistisch signfikanter Neutrinoflare wurde gefunden und obere Fluenzgrenzen f ̈ur jede der Quellen werden ausgerechnet. Diese Grenzen sind im Durchschnitt um einen Faktor zwei besser als vorherige Obergrenzen von Analysen einzelner Flares. / Active Galactic Nuclei are among the best candidate sources for high-energy cosmic rays. High-energy neutrinos are expected to be produced in these sources via interactions of cosmic rays with matter or photon fields present in the source vicinity. The resulting neutrino flux may exhibit time variability on the same time scales than the ones observed in the electromagnetic radiation that is emitted from these sources. Time variability can be taken into account in high-energy neutrino searches in order to increase their detection probability with respect to search methods that include only energy and spatial information. In this work, two new methods are developed to look for high-energy neutrino flares emitted from Active Galactic Nuclei: the Multi-flare and Multi-flare stacking method. The Multi-flare method is designed to be sensitive not only to one bright flare emitted from a single source, as considered in other existing search methods, but also to several weak flares that might not be detected individually. This is achieved by developing a likelihood stacking approach that analyzes the cumulative neutrino emission from several flares. This method does not assume a-priori time coincidences with photon flares observed in the electromagnetic spectrum, allowing uncorrelated neutrino emission with different flare durations as considered in some emission models. The Multi-flare stacking method is an extension of the Multi-flare method to include several sources that might be too weak for individual detection. The two search methods are applied to a pre-selected list of Active Galactic Nuclei using data of the IceCube Neutrino Observatory (May-2009 to May 2012). No statistically significant neutrino flares are detected and fluence upper limits are calculated for each selected source. These limits are on average a factor of two better than previous upper limits from single-flare searches.