Understanding Heating in Active Region Cores through Machine Learning. I. Numerical Modeling and Predicted Observables

Abstract

To adequately constrain the frequency of energy deposition in active region cores in the solar corona, systematic comparisons between detailed models and observational data are needed. In this paper, we describe a pipeline for forward modeling active region emission using magnetic field extrapolations and field-aligned hydrodynamic models. We use this pipeline to predict time-dependent emission from active region NOAA 1158 as observed by SDO/AIA for low-, intermediate-, and high-frequency nanoflares. In each pixel of our predicted multi-wavelength, time-dependent images, we compute two commonly-used diagnostics: the emission measure slope and the time lag. We find that signatures of the heating frequency persist in both of these diagnostics. In particular, our results show that the distribution of emission measure slopes narrows and the mean decreases with decreasing heating frequency and that the range of emission measure slopes is consistent with past observational and modeling work. Furthermore, we find that the time lag becomes increasingly spatially coherent with decreasing heating frequency while the distribution of time lags across the whole active region becomes more broad with increasing heating frequency. In a follow up paper, we train a random forest classifier on these predicted diagnostics and use this model to classify real AIA observations of NOAA 1158 in terms of the underlying heating frequency.

Multi-scale observations of thermal non-equilibrium cycles in coronal loops

“…We suggest, therefore, that the use of the 171-131 time lag information needs to be revisited and that 304 should be added in time-lag analyses. This is generally not the case even if the timelag map method is now widely used (Viall & Klimchuk 2017;Winebarger et al 2018;Barnes et al 2019).…”

IRIS Observations of Short-term Variability in Moss Associated with Transient Hot Coronal Loops

“…Analysis of EM(T) slopes from coronal observations of active regions (ARs) are generally compatible with high frequency coronal heating (e.g., Warren et al 2012;Del Zanna et al 2015b), though lowfrequency heating is found to also play a role depending on the evolutionary stage of the AR (e.g., Ugarte-Urra & Warren 2012). Additional diagnostics of coronal heating properties are provided for instance by the analysis of time lags imaging observations of AR in different passbands sensitive to different temperature (e.g., Viall & Klimchuk 2011;Winebarger et al 2018; Barnes et al 2019) with the Atmospheric Imaging Assembly (AIA, Lemen et al 2012) onboard the Solar Dynamics Observatory (SDO; Pesnell et al 2012). Studies of elemental abundances in ARs at different evolutionary stages (Baker et al 2015(Baker et al , 2018, or during impulsive heating events (e.g., Warren et al 2016) can also provide clues about heating properties, since the coronal abundances appear to vary with respect to photospheric abundances and this fractionation process is likely related to the heating process (e.g., Feldman 1992;Testa 2010;Testa et al 2015).…”

Modelling the solar transition region using an adaptive conduction method

“…Therefore, for studies of multiple loops forming an active region (Bradshaw & Viall 2016;Barnes et al 2019) and for long simulations Winebarger et al 2018), it is desirable to develop methods that mitigate the need for highly resolved numerical grids. Further, there is Article number, page 1 of 20 arXiv:2002.01887v2 [astro-ph.SR] 28 Feb 2020 also a need for a 1D code that can be run quickly in order to assess the viability of physical ideas.…”