Machine learning techniques to determine elemental concentrations from raw IBA spectra

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dc.contributor.authorCohen, DDen_AU
dc.contributor.authorCrawford, Jen_AU
dc.date.accessioned2025-12-12T01:44:57Zen_AU
dc.date.available2025-12-12T01:44:57Zen_AU
dc.date.issued2024-01en_AU
dc.date.statistics2025-03-06en_AU
dc.description.abstractFor many decades we have run MeV protons beams together with four IBA spectra simultaneously to obtain over 35 different elemental concentrations on any given target. These include PESA for hydrogen, RBS for carbon, nitrogen and oxygen, PIXE for aluminium to lead and PIGE for light elements like fluorine, sodium and aluminium. As part of a machine learning process we have taken five years of monthly raw spectra for each of the four IBA techniques and used the R code subroutine XgBoost and the corresponding calculated elemental concentrations from iBAT analysis code to train the system. This system training included 35 different elemental species. We then used the system to predict the elemental concentrations, from just the next six months of raw IBA spectra with no other inputs. The results were excellent for all elemental concentrations above their minimum detection limits. Crown Copyright © 2023 Published by Elsevier B.V.en_AU
dc.identifier.articlenumber165169en_AU
dc.identifier.citationCohen, D. D., & Crawford, J. (2024). Machine learning techniques to determine elemental concentrations from raw IBA spectra. Paper presented to the 19th International Conference on Electromagnetic Isotope Separators and Related Topics (EMIS) , Daejeon, South Korea, October 3-7, 2022. In Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 546, 165169. doi:10.1016/j.nimb.2023.165169en_AU
dc.identifier.conferenceenddate2022-10-03en_AU
dc.identifier.conferencename19th International Conference on Electromagnetic Isotope Separators and Related Topics (EMIS)en_AU
dc.identifier.conferenceplaceDaejeon, South Koreaen_AU
dc.identifier.conferencestartdate2022-10-03en_AU
dc.identifier.issn0168-583Xen_AU
dc.identifier.journaltitleNuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atomsen_AU
dc.identifier.urihttps://doi.org/10.1016/j.nimb.2023.165169en_AU
dc.identifier.urihttps://apo.ansto.gov.au/handle/10238/16779en_AU
dc.identifier.volume546en_AU
dc.languageEnglishen_AU
dc.language.isoenen_AU
dc.publisherElsevieren_AU
dc.subjectMachine Learningen_AU
dc.subjectProton beamsen_AU
dc.subjectProgramming languagesen_AU
dc.subjectIon beamsen_AU
dc.subjectElementary particlesen_AU
dc.subjectDetectionen_AU
dc.subjectMeV Rangeen_AU
dc.subjectMachine Learningen_AU
dc.subjectArtificial intelligenceen_AU
dc.titleMachine learning techniques to determine elemental concentrations from raw IBA spectraen_AU
dc.typeConference Paperen_AU
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