Based on a study of existing solar filament and tracking methods, a fully automated solar filament detection and tracking method is presented. An adaptive thresholding technique is used in a segmentation phase to identify candidate filament pixels. This phase is followed by retrieving the actual filament area from a region grown filament by using statistical parameters and morphological operations. This detection technique gives the opportunity to develop an accurate spine extraction algorithm. Features including separation distance, orientation and average intensities are extracted and fed to a Neural Network (NN) classifier to merge broken filament components. Finally, the results for two consecutive images are compared to detect filament disappearance events, taking advantage of the maps resulting from converting solar images to Heliographic Carrington co-ordinates. The study has demonstrated the novelty of the algorithms developed in terms of them now all being fully automated; significantly the algorithms do not require any empirical values to be used whatsoever unlike previous techniques. This combination of features gives the opportunity for these methods to work in real-time. Comparisons with other researchers shows that the present algorithms represent the filaments more accurately and evaluate computationally faster - which could lead to a more precise tracking practice in real-time. An additional development phase developed in this dissertation in the process of detecting solar filaments is the detection of filament disappearances. Some filaments and prominences end their life with eruptions. When this occurs, they disappear from the surface of the Sun within a few hours. Such events are known as disappearing filaments and it is thought that they are associated with coronal mass ejections (CMEs). Filament disappearances are generally monitored by observing and analysing successive solar H-alpha images. After filament regions are obtained from individual H-alpha images, a NN classifier is used to categorize the detected filaments as Disappeared Filaments (DFs) or Miss-Detected Filaments (MDFs). Features such as Area, Length, Mean, Standard Deviation, Skewness and Kurtosis are extracted and fed to this neural network which achieves a confidence level of at least 80%. Comparing the results with other researchers shows high divergence between the results. The NN method shows better convergence with the results of the National Geophysical Data Centre (NGDC) than the results of the others researchers.

Based on a study of existing solar filament and tracking methods, a fully automated solar filament detection and tracking method is presented. An adaptive thresholding technique is used in a segmentation phase to identify candidate filament pixels. This phase is followed by retrieving the actual filament area from a region grown filament by using statistical parameters and morphological operations. This detection technique gives the opportunity to develop an accurate spine extraction algorithm. Features including separation distance, orientation and average intensities are extracted and fed to a Neural Network (NN) classifier to merge broken filament components. Finally, the results for two consecutive images are compared to detect filament disappearance events, taking advantage of the maps resulting from converting solar images to Heliographic Carrington co-ordinates. The study has demonstrated the novelty of the algorithms developed in terms of them now all being fully automated; significantly the algorithms do not require any empirical values to be used whatsoever unlike previous techniques. This combination of features gives the opportunity for these methods to work in real-time. Comparisons with other researchers shows that the present algorithms represent the filaments more accurately and evaluate computationally faster - which could lead to a more precise tracking practice in real-time. An additional development phase developed in this dissertation in the process of detecting solar filaments is the detection of filament disappearances. Some filaments and prominences end their life with eruptions. When this occurs, they disappear from the surface of the Sun within a few hours. Such events are known as disappearing filaments and it is thought that they are associated with coronal mass ejections (CMEs). Filament disappearances are generally monitored by observing and analysing successive solar H-alpha images. After filament regions are obtained from individual H-alpha images, a NN classifier is used to categorize the detected filaments as Disappeared Filaments (DFs) or Miss-Detected Filaments (MDFs). Features such as Area, Length, Mean, Standard Deviation, Skewness and Kurtosis are extracted and fed to this neural network which achieves a confidence level of at least 80%. Comparing the results with other researchers shows high divergence between the results. The NN method shows better convergence with the results of the National Geophysical Data Centre (NGDC) than the results of the others researchers.

No / We present an automated technique for comparison of magnetic field inversion-line maps from SOHO/MDI magnetograms with solar filament data from the Solar Feature Catalogue created as part of the European Grid of Solar Observations project. The Euclidean distance transform and connected component labelling are used to identify nearest inversion lines to filament skeletons. Several filament inversion-line characteristics are defined and used to automate the decision whether a particular filament/inversion-line pair is suitable for quantitative comparison of orientation and separation. The technique is tested on 551 filaments from 14 H¿ images at various dates, and the distributions of angles and distances between filament skeletons and line-of-sight (LOS) magnetic inversion lines are presented for six levels of magnetic field smoothing. The results showed the robustness of the developed technique which can be applied for a statistical analysis of magnetic field in the vicinity of filaments. The method accuracy is limited by the static filament detection which does not distinguish between filaments, fibrils, pre-condensations and filament barbs and this may increase the asymmetries in magnetic distributions and broadening in angular distributions that requires the incorporation of a feature tracking technique.

Magnetic fields are fundamental to the structure and dynamics of the Sun’s corona. Observations show them to be locally complex, with highly sheared and twisted fields visible in solar filaments/prominences. The free magnetic energy contained in such fields is the primary source of energy for coronal mass ejections, which are important—but still poorly understood drivers of space weather in the near-Earth environment. In this thesis, a new model is developed for the evolution of the large-scale magnetic field in the global solar corona. The model is based on observations of the radial magnetic field on the solar photosphere (visible surface). New active regions emerge, and their transport and dispersal by surface motions are simulated accurately with a surface flux transport model. The 3D coronal magnetic field is evolved in response to these photospheric motions using a magneto-frictional technique. The resulting sequence of nonlinear force-free equilibria traces the build-up of magnetic helicity and free energy over many months. The global model is applied to study two phenomena: filaments and coronal mass ejections. The magnetic field directions in a large sample of observed filaments are compared with a 6-month simulation. Depending on the twist of newly-emerging active regions, the correct chirality is simulated for up to 96% of filaments tested. On the basis of these simulations, an explanation for the observed hemispheric pattern of filament chirality is put forward, including why exceptions occur for filaments in certain locations. Twisted magnetic flux ropes develop in the simulations, often losing equilibrium and lifting off, removing helicity. The physical basis for such losses of equilibrium is demonstrated through 2D analytical models. In the 3D global simulations, the twist of emerging regions is a key parameter controlling the number of lift-offs, which may explain around a third of observed coronal mass ejections.

Orientadores: André Leon Sampaio Gradvohl, Vitor Rafael Coluci / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Tecnologia / Made available in DSpace on 2018-08-21T23:26:09Z (GMT). No. of bitstreams: 1 Andrijauskas_Fabio_M.pdf: 2796809 bytes, checksum: 9fd4e03f6038d482ed05a64517bb1780 (MD5) Previous issue date: 2013 / Resumo: A quantidade de imagens astronômicas geradas cresce diariamente, além da quantidade já obtida e armazenada. Uma grande fonte de dados são imagens solares, cujo estudo pode detectar eventos que têm a capacidade de afetar as telecomunicações, transmissão de energia elétrica e outros sistemas na Terra. Para que tais eventos sejam detectados, torna-se necessário analisar essas imagens de forma eficiente, levando em conta os aspectos de armazenamento, processamento e visualização. Agregar algoritmos de processamento de imagem e técnicas de computação de alto desempenho facilita o tratamento da informação de forma correta e em tempo reduzido. As técnicas de computação para alto desempenho utilizadas neste trabalho foram desenvolvidas para sistemas híbridos, isto é, aqueles que utilizam uma combinação de sistemas de memórias compartilhada e distribuída. Foram produzidas versões paralelas para sistemas híbridos de técnicas já estabelecidas. Além disso, novas técnicas foram propostas e testadas para esse sistema tais como o Filamento Diffusion Detection. Para avaliar a melhora no desempenho, foram feitas comparações entre as versões seriais e paralelas. Esse texto também apresenta um sistema com capacidade para armazenar, processar e visualizar as imagens solares. Em uma das técnicas de detecção de filamentos, o processo foi acelerado 120 vezes e um processo auxiliar para a detecção de áreas mais brilhantes foi 155 vezes mais rápido do que a versão serial / Abstract: The number of astronomical images produced grows daily, in addition to the amount already stored. Great sources of data are solar images, whose study can detect events which have the capacity to affect the telecommunications, electricity transmission and other systems on Earth. For such events being detected, it becomes necessary to treat these images in a coherent way, considering aspects of storage, processing and image visualization. Combining image processing algorithms and high performance computing techniques facilitates the handling of information accurately and in a reduced time. The techniques for high performance computing used in this work were developed for hybrid systems, which employ a combination of shared and distributed memory systems. Parallel version of some established techniques were produced for hybrid systems. Moreover, new techniques have been proposed and tested for this system. To evaluate the improvement in performance, comparisons were made between serial and parallel versions. In addition to the analysis, this text also presents a system with capacity to store, process and visualize solar images. In one of the techniques for detecting filaments, the process was accelerated 120 times. Also an auxiliary process for the detection of brighter areas was 155 times faster than the serial version / Mestrado / Tecnologia e Inovação / Mestre em Tecnologia

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).

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).