Bayes-Turchin approach to XAS analysis.

Area of Science:

• Materials Science
• Spectroscopy
• Computational Chemistry

Background:

• Traditional X-ray absorption fine structure (XAFS) analysis relies on least-squares fitting.
• Existing methods often have limitations regarding model parameter space and require Fourier filtering.

Purpose of the Study:

• To introduce and evaluate the Bayes-Turchin method as an alternative for X-ray absorption spectra (XAS) analysis.
• To demonstrate the method's capability in analyzing both XAFS and X-ray absorption near-edge spectra (XANES).

Main Methods:

• The Bayes-Turchin method is applied, leveraging a priori estimates of model parameters and their uncertainties.
• Linear equations for model parameters are regularized using the 'Turchin condition'.
• Parameter space is naturally partitioned into relevant and irrelevant subspaces.

Main Results:

• The Bayes-Turchin method allows analysis of the full XAS, encompassing both XAFS and XANES regions.
• It avoids restrictions on model parameter space and the need for Fourier filtering.
• Effective analysis of XANES spectra is achieved through fits to multiple-scattering calculations, even with short data ranges.

Conclusions:

• The Bayes-Turchin method provides a more robust and comprehensive approach to XAS analysis compared to traditional methods.
• Its ability to incorporate prior information and handle full spectra makes it suitable for complex spectral analysis, including XANES.
• The method demonstrates reliable performance even with limited experimental data, offering flexibility in spectral analysis.