Representation of solar features in 3D for creating visual solar catalogues
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AbstractIn this study a method for 3D representation of active regions and sunspots that are detected from Solar and Heliospheric Observatory/Michelson Doppler Imager magnetogram and continuum images is provided. This is our first attempt to create a visual solar catalogue. Because of the difficulty of providing a full description of data in text based catalogues, it can be more accurate and effective for scientist to search 3D solar feature models and descriptions at the same time in such a visual solar catalogue. This catalogue would improve interpretation of solar images, since it would allow us to extract data embedded in various solar images and visualize it at the same time. In this work, active regions that are detected from magnetogram images and sunspots that are detected from continuum images are represented in 3D coordinates. Also their properties extracted from text based catalogues are represented at the same time in 3D environment. This is the first step for creating a 3D solar feature catalogue where automatically detected solar features will be presented visually together with their properties.
CitationColak T, Qahwaji RSR, Ipson SS and Ugail H (2011) Representation of solar features in 3D for creating visual solar catalogues. Advances in Space Research. 47(12): 2092-2104.
Link to publisher’s versionhttp://dx.doi.org/10.1016/j.asr.2010.08.030
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Solar Feature Catalogues in EGSOZharkova, Valentina V.; Aboudarham, J.; Zharkov, Sergei I.; Ipson, Stanley S.; Benkhalil, Ali K.; Fuller, N. (Springer, 2005)The Solar Feature Catalogues (SFCs) are created from digitized solar images using automated pattern recognition techniques developed in the European Grid of Solar Observation (EGSO) project. The techniques were applied for detection of sunspots, active regions and filaments in the automatically standardized full-disk solar images in Caii K1, Caii K3 and H¿ taken at the Meudon Observatory and white-light images and magnetograms from SOHO/MDI. The results of automated recognition are verified with the manual synoptic maps and available statistical data from other observatories that revealed high detection accuracy. A structured database of the Solar Feature Catalogues is built on the MySQL server for every feature from their recognized parameters and cross-referenced to the original observations. The SFCs are published on the Bradford University web site http://www.cyber.brad.ac.uk/egso/SFC/ with the pre-designed web pages for a search by time, size and location. The SFCs with 9 year coverage (1996¿2004) provide any possible information that can be extracted from full disk digital solar images. Thus information can be used for deeper investigation of the feature origin and association with other features for their automated classification and solar activity forecast.
Engineering System Design for Automated Space Weather Forecast. Designing Automatic Software Systems for the Large-Scale Analysis of Solar Data, Knowledge Extraction and the Prediction of Solar Activities Using Machine Learning Techniques.Qahwaji, Rami S.R.; Ipson, Stanley S.; Alomari, Mohammad H. (University of BradfordSchool of Computing, Informatics & Media, 2010-03-03)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).
Automated Technique For Comparison Of Magnetic Field Inversion Lines With Filament Skeletons From The Solar Feature Catalogue.Ipson, Stanley S.; Zharkova, Valentina V.; Zharkov, Sergei I.; Benkhalil, Ali K.; Aboudarham, J.; Fuller, N. (2005)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.