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The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title: Understanding the role of magnetic field evolution in the initiation and development of solar eruptions
Author: Liu, Nian
View Online: njit-etd2022-039
(xviii, 101 pages ~ 22.4 MB pdf)
Department: Department of Physics
Degree: Doctor of Philosophy
Program: Applied Physics
Document Type: Dissertation
Advisory Committee: Wang, Haimin (Committee co-chair)
Jing, Ju (Committee co-chair)
Kosovichev, Alexander G. (Committee member)
Wang, Jason T. L. (Committee member)
Maitra, Neepa T. (Committee member)
Date: 2022-08
Keywords: Solar flares
Solar physics
Availability: Unrestricted
Abstract:

This dissertation aims to understand the initiation and evolution of solar eruptions. The essential science questions to answer include: What is the role of magnetohydro dynamic (MHD) instabilities and magnetic reconnection in triggering and driving eruptions? What are the role of Kink Instability (KI) and Torus Instability (TI) in determining the successful and failed eruptions? What is the thermal behavior of flare precursors in the initiation stage of solar eruptions? Finally, how does the corona magnetic field respond to the flare eruptions? The dissertation mainly includes the following studies.

First, this dissertation presents a multi-instrument study of the two precursor brightenings prior to the M6.5 flare with a focus on their thermal behavior in terms of time variations of temperature (T), electron number density (n), and emission measure (EM). This study quantitatively describes the differences in the thermal parameters at the precursor phase, measured by different instruments operating at different wavelength regimes and for different emission mechanisms. The precursor brightenings in the passbands of Hard X-rays (HXR), extreme ultraviolet (EUV), microwave (MW), and Ha are found to occur within a strong magnetic field region (1200 G) around the flaring polarity inversion line (PIL). Such a small energy release in the lower atmosphere may be related to the onset of the main flare.

Second, a statistical analysis of torus instability (TI) and kink instability (KI) in solar eruptions is presented, in order to improve our understanding of the likelihood of a CME based on the observed TI parameter decay index and KI parameter twist number. It is found that TI plays an important role in distinguishing between ejective and confined flares, while KI is much less influential. However, TI is not a necessary condition for eruption. Some magnetic flux ropes (MFRs) in the TI-stable regime still manage to break through the strong strapping field and evolve into CMEs. It, therefore, implies that an additional driving mechanism, such as magnetic reconnection, may be involved in eruptions.

Third, a study of the magnetic field evolution of the X5.4 flare with two magnetic field extrapolation methods: Non-linear-force-free field (NLFFF) and Non-force-free field (Non-FFF) extrapolations are included in this dissertation. It is found that this flare is most likely triggered by the tether-cutting reconnection and the subsequent DAI. Clear 3D back-reactions of increasing horizontal magnetic field (Bh) and decreasing inclination angle (F) of the magnetic field from the photosphere are presented in both NLFFF and Non-FFF. The back-reaction of the increasing downward Lorentz force (Fz) acting on the photosphere produced by the Non-FFF result spatially correlates with the flare initiating location in the DAI analysis.

Last but not least, a study of analyzing the magnetic field structure of the sunspot light bridges in AR 12371 during the M6.5 flare is included in this dissertation. Analysis of the 3D NLFFF model shows a low-lying 3D magnetic canopy as well as a 3D current system. The most substantial difference between the LBs and umbrae is found in the overall magnetic topology in that the field lines emanating from the two LBs are more twisted than that from the neighboring umbrae.

At the end, this dissertation briefly introduces the SolarDB, a cyberinfrastructure built for flare studies and photospheric vector magnetic field reconstruction taking advantage of machine learning (ML)/ deep learning (DL) tools, and future work about the MHD simulation for solar eruptions.


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