Authors
Weber, C.; Ray, D.; Valverde, A.; Clark, J.; Sharma, K.
Abstract
The development of the phase-imaging ion-cyclotron resonance (PI-ICR) techniquefor use in Penning trap mass spectrometry (PTMS) increased the speed and precisionwith which PTMS experiments can be carried out. In PI-ICR, data sets of the locationsof individual ion hits on a detector are created showing how ions cluster together intospots 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 determiningthe locations of spot centers non-trivial. A method for assigning groups of ions to theirrespective spots and determining the spot centers is therefore essential for further improving precision and confidence in PI-ICR experiments. We present the class of Gaussianmixture model (GMM) clustering algorithms as an optimal solution. We show that onsimulated PI-ICR data, several types of GMM clustering algorithms perform better thanother 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 theCanadian Penning trap mass spectrometer were checked using GMMs, producing resultsthat were in close agreement with the previously published values.