Applying machine learning methods for the analysis of two-dimensional mass spectra
Gao, Z., Solders, A., Al-Adili, A., Beliuskina, O., Eronen, T., Kankainen, A., Lantz, M., Moore, I. D., Nesterenko, D. A., Penttilä, H., Pomp, S., Sjöstrand, H., the IGISOL team. (2023). Applying machine learning methods for the analysis of two-dimensional mass spectra. European Physical Journal A, 59, Article 169. https://doi.org/10.1140/epja/s10050-023-01080-x
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European Physical Journal ADate
2023Discipline
KiihdytinlaboratorioResurssiviisausyhteisöHyvinvoinnin tutkimuksen yhteisöAccelerator LaboratorySchool of Resource WisdomSchool of WellbeingCopyright
© The Author(s) 2023
In a measurement of isomeric yield-ratios in fission, the Phase-Imaging Ion-Cyclotron-Resonance technique, which projects the radial motions of ions in the Penning trap (JYFLTRAP) onto a position-sensitive micro-channel plate detector, has been applied. To obtain the yield ratio, that is the relative population of two states of an isomer pair, a novel analysis procedure has been developed to determine the number of detected ions in each state, as well as corrections for the detector efficiency and decay losses. In order to determine the population of the states in cases where their mass difference is too small to reach full separation, a Bayesian Gaussian Mixture model was implemented. The position-dependent efficiency of the micro-channel plate detector was calibrated by mapping it with 133133Cs++ ions, and a Gaussian Process was trained with the position data to construct an efficiency function that could be used to correct the recorded distributions. The obtained numbers of counts of excited and ground-state ions were used to derive the isomeric yield ratio, taking into account decay losses as well as feeding from precursors.
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https://converis.jyu.fi/converis/portal/detail/Publication/184081412
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Related funder(s)
Research Council of Finland; European CommissionFunding program(s)
Academy Research Fellow, AoF; ERC Consolidator Grant; FP7 (EU's 7th Framework Programme)
The content of the publication reflects only the author’s view. The funder is not responsible for any use that may be made of the information it contains.
Additional information about funding
This work was supported by the Swedish research council Vetenskapsrådet (Ref. No. 2017-06481), the European Commission within the Seventh Framework Programme through Fission-2013-CHANDA (Project No. 605203), the Swedish Radiation Safety Authority (SSM). A. Kankainen and D. A. Nesterenko acknowledge the funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 771036 (ERC CoG MAIDEN). T. Eronen acknowledges the funding from Academy of Finland (Project No. 295207). We thank Georg Schnabel and Joachim Hansson for fruitful discussions on applying machine learning methods. Open access funding provided by Uppsala University. ...License
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