Constraining Coronal Heating: Employing Bayesian Analysis Techniques to Improve the Determination of Solar Atmospheric Plasma Parameters

Abstract

One way of revealing the nature of the coronal heating mechanism is by comparing simple theoretical one-dimensional hydrostatic loop models with observations at the temperature and/or density structure along these features. The most well-known method for dealing with comparisons like that is the χ² approach. In this paper we consider the restrictions imposed by this approach and present an alternative way for making model comparisons using Bayesian statistics. In order to quantify our beliefs we use Bayes factors and information criteria such as AIC and BIC. Two datasets (Ugarte-Urra et al.2005; Priest et al.2000) are reanalyzed using the method described above. For the dataset of Ugarte-Urra et al. (2005), we conclude to apex dominant heating as the likely heating candidate, whereas the dataset of Priest et al. (2000) implies basal heating. Note that these new results are different from those obtained using the chi-squared statistic. For this we suggest that proper usage of Classical and Bayesian statistics should be applied in order to make safe assumptions about the nature of the coronal heating mechanisms.