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Activity Number: 11 - Statistical Inference for Solar and Geophysical Data
Type: Invited
Date/Time: Monday, August 3, 2020 : 10:00 AM to 11:50 AM
Sponsor: Astrostatistics Special Interest Group
Abstract #309283
Title: Effect of Systematic Uncertainties on Density and Temperature Estimates in Coronae of Capella
Author(s): Xixi Yu* and David van Dyk and David Stenning and Vinay Kashyap and Giulio Del Zanna
Companies: Imperial College London and Imperial College London and Imperial College London and Center for Astrophysics | Harvard & Smithsonian and Centre for Mathematical Sciences, University of Cambridge
Keywords: statistical method; Bayesian method; stellar corona

Information about the physical properties of astrophysical objects cannot be measured directly but is inferred by interpreting spectroscopic observations in the context of atomic physics calculations. A critical component of this analysis is understanding how uncertainties in the underlying atomic physics propagates to the uncertainties in the inferred plasma parameters. Instead of using the standard approach, a common strategy deployed by the astrophysicists, that treats the uncertainty as fixed and known and obtains the best-fit values of the parameters, we propose a multistage analysis to prevent underestimation of the error bars on the model parameters and increase the accuracy of the analysis results. A case study on Fe XVII and O VII/VIII is discussed where we implement both a pragmatic Bayesian method where atomic physics information is unaffected by observed data, and a fully Bayesian method where the data can be used to probe physics, and in particular detail a method of summarizing atomic uncertainties using principal components analysis.

Authors who are presenting talks have a * after their name.

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