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Applying machine learning methods for the analysis of two-dimensional mass spectra
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 relativ...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10368573/ https://www.ncbi.nlm.nih.gov/pubmed/37502124 http://dx.doi.org/10.1140/epja/s10050-023-01080-x |
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author | 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. |
author_facet | 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. |
author_sort | Gao, Z. |
collection | PubMed |
description | 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 [Formula: see text] Cs[Formula: see text] 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. |
format | Online Article Text |
id | pubmed-10368573 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-103685732023-07-27 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. Eur Phys J A Hadron Nucl Special Article - New Tools and Techniques 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 [Formula: see text] Cs[Formula: see text] 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. Springer Berlin Heidelberg 2023-07-25 2023 /pmc/articles/PMC10368573/ /pubmed/37502124 http://dx.doi.org/10.1140/epja/s10050-023-01080-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Special Article - New Tools and Techniques 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. Applying machine learning methods for the analysis of two-dimensional mass spectra |
title | Applying machine learning methods for the analysis of two-dimensional mass spectra |
title_full | Applying machine learning methods for the analysis of two-dimensional mass spectra |
title_fullStr | Applying machine learning methods for the analysis of two-dimensional mass spectra |
title_full_unstemmed | Applying machine learning methods for the analysis of two-dimensional mass spectra |
title_short | Applying machine learning methods for the analysis of two-dimensional mass spectra |
title_sort | applying machine learning methods for the analysis of two-dimensional mass spectra |
topic | Special Article - New Tools and Techniques |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10368573/ https://www.ncbi.nlm.nih.gov/pubmed/37502124 http://dx.doi.org/10.1140/epja/s10050-023-01080-x |
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