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Learning Atom Probe Tomography time-of-flight peaks for mass-to-charge ratio spectrometry



Kevin J. Coakley, Norman A. Sanford


In laser-assisted atom probe tomography, an important goal is to reconstruct the mass-to-charge ratio, (m/z), spectrum due to various ion species. In general, the probability mass function (pmf) associated with the time-of- flight (TOF) spectrum produced by each ion species is unknown and varies from species-to-species. Moreover, measuring pmfs for distinct ion species in calibration experiments is not practical. Here, we present a mixture model method to determine TOF pmfs that can vary from peak-to-peak. In this approach, we determine weights of candidate pmfs with a maximum likelihood method. In a proof-of-principle study, we apply our method to a TOF spectrum acquired from a silicon sample and determine intensity estimates of singly charged isotopes of silicon.


Atom Probe Tomography, Cross-validation, Expectation Maximization algorithm, Machine learning, Maximum likelihood, Mixture model, Silicon isotopes


Coakley, K. and Sanford, N. (2022), Learning Atom Probe Tomography time-of-flight peaks for mass-to-charge ratio spectrometry, Ultramicroscopy, [online],, (Accessed May 26, 2024)


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Created April 2, 2022, Updated November 29, 2022