Development of advanced algorithms to detect, characterize and forecast solar activities

Study of the solar activity is an important part of space weather research. It is facing serious challenges because of large data volume, which requires application of state-of-the-art machine learning and computer vision techniques. This dissertation targets at two essential aspects in space weather research: automatic feature detection and forecasting of eruptive events. Feature detection includes solar filament detection and solar fibril tracing. A solar filament consists of a mass of gas suspended over the chromosphere by magnetic fields and seen as a dark, ribbon-shaped feature on the bright solar disk in Hα (Hydrogen-alpha) full-disk solar images. In this dissertation, an automatic solar filament detection and characterization method is presented. The investigation illustrates that the statistical distribution of the Laplacian filter responses of a solar disk contains a special signature which can be used to identify the best threshold value for solar filament segmentation. Experimental results show that this property holds across different solar images obtained by different solar observatories. Evaluation of the proposed method shows that the accuracy rate for filament detection is more than 95% as measured by filament number and more than 99% as measured by filament area, which indicates that only a small fraction of tiny filaments are missing from the detection results. Comparisons indicate that the proposed method outperforms a previous method. Based on the proposed filament segmentation and characterization method, a filament tracking method is put forward, which is capable of tracking filaments throughout their disk passage. With filament tracking, the variation of filaments can be easily recorded. Solar fibrils are tiny dark threads of masses in Hα images. It is generally believed that fibrils are magnetic field-aligned, primarily due to the reason that the high electrical conductivity of the solar atmosphere freezes the ionized mass in magnetic field lines and prevents them from diffusing across the lines. In this dissertation, a method that automatically segments and models fibrils from Hα images is proposed. Experimental results show that the proposed method is very successful to derive traces of most fibrils. This is critical for determining the non-potentiality of active regions. Solar flares are generated by the sudden and intense release of energy stored in solar magnetic fields, which can have a significant impact on the near earth space environment (so called space weather). In this dissertation, an automated solar flare forecasting method is presented. The proposed method utilizes logistic regression and SVM (support vector machine) to forecast the occurrences of solar flares based on photospheric magnetic features. Logistic regression is used to derive the probabilities of solar flares occurrence, which are then fed to SVM for determining whether a flare will occur. Comparisons with existing methods show that there is an improvement in the accuracy of X-class solar flare forecasting. It is also found that when sunspot-group classification is combined with photospheric magnetic parameters, the performance of flare forecasting can be further lifted

First 3D Reconstructions of Coronal Loops with the STEREO A+B Spacecraft: IV. Magnetic Modeling with Twisted Force-Free Fields

The three-dimensional (3D) coordinates of stereoscopically triangulated loops provide strong constraints for magnetic field models of active regions in the solar corona. Here we use STEREO/A and B data from some 500 stereoscopically triangulated loops observed in four active regions (2007 Apr 30, May 9, May 19, Dec 11), together with SOHO/MDI line-of-sight magnetograms. We measure the average misalignment angle between the stereoscopic loops and theoretical magnetic field models, finding a mismatch of $\mu=19^\circ-46^\circ$ for a potential field model, which is reduced to $\mu=14^\circ-19^\circ$ for a non-potential field model parameterized by twist parameters. The residual error is commensurable with stereoscopic measurement errors ($\mu_{SE} \approx 8^\circ-12^\circ$). We developed a potential field code that deconvolves a line-of-sight magnetogram into three magnetic field components $(B_x, B_y, B_z)$, as well as a non-potential field forward-fitting code that determines the full length of twisted loops ($L \approx 50-300$ Mm), the number of twist turns (median $N_{twist}=0.06$), the nonlinear force-free $\alpha$-parameter (median $\alpha \approx 4 \times 10^{-11}$ cm$^{-1}$), and the current density (median $j_z \approx 1500$ Mx cm$^{-2}$ s$^{-1}$). All twisted loops are found to be far below the critical value for kink instability, and Joule dissipation of their currents is found be be far below the coronal heating requirement. The algorithm developed here, based on an analytical solution of nonlinear force-free fields that is accurate to second order (in the force-free parameter $\alpha$), represents the first code that enables fast forward-fitting to photospheric magnetograms and stereoscopically triangulated loops in the solar corona.Comment: The Astrophysical Journal (in press), 37 pages, 14 Figure

Machine learning and computer vision in solar physics

In the recent decades, the difficult task of understanding and predicting violent solar eruptions and their terrestrial impacts has become a strategic national priority, as it affects the life of human beings, including communication, transportation, the power grid, national defense, space travel, and more. This dissertation explores new machine learning and computer vision techniques to tackle this difficult task. Specifically, the dissertation addresses four interrelated problems in solar physics: magnetic flux tracking, fibril tracing, Stokes inversion and vector magnetogram generation. First, the dissertation presents a new deep learning method, named SolarUnet, to identify and track solar magnetic flux elements in observed vector magnetograms. The method consists of a data preprocessing component that prepares training data from a physics-based tool, a deep learning model implemented as a U-shaped convolutional neural network for fast and accurate image segmentation, and a postprocessing component that prepares tracking results. The tracking results can be used in deriving statistical parameters of the local and global solar dynamo, allowing for sophisticated analyses of solar activities in the solar corona and solar wind. Second, the dissertation presents another new deep learning method, named FibrilNet, for tracing chromospheric fibrils in Ha images of solar observations. FibrilNet is a Bayesian convolutional neural network, which adopts the Monte Carlo dropout sampling technique for probabilistic image segmentation with uncertainty quantification capable of handling both aleatoric uncertainty and epistemic uncertainty. The traced Ha fibril structures provide the direction of magnetic fields, where the orientations of the fibrils can be used as a constraint to improve the nonlinear force-free extrapolation of coronal fields. Third, the dissertation presents a stacked deep neural network (SDNN) for inferring line-of-sight (LOS) velocities and Doppler widths from Stokes profiles collected by GST/NIRIS at Big Bear Solar Observatory. Experimental results show that SDNN is faster, while producing smoother and cleaner LOS velocity and Doppler width maps, than a widely used physics-based method. Furthermore, the results demonstrate the better learning capability of SDNN than several related machine learning algorithms. The high-quality velocity fields obtained through Stokes inversion can be used to understand solar activity and predict solar eruptions. Fourth, the dissertation presents a generative adversarial network, named MagNet, for generating vector components to create synthetic vector magnetograms of solar active regions. MagNet allows us to expand the availability of photospheric vector magnetograms to the period from 1996 to present, covering solar cycles 23 and 24, where photospheric vector magnetograms were not available prior to 2010. The synthetic vector magnetograms can be used as input of physics-based models to derive important physical parameters for studying the triggering mechanisms of solar eruptions and for forecasting eruptive events. Finally, implementations of some of the deep learning-based methods using Jupyter notebooks and Google Colab with GitHub are presented and discussed

The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: Overview and Performance

The Helioseismic and Magnetic Imager (HMI) began near-continuous full-disk solar measurements on 1 May 2010 from the Solar Dynamics Observatory (SDO). An automated processing pipeline keeps pace with observations to produce observable quantities, including the photospheric vector magnetic field, from sequences of filtergrams. The primary 720s observables were released in mid 2010, including Stokes polarization parameters measured at six wavelengths as well as intensity, Doppler velocity, and the line-of-sight magnetic field. More advanced products, including the full vector magnetic field, are now available. Automatically identified HMI Active Region Patches (HARPs) track the location and shape of magnetic regions throughout their lifetime. The vector field is computed using the Very Fast Inversion of the Stokes Vector (VFISV) code optimized for the HMI pipeline; the remaining 180 degree azimuth ambiguity is resolved with the Minimum Energy (ME0) code. The Milne-Eddington inversion is performed on all full-disk HMI observations. The disambiguation, until recently run only on HARP regions, is now implemented for the full disk. Vector and scalar quantities in the patches are used to derive active region indices potentially useful for forecasting; the data maps and indices are collected in the SHARP data series, hmi.sharp_720s. Patches are provided in both CCD and heliographic coordinates. HMI provides continuous coverage of the vector field, but has modest spatial, spectral, and temporal resolution. Coupled with limitations of the analysis and interpretation techniques, effects of the orbital velocity, and instrument performance, the resulting measurements have a certain dynamic range and sensitivity and are subject to systematic errors and uncertainties that are characterized in this report.Comment: 42 pages, 19 figures, accepted to Solar Physic

The formation and eruption of magnetic flux ropes in solar and stellar coronae

Flux ropes are magnetic structures commonly found in the solar corona. They are thought to play an important role in solar flares and coronal mass ejections. Understanding their formation and eruption is of paramount importance for our understanding of space weather. In this thesis the magnetofrictional method is applied to simulate the formation of flux ropes and track their evolution up to eruption both in solar and stellar coronae. Initially, the coronal magnetic field of a solar active region is simulated using observed magnetograms to drive the coronal evolution. From the sequence of magnetograms the formation of a flux rope is simulated, and compared with coronal observations. Secondly a procedure to produce proxy SOLIS synoptic magnetograms from SDO/HMI and SOHO/MDI magnetograms is presented. This procedure allows SOLIS-like synoptic magnetograms to be produced during times when SOLIS magnetograms are not available. Thirdly, a series of scaling laws for the formation and life-times of flux ropes in stellar coronae are determined as a function of stellar differential rotation and surface diffusion. These scaling laws can be used to infer the response of stellar coronae to the transport of magnetic fields at their surface. Finally, global long-term simulations of stellar corona are carried out to determine the coronal response to flux emergence and differential rotation. A bipole emergence model is developed and is used in conjunction with a surface flux transport model in order to drive the global coronal evolution. These global simulations allow the flux, energy and flux rope distributions to be studied as a function of a star’s differential rotation and flux emergence rate

Automatic prediction of solar flares and super geomagnetic storms

Space weather is the response of our space environment to the constantly changing Sun. As the new technology advances, mankind has become more and more dependent on space system, satellite-based services. A geomagnetic storm, a disturbance in Earth\u27s magnetosphere, may produce many harmful effects on Earth. Solar flares and Coronal Mass Ejections (CMEs) are believed to be the major causes of geomagnetic storms. Thus, establishing a real time forecasting method for them is very important in space weather study. The topics covered in this dissertation are: the relationship between magnetic gradient and magnetic shear of solar active regions; the relationship between solar flare index and magnetic features of solar active regions; based on these relationships a statistical ordinal logistic regression model is developed to predict the probability of solar flare occurrences in the next 24 hours; and finally the relationship between magnetic structures of CME source regions and geomagnetic storms, in particular, the super storms when the index decreases below -200 nT is studied and proved to be able to predict those super storms. The results are briefly summarized as follows: (1) There is a significant correlation between magnetic gradient and magnetic shear of active region. Furthermore, compared with magnetic shear, magnetic gradient might be a better proxy to locate where a large flare occurs. It appears to be more accurate in identification of sources of X-class flares than M-class flares; (2) Flare index, defined by weighting the SXR flares, is proved to have positive correlation with three magnetic features of active region; (3) A statistical ordinal logistic regression model is proposed for solar flare prediction. The results are much better than those data published in the NASA/SDAC service, and comparable to the data provided by the NOAA/SEC complicated expert system. To our knowledge, this is the first time that logistic regression model has been applied in solar physics to predict flare occurrences; (4) The magnetic orientation angle θ, determined from a potential field model, is proved to be able to predict the probability of super geomagnetic storms (Dst ≤ -200nT). The results show that those active regions associated with |θ| \u3c 90° are more likely to cause a super geomagnetic storm

Long-period oscillations of active region patterns: least-squares mapping on second-order curves

Active regions (ARs) are the main sources of variety in solar dynamic events. Automated detection and identification tools need to be developed for solar features for a deeper understanding of the solar cycle. Of particular interest here are the dynamical properties of the ARs, regardless of their internal structure and sunspot distribution. We studied the oscillatory dynamics of two ARs: NOAA 11327 and NOAA 11726 using two different methods of pattern recognition. We developed a novel method of automated AR border detection and compared it to an existing method for the proof-of-concept. The first method uses least-squares fitting on the smallest ellipse enclosing the AR, while the second method applies regression on the convex hull.} After processing the data, we found that the axes and the inclination angle of the ellipse and the convex hull oscillate in time. These oscillations are interpreted as the second harmonic of the standing long-period kink oscillations (with the node at the apex) of the magnetic flux tube connecting the two main sunspots of the ARs. In both ARs we have estimated the distribution of the phase speed magnitude along the magnetic tubes (along the two main spots) by interpreting the obtained oscillation of the inclination angle as the standing second harmonic kink mode. After comparing the obtained results for fast and slow kink modes, we conclude that both of these modes are good candidates to explain the observed oscillations of the AR inclination angles, as in the high plasma $\beta$ regime the phase speeds of these modes are comparable and on the order of the Alfv\'{e}n speed. Based on the properties of the observed oscillations, we detected the appropriate depth of the sunspot patterns, which coincides with estimations made by helioseismic methods. The latter analysis can be used as a basis for developing a magneto-seismological tool for ARs.Comment: 10 pages, 6 figures, Accepted for publication in A&

Enhanced flare prediction by advanced feature extraction from solar images : developing automated imaging and machine learning techniques for processing solar images and extracting features from active regions to enable the efficient prediction of solar flares.

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