Incorporating Atomic Data Errors in Stellar DEM Reconstruction

Astronomy and Astrophysics – Astrophysics

Scientific paper

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Stellar Atmospheres, Atomic Collisions, Markov Processes, Monte Carlo Methods, Stellar Atmospheres, Radiative Transfer, Opacity And Line Formation, Atomic And Molecular Data, Spectra, And Spectral Parameters

Scientific paper

We develop a powerful new method to reconstruct stellar Differential Emission Measures (DEMs) its Bayesian framework allows us to incorporate atomic and calibration errors as prior information. For instance, known errors in the line locations, as well as lines missing from the atomic data base, can be included directly during fitting. Highly correlated systematic errors in the ion balance may be included as well, as a natural sequence during Monte Carlo sampling. Our method uses the statistical framework of data augmentation, where we treat photon counts in each level of a hierarchical structure as missing data. We demonstrate our method by fitting a selected subset of emission lines and continuum in Chandra and EUVE data of Capella to estimate the DEM that best describes the data, and simultaneously determine the element abundances. The Markov Chain Monte Carlo based method also naturally produces error estimates on the fit parameters.

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