Gaussian mixture model clustering algorithms for the analysis of high-precision mass measurements

Weber, C. M.; Ray, D.; Valverde, A. A.; Clark, J. A.; Sharma, K. S.

doi:10.1016/j.nima.2021.166299

Title: Gaussian mixture model clustering algorithms for the analysis of high-precision mass measurements

Journal Article · Wed Jan 05 00:00:00 UTC 2022 · Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment

DOI: https://doi.org/10.1016/j.nima.2021.166299 · OSTI ID:1924253

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Argonne National Laboratory (ANL), Lemont, IL (United States)
Argonne National Laboratory (ANL), Lemont, IL (United States); University of Manitoba, Winnipeg, MB (Canada)
University of Manitoba, Winnipeg, MB (Canada)

The development of the phase-imaging ion-cyclotron resonance (PI-ICR) technique for use in Penning trap mass spectrometry (PTMS) increased the speed and precision with which PTMS experiments can be carried out. In PI-ICR, data sets of the locations of individual ion hits on a detector are created showing how ions cluster together into spots according to their cyclotron frequency. Ideal data sets would consist of a single, 2D-spherical spot with no other noise, but in practice data sets typically contain multiple spots, non-spherical spots, or significant noise, all of which can make determining the locations of spot centers non-trivial. A method for assigning groups of ions to their respective spots and determining the spot centers is therefore essential for further improving precision and confidence in PI-ICR experiments. Here, we present the class of Gaussian mixture model (GMM) clustering algorithms as an optimal solution. We show that on simulated PI-ICR data, several types of GMM clustering algorithms perform better than other clustering algorithms over a variety of typical scenarios encountered in PI-ICR. The mass spectra of 163Gd, ¹⁶³mGd, ¹⁶²Tb, and ¹⁶²mTb measured using PI-ICR at the Canadian Penning trap mass spectrometer were checked using GMMs, producing results that were in close agreement with the previously published values.

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Research Organization:: Argonne National Laboratory (ANL), Lemont, IL (United States)

Sponsoring Organization:: Natural Sciences and Engineering Research Council (NSERC); USDOE; USDOE Office of Science (SC), Nuclear Physics (NP); USDOE Office of Science (SC), Workforce Development for Teachers and Scientists (WDTS)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1924253

Journal Information:: Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment, Journal Name: Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment Vol. 1027; ISSN 0168-9002

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY
Gaussian mixture models
PI-ICR
clustering algorithms
machine learning
mass spectrometry
penning trap

Title: Gaussian mixture model clustering algorithms for the analysis of high-precision mass measurements

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