Sparse Bayesian Inference and the Temperature Structure of the Solar Corona

Abstract

Measuring the temperature structure of the solar atmosphere is critical to understanding how it is heated to high temperatures. Unfortunately, the temperature of the upper atmosphere cannot be observed directly, but must be inferred from spectrally resolved observations of individual emission lines that span a wide range of temperatures. Such observations are "inverted" to determine the distribution of plasma temperatures along the line of sight. This inversion is ill-posed and, in the absence of regularization, tends to produce wildly oscillatory solutions. We introduce the application of sparse Bayesian inference to the problem of inferring the temperature structure of the solar corona. Within a Bayesian framework a preference for solutions that utilize a minimum number of basis functions can be encoded into the prior and many ad hoc assumptions can be avoided. We demonstrate the efficacy of the Bayesian approach by considering a test library of 40 assumed temperature distributions.

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References in corpus (3)

• Thermal Diagnostics with the Atmospheric Imaging Assembly onboard the Solar Dynamics Observatory: A Validated Method for Differential Emission Measure Inversions
• Observations of Active Region Loops with the EUV Imaging Spectrometer on Hinode
• The Evolution of the EM Distribution in the Core of an Active Region

Cited by in corpus (2)

• Nanoflare Diagnostics from Magnetohydrodynamic Heating Profiles
• The temperature and emission measure distribution in the quiet and active solar corona: a Bayesian approach