Evaluating the Uncertainties in the Electron Temperature and Radial Speed Measurements Using White Light Corona Eclipse Observations

We examine the uncertainties in two plasma parameters from their true values in a simulated asymmetric corona. We use the Corona Heliosphere (CORHEL) and Magnetohydrodynamics Around the Sphere (MAS) models in the Community Coordinated Modeling Center (CCMC) to investigate the differences between an assumed symmetric corona and a more realistic, asymmetric one. We were able to predict the electron temperatures and electron bulk flow speeds to within ±?0.5 MK and ±?100 km?s^?1, respectively, over coronal heights up to 5.0 R_⊙ from Sun center. We believe that this technique could be incorporated in next-generation white-light coronagraphs to determine these electron plasma parameters in the low solar corona. We have conducted experiments in the past during total solar eclipses to measure the thermal electron temperature and the electron bulk flow speed in the radial direction in the low solar corona. These measurements were made at different altitudes and latitudes in the low solar corona by measuring the shape of the K-coronal spectra between 350 nm and 450 nm and two brightness ratios through filters centered at 385.0 nm/410.0 nm and 398.7 nm/423.3 nm with a bandwidth of ≈?4 nm. Based on symmetric coronal models used for these measurements, the two measured plasma parameters were expected to represent those values at the points where the lines of sight intersected the plane of the solar limb.     

Dependence of DOLP on Coronal Electron Temperature,Speed, and Structure

We study the predictive capabilities of magnetic-feature properties (MF) generated by the Solar Monitor Active Region Tracker (SMART: Higgins et al. in Adv. Space Res. 47, 2105, 2011) for solar-flare forecasting from two datasets: the full dataset of SMART detections from 1996 to 2010 which has been previously studied by Ahmed et al. (Solar Phys. 283, 157, 2013) and a subset of that dataset that only includes detections that are NOAA active regions (ARs). The main contributions of this work are: we use marginal relevance as a filter feature selection method to identify the most useful SMART MF properties for separating flaring from non-flaring detections and logistic regression to derive classification rules to predict future observations. For comparison, we employ a Random Forest, Support Vector Machine, and a set of Deep Neural Network models, as well as lasso for feature selection. Using the linear model with three features we obtain significantly better results (True Skill Score: TSS = 0.84) than those reported by Ahmed et al. (Solar Phys. 283, 157, 2013) for the full dataset of SMART detections. The same model produced competitive results (TSS = 0.67) for the dataset of SMART detections that are NOAA ARs, which can be compared to a broader section of flare-forecasting literature. We show that more complex models are not required for this data.