The solar corona is still enigmatic from a scientific point of view, with important unsolved questions about its nature, such as solar wind acceleration and coronal heating (McComas et al. 2007; Parnell & De Moortel 2012). Although finding solutions to these questions is important on its own, it is also expected to have major implications for related fields, such as space weather (Singh et al. 2010), which is set to grow in importance as we make advances in technology and space exploration. One impediment toward solving coronal mysteries is the notorious difficulty in determining its key physical parameters, such as the magnetic field, by direct spectroscopic or polarimetric measurements. Early observational evidence of waves in coronal structures using SOHO/TRACE (Aschwanden et al. 1999; Berghmans & Clette 1999) paved the way for the previously theorized coronal seismology (Uchida 1970; Roberts et al. 1984) to be a tool for coronal plasma diagnostics. The first attempt to seismologically determine the magnetic field of transversely oscillating coronal loops was applied to the first such event observed by Nakariakov & Ofman (2001). Since then, coronal seismology has successfully been applied to numerous oscillation events (for reviews, see, e.g., De Moortel 2005; De Moortel & Nakariakov 2012; Stepanov et al. 2012). A common feature of all previously diagnosed events is their localization in time, i.e., single events. This obviously limits the applicability of seismology to the brief duration of the oscillation event. Moreover, these single-oscillation events tend to be rare, as they are mostly related to flaring events or eruptions, meaning that seismology is restricted to brief diagnostics highly localized in time, or in time and space for non-global oscillations. However, the discovery of ubiquitous propagating transverse oscillations with CoMP (Tomczyk et al. 2007; Tomczyk & McIntosh 2009) and SDO (McIntosh et al. 2011) or the more recently identified nearly ubiquitous, decay-less low-amplitude kink coronal loop oscillations (Anfinogentov et al. 2013, 2015; Nisticò et al. 2013), as well as oscillations in plumes (Thurgood et al. 2014), led to the possibility of continuous diagnostics in time, i.e., dynamic coronal seismology. Consequently, very recently, the first seismologic “magnetic field image” was obtained, based on the ubiquitous transverse waves (Long et al. 2017), using a methodology put forth by Morton et al. (2015). In this study, the authors use the magnetohydrodynamic (MHD) kink phase speed of a flux tube (Edwin & Roberts 1983) as the inversion tool for the magnetic field. These ubiquitous propagating transverse waves are now widely regarded as Alfvénic waves (Goossens et al. 2012), although this reinterpretation of the nature of the waves does not modify the phase speed formula used in the inversion. However, it is still assumed that the observed ubiquitous waves are transverse oscillations of flux tubes, and while the fine structure in the corona is still unknown (Peter et al. 2013; Reale 2014; Aschwanden & Peter 2017), it is unlikely that the magnetic cylinder model is satisfactory. Structuring across the magnetic field, among other factors (De Moortel & Pascoe 2009; Pascoe & De Moortel 2014), is an important detail for seismology, as it can greatly influence the nature and propagation of MHD waves (Luna et al. 2010; Terradas et al. 2010; Verth et al. 2010), altering the dependence of the observed phase speed on physical properties such as the magnetic field or mass density (e.g., Verth et al. 2007; Arregui et al. 2013), which might lead to erroneous inversions if not considered. Some light was shed recently on the weaknesses of modeling the corona as a bundle of independent thin magnetic strands by Magyar & Van Doorsselaere (2016). In their simulations, a loop consisting of packed strands is quickly deformed and mixed when disturbed by propagating transverse waves, leading to a turbulent cross-section (Magyar et al. 2017). This result reiterates the above-mentioned need to move away from rigid cylindrical models in favor of more realistic descriptions that account for nonlinearities.
Citation: McIntosh et al. 2011
Citation function: Background; Background; Background