Denoising framework for X-ray absorption spectroscopy data.

Area of Science:

• Materials Science
• Spectroscopy
• Data Analysis

Background:

• X-ray absorption spectroscopy (XAS) is crucial for material characterization but faces challenges with low signal-to-noise ratios in spectra.
• Analyzing XAS spectra is difficult due to non-stationary features, energy-dependent noise, and non-uniform sampling.
• Existing denoising methods often underperform with these complex spectral characteristics.

Purpose of the Study:

• To introduce a novel stationarity warping approach for significantly improving XAS spectral denoising.
• To develop advanced denoising techniques, including Gaussian process regression and convolutional autoencoders.
• To package these methods into user-friendly Python software (XASDenoise) for XAS data analysis.

Main Methods:

• Developed a stationarity warping technique to transform XAS spectra into a stationary domain.
• Implemented advanced denoising algorithms: Gaussian process regression and convolutional autoencoders.
• Integrated warping and denoising methods into the XASDenoise Python package.

Main Results:

• Stationarity warping demonstrably improves denoising performance across various XAS datasets.
• The method enhances spectral feature preservation while effectively suppressing noise.
• Benchmarking confirms consistent performance enhancement when combined with different denoising techniques.

Conclusions:

• Stationarity warping is a versatile and effective preprocessing step for XAS denoising.
• The XASDenoise software provides accessible and powerful tools for improving XAS data quality.
• This work advances the reliable analysis of structural and electronic properties using XAS.