SALT spectroscopy and classification of supernova spectra using Bayesian techniques

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dc.contributor.advisorBassett, Bruce Aen_ZA
dc.contributor.advisorCrawford Steven, Men_ZA
dc.contributor.authorKasai, Eli Kunwijien_ZA
dc.date.accessioned2018-02-05T12:55:06Z
dc.date.available2018-02-05T12:55:06Z
dc.date.issued2017en_ZA
dc.description.abstractIn this thesis, we present the Southern African Large Telescope spectroscopic follow-up programme for supernova candidates discovered by the international Dark Energy Survey, the goals of which are to measure the expansion history of the Universe and shed light on the mysterious nature of dark energy. In total, we took spectra for 36 supernova candidates. These were classified using a new Bayesian Supernova spectra classifier, SuperNovaMC, that we developed to address limitations with existing algorithms. SuperNovaMC simultaneously finds the best fitting supernova and host galaxy using Bayesian model selection, fitting the entire spectrum with Monte Carlo Markov Chain methods which allow estimation of the entire parameter posterior distributions, and hence principled statistical analysis even at low signal-to-noise. After extensive testing of SuperNovaMC against simulations and literature data, we use it to classify 20 of our Dark Energy Survey candidates as Type Ia supernovae. We further performed equivalent width measurements of two Type Ia supernova spectral features: Ca II H&K and Si II 4000, using a sub-sample of the 20 Type Ia supernovae. We compared our results to those of a similar study conducted on a low-redshift (z < 0:1) Type Ia supernova sample and found the two sets of results to be consistent, suggesting no redshift evolution in the equivalent widths of the two spectral features in the redshift range 0:1 < z < 0:3 that we conducted the study in.en_ZA
dc.identifier.apacitationKasai, E. K. (2017). <i>SALT spectroscopy and classification of supernova spectra using Bayesian techniques</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Mathematics and Applied Mathematics. Retrieved from http://hdl.handle.net/11427/27283en_ZA
dc.identifier.chicagocitationKasai, Eli Kunwiji. <i>"SALT spectroscopy and classification of supernova spectra using Bayesian techniques."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Mathematics and Applied Mathematics, 2017. http://hdl.handle.net/11427/27283en_ZA
dc.identifier.citationKasai, E. 2017. SALT spectroscopy and classification of supernova spectra using Bayesian techniques. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Kasai, Eli Kunwiji AB - In this thesis, we present the Southern African Large Telescope spectroscopic follow-up programme for supernova candidates discovered by the international Dark Energy Survey, the goals of which are to measure the expansion history of the Universe and shed light on the mysterious nature of dark energy. In total, we took spectra for 36 supernova candidates. These were classified using a new Bayesian Supernova spectra classifier, SuperNovaMC, that we developed to address limitations with existing algorithms. SuperNovaMC simultaneously finds the best fitting supernova and host galaxy using Bayesian model selection, fitting the entire spectrum with Monte Carlo Markov Chain methods which allow estimation of the entire parameter posterior distributions, and hence principled statistical analysis even at low signal-to-noise. After extensive testing of SuperNovaMC against simulations and literature data, we use it to classify 20 of our Dark Energy Survey candidates as Type Ia supernovae. We further performed equivalent width measurements of two Type Ia supernova spectral features: Ca II H&K and Si II 4000, using a sub-sample of the 20 Type Ia supernovae. We compared our results to those of a similar study conducted on a low-redshift (z < 0:1) Type Ia supernova sample and found the two sets of results to be consistent, suggesting no redshift evolution in the equivalent widths of the two spectral features in the redshift range 0:1 < z < 0:3 that we conducted the study in. DA - 2017 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2017 T1 - SALT spectroscopy and classification of supernova spectra using Bayesian techniques TI - SALT spectroscopy and classification of supernova spectra using Bayesian techniques UR - http://hdl.handle.net/11427/27283 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/27283
dc.identifier.vancouvercitationKasai EK. SALT spectroscopy and classification of supernova spectra using Bayesian techniques. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Mathematics and Applied Mathematics, 2017 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/27283en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Mathematics and Applied Mathematicsen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherMathematicsen_ZA
dc.titleSALT spectroscopy and classification of supernova spectra using Bayesian techniquesen_ZA
dc.typeDoctoral Thesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnamePhDen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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