AbstractWave motions have been detected throughout the solar atmosphere for many decades, and their ability to transport energy through the atmosphere has sparked wide investigation into the role of waves in the heating of the upper solar atmosphere. Strong predominantly vertical magnetic fields within sunspots provide ideal conduits for waves generated by subphotospheric p-modes to propagate upwards through the photosphere and chromosphere, where their energy may be dissipated. In the study of atmospheric wave signatures, observations are acquired at a range of atmospheric heights by utilising a variety of spectral lines. However, weak chromospheric absorption lines are often temperature sensitive, and readily capture shock fronts as optically thin emission. This presence of multiple spectral components within line profiles makes it challenging to determine accurate plasma Doppler (line-of-sight) velocities, resulting in the underutilisation of available data. As well as studying wave signatures within solar observations, numerical magnetohydrodynamic (MHD) simulations of waves propagating through a model solar atmosphere are regularly utilised in the development of wave heating theories. It is typically assumed that the solar atmosphere is a fully ionised plasma, however, owing to a reduced temperature in the lower solar atmosphere, the plasma is often only partially ionised. Due to the decoupled neutral and charged components within the partially ionised solar atmosphere, processes such as ambipolar diffusion occur. By modifying the underlying physics of MHD simulations, the role of ambipolar diffusion in propagating wave characteristics can be studied within an idealised partially ionised photosphere and chromosphere.
In the first study, a novel method is presented that uses machine learning to detect the presence of multiple spectral components within observed spectral line profiles. Each spectral component within the profile is subsequently constrained through single or multiple Voigt fits, which allows active and quiescent components to be isolated for further analysis. A proof of concept study is presented, which benchmarks the application of the method to a Ca ɪɪ 8542 Å spectral imaging dataset, to assess its applicability for observational datasets typical of sunspot chromospheres. Minimisation tests are performed between the observed and fitted line profiles to verify the reliability of the results. Median reduced chi-squared values of 1.03 are achieved for the umbral line profiles.
In the second study, the Mᴀɴᴄʜᴀ3D numerical code is employed to investigate the role of ambipolar diffusion in magnetoacoustic waves propagating through the atmosphere immediately above the umbra of a sunspot. Simulations are performed both with and without ambipolar diffusion, where the non-ideal MHD equations are solved for data-driven perturbations to the magnetostatic equilibrium. Energy spectral densities are analysed, and evidence is presented suggesting that, within weakly ionised low density regions where the ambipolar diffusion coefficient is large, ambipolar diffusion has a key role in determining wave characteristics. It is therefore proposed that, when simulating and observing the lower solar atmosphere the effect of ambipolar diffusion is important and should be carefully considered.
|Date of Award||Jul 2023|
|Sponsors||Northern Ireland Department for the Economy|
|Supervisor||David Jess (Supervisor) & Michail Mathioudakis (Supervisor)|
- Ambipolar diffusion
- machine learning
- magnetoacoustic waves
- magnetohydrodynamic waves
- partial ionisation
- solar active regions
- solar atmosphere
- solar physics