A statistical study of the inferred transverse density profile of coronal loop threads observed with SDO/AIA

C. R. Goddard, D. J. Pascoe, S. Anfinogentov, V. M. Nakariakov
2017 Astronomy and Astrophysics  
Aims. We carry out a statistical study of the inferred coronal loop cross-sectional density profiles using extreme ultraviolet (EUV) imaging data from the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO). Methods. We analysed 233 coronal loops observed during 2015/2016. We consider three models for the density profile; the step function (model S ), the linear transition region profile (model L), and a Gaussian profile (model G). Bayesian inference is used to
more » ... nce is used to compare the three corresponding forward modelled intensity profiles for each loop. These are constructed by integrating the square of the density from a cylindrical loop cross-section along the line of sight, assuming an isothermal cross-section, and applying the instrumental point spread function. Results. Calculating the Bayes factors for comparisons between the models, it was found that in 47% of cases there is very strong evidence for model L over model S and in 45% of cases very strong evidence for model G over S . Using multiple permutations of the Bayes factor the favoured density profile for each loop was determined for multiple evidence thresholds. There were a similar number of cases where model L or G are favoured, showing evidence for inhomogeneous layers and constantly varying density cross-sections, subject to our assumptions and simplifications. Conclusions. For sufficiently well resolved loop threads with no visible substructure it has been shown that using Bayesian inference and the observed intensity profile we can distinguish between the proposed density profiles at a given AIA wavelength and spatial resolution. We have found very strong evidence for inhomogeneous layers, with model L being the most general, and a tendency towards thicker or even continuous layers.
doi:10.1051/0004-6361/201731023 fatcat:3qslctjnfjbk7mczdhvvygjtpi