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dc.contributor.authorGao, Z.
dc.contributor.authorSolders, A.
dc.contributor.authorAl-Adili, A.
dc.contributor.authorBeliuskina, O.
dc.contributor.authorEronen, T.
dc.contributor.authorKankainen, A.
dc.contributor.authorLantz, M.
dc.contributor.authorMoore, I. D.
dc.contributor.authorNesterenko, D. A.
dc.contributor.authorPenttilä, H.
dc.contributor.authorPomp, S.
dc.contributor.authorSjöstrand, H.
dc.contributor.authorthe IGISOL team
dc.date.accessioned2023-09-06T09:24:30Z
dc.date.available2023-09-06T09:24:30Z
dc.date.issued2023
dc.identifier.citationGao, 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. <i>European Physical Journal A</i>, <i>59</i>, Article 169. <a href="https://doi.org/10.1140/epja/s10050-023-01080-x" target="_blank">https://doi.org/10.1140/epja/s10050-023-01080-x</a>
dc.identifier.otherCONVID_184081412
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/88890
dc.description.abstractIn 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.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofseriesEuropean Physical Journal A
dc.rightsCC BY 4.0
dc.titleApplying machine learning methods for the analysis of two-dimensional mass spectra
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202309064924
dc.contributor.laitosFysiikan laitosfi
dc.contributor.laitosDepartment of Physicsen
dc.contributor.oppiaineKiihdytinlaboratoriofi
dc.contributor.oppiaineResurssiviisausyhteisöfi
dc.contributor.oppiaineHyvinvoinnin tutkimuksen yhteisöfi
dc.contributor.oppiaineAccelerator Laboratoryen
dc.contributor.oppiaineSchool of Resource Wisdomen
dc.contributor.oppiaineSchool of Wellbeingen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn1434-6001
dc.relation.volume59
dc.type.versionpublishedVersion
dc.rights.copyright© The Author(s) 2023
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber295207
dc.relation.grantnumber771036
dc.relation.grantnumber771036
dc.relation.grantnumber605203
dc.relation.grantnumber605203
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/771036/EU//MAIDEN
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/605203/EU//
dc.subject.ysotutkimuslaitteet
dc.subject.ysokoneoppiminen
dc.subject.ysomassa (fysiikka)
dc.subject.ysoydinfysiikka
dc.subject.ysofissio
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p2440
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p9383
jyx.subject.urihttp://www.yso.fi/onto/yso/p14759
jyx.subject.urihttp://www.yso.fi/onto/yso/p18705
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1140/epja/s10050-023-01080-x
dc.relation.funderResearch Council of Finlanden
dc.relation.funderEuropean Commissionen
dc.relation.funderEuropean Commissionen
dc.relation.funderSuomen Akatemiafi
dc.relation.funderEuroopan komissiofi
dc.relation.funderEuroopan komissiofi
jyx.fundingprogramAcademy Research Fellow, AoFen
jyx.fundingprogramERC Consolidator Granten
jyx.fundingprogramFP7 (EU's 7th Framework Programme)en
jyx.fundingprogramAkatemiatutkija, SAfi
jyx.fundingprogramERC Consolidator Grantfi
jyx.fundingprogramEU:n 7. puiteohjelma (FP7)fi
jyx.fundinginformationThis 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.
dc.type.okmA1


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