Bayesian approach to automatic mass-spectrum peak identification in atom probe tomography.

Area of Science:

• Materials Science
• Analytical Chemistry
• Computational Methods

Background:

• Manual identification of mass-spectrum peaks in atom-probe tomography is time-consuming and error-prone.
• Accurate peak identification is crucial for reliable reconstruction of atomic-scale sample models.

Purpose of the Study:

• To develop an automated and robust Bayesian approach for mass-spectrum peak identification.
• To enhance the efficiency and accuracy of atom-probe tomography reconstruction.

Main Methods:

• A Bayesian framework for ranking candidate ions based on posterior probabilities.
• Iterative reconstruction of sample models incorporating prior information and error models.
• Development of a 'sliding window' method using Fisher information for peak decomposition.

Main Results:

• The Bayesian approach reliably identifies mass-spectrum peaks in inorganic samples.
• Reconstructed sample models are consistent with manual analysis results.
• The 'sliding window' method provides accurate and efficient peak decomposition.

Conclusions:

• The proposed Bayesian method offers a significant improvement over manual peak identification in atom-probe tomography.
• This automated approach enhances the reliability and efficiency of materials characterization.
• The developed techniques are applicable to time-of-flight mass spectrometry data analysis.