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

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 sh...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment Jg. 1027; H. C; S. 166299
Hauptverfasser: Weber, C.M., Ray, D., Valverde, A.A., Clark, J.A., Sharma, K.S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier B.V 11.03.2022
Elsevier
Schlagworte:
Clustering algorithms
Gaussian mixture models
Machine learning
Mass spectrometry
Penning trap
PI-ICR
PI-ICR
Penning trap
Gaussian mixture models
Mass spectrometry
Clustering algorithms
Machine learning
ISSN:0168-9002, 1872-9576
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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. 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.
Bibliographie:USDOE
ISSN:0168-9002
1872-9576
DOI:10.1016/j.nima.2021.166299