Application of Statistical Regularization Methods to Determine the Differential Emission Measure

Physics – Atomic Physics

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Scientific paper

We investigate the statistical regularization inversion technique described by Craig and Brown (1986). Regularized inversion techniques utilize an a-priori smoothness constraint to stabilize the inversion. Standard regularization methods rely on minimization of residual errors to determine the amount of smoothing, typically leading to over-smoothed solutions. In statistical regularization, however, the amount of smoothing is determined by balancing the residual error of the solutions with the stability of the inversions. Stability is determined by the dispersions in the solutions for random realizations of the data consistent with the observational errors. This technique prevents over-smoothing, and the dispersions provide an estimate of the errors associated with the recovered source function. We use this technique to determine the differential emission measure (DEM) in stellar flare transition regions. The DEM is defined as xi (T)=n_e(2) / |{dlnT / dz}|, and is related to emission line flux F by the integral equation F = int G(T) xi (T){dT/ T}, where G(T) is the temperature dependent emissivity. Inversions were performed on model data for strong transition region lines observable by IUE. These data were derived by assuming three different forms for the source function (DEM) consistent with current energy transport theory. The kernel functions (emissivities), were derived with recent atomic physics data. Tests performed on model data showed the technique to be stable to data errors of up to 25\ agreed with models to 50\ consistent with observational errors of IUE line fluxes. Having proven the technique's applicability to a wide range of possible source functions, we extend the method to M dwarf flare emission data. Using IUE transition region line fluxes, we determine the differential emission measures for flares on AD Leo, EQ Pegasi, AT Mic, Proxima Centauri, and Gleise 867a. These results are compared with the differential emission measure computed from three energy transport models.

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