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