Quantifying the Significance of Substructure in Coronal Loops

Abstract:

A method to infer the presence of small-scale substructure in SDO/AIA (Atmospheric Imaging Assembly on the Solar Dynamics Observatory) images of coronal loops is developed. We can classify visible loop structure based on this propensity to show substructure which puts constraints on contemporary solutions to the coronal heating problem. The method uses the Bayesian algorithm Low-count Image Reconstruction and Analysis (LIRA) to infer the multi-scale component of the loops which describes deviations from a smooth model. The increase in contrast of features in this multi-scale component is determined using a statistic that estimates the sharpness across the image. Regions with significant substructure are determined using p-value upper bounds. We are able to locate substructure visible in Hi-C (High-Resolution Coronal Imager) data that are not salient features in the corresponding AIA image. Looking at coronal loops at different regions of the Sun (e.g., low-lying structure and loops in the upper corona) we are able to map where detectable substructure exists and thus the influence of the nanoflare heating process.

We acknowledge support from AIA under contract SP02H1701R from Lockheed-Martin to SAO.