The application of Bayesian statistics and maximum entropy to Ion beam analysis techniques

Simple item page
dc.contributor.advisorProzesky, Victor Men_ZA
dc.contributor.advisorBritton, David Ten_ZA
dc.contributor.authorPadayachee, Jayanethieen_ZA
dc.date.accessioned2016-01-02T04:53:02Z
dc.date.available2016-01-02T04:53:02Z
dc.date.issued1997en_ZA
dc.descriptionBibliography: pages 128-129.en_ZA
dc.description.abstractThe elimination of some blurring property, such as the detector response function, from spectra has received a considerable amount of attention. The problem is usually complicated by the presence of noise in the data, and in general, there exists an infinite set of possible solutions which are consistent with the data within the bounds imposed by the noise. Such a problem is known, generally, as an ill-defined inverse problem. Many techniques have been developed in an attempt to solve inverse problems, for example the problem of deconvolution, but these techniques employ ad hoc modifications to solve different problems. Bayesian Statistics has been proved to be the only consistent method for solving inverse problems of the type where the information is expressed in terms of probability distributions. This dissertation presents results of applying the Bayesian formalism, together with the concepts of maximum information entropy and multiresolution pixons, to various inverse problems in ion beam analysis; The results of this method of deconvoluting Rutherford Backscattering Spectrometry (RBS) and Proton Induced X-ray Emission (PIXE) spectra are compared to the results from other deconvolution techniques, namely Fourier Transforms, Jansson's method and maximum entropy (MaxEnt) without pixons. All the deconvolution techniques show an improvement in the resolution of the RBS spectra but only the MaxEnt techniques show a significant improvement in the resolution of the PIXE spectra. The MaxEnt methods also produce physically acceptable results. The MaxEnt formalism was applied to the extraction of depth profiles from RBS and PIXE spectra and yielded good results. The technique was also used to deconvolute the beam profile from one-dimensional nuclear microprobe scans.en_ZA
dc.identifier.apacitationPadayachee, J. (1997). <i>The application of Bayesian statistics and maximum entropy to Ion beam analysis techniques</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Physics. Retrieved from http://hdl.handle.net/11427/16143en_ZA
dc.identifier.chicagocitationPadayachee, Jayanethie. <i>"The application of Bayesian statistics and maximum entropy to Ion beam analysis techniques."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Physics, 1997. http://hdl.handle.net/11427/16143en_ZA
dc.identifier.citationPadayachee, J. 1997. The application of Bayesian statistics and maximum entropy to Ion beam analysis techniques. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Padayachee, Jayanethie AB - The elimination of some blurring property, such as the detector response function, from spectra has received a considerable amount of attention. The problem is usually complicated by the presence of noise in the data, and in general, there exists an infinite set of possible solutions which are consistent with the data within the bounds imposed by the noise. Such a problem is known, generally, as an ill-defined inverse problem. Many techniques have been developed in an attempt to solve inverse problems, for example the problem of deconvolution, but these techniques employ ad hoc modifications to solve different problems. Bayesian Statistics has been proved to be the only consistent method for solving inverse problems of the type where the information is expressed in terms of probability distributions. This dissertation presents results of applying the Bayesian formalism, together with the concepts of maximum information entropy and multiresolution pixons, to various inverse problems in ion beam analysis; The results of this method of deconvoluting Rutherford Backscattering Spectrometry (RBS) and Proton Induced X-ray Emission (PIXE) spectra are compared to the results from other deconvolution techniques, namely Fourier Transforms, Jansson's method and maximum entropy (MaxEnt) without pixons. All the deconvolution techniques show an improvement in the resolution of the RBS spectra but only the MaxEnt techniques show a significant improvement in the resolution of the PIXE spectra. The MaxEnt methods also produce physically acceptable results. The MaxEnt formalism was applied to the extraction of depth profiles from RBS and PIXE spectra and yielded good results. The technique was also used to deconvolute the beam profile from one-dimensional nuclear microprobe scans. DA - 1997 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 1997 T1 - The application of Bayesian statistics and maximum entropy to Ion beam analysis techniques TI - The application of Bayesian statistics and maximum entropy to Ion beam analysis techniques UR - http://hdl.handle.net/11427/16143 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/16143
dc.identifier.vancouvercitationPadayachee J. The application of Bayesian statistics and maximum entropy to Ion beam analysis techniques. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Physics, 1997 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/16143en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Physicsen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherPhysicsen_ZA
dc.titleThe application of Bayesian statistics and maximum entropy to Ion beam analysis techniquesen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
thesis_sci_1997_padayachee_jayanethie.pdf
Size:
2.18 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Masters