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Novel methods of supernova classification and type probability estimation
Future photometric surveys will provide vastly more supernovae than have presently been observed, the majority of which will not be spectroscopically typed. Key to extracting information from these future datasets will be the efficient use of light-curves. In the first part of this thesis we introdu...
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| Format: | Thesis |
| Language: | English |
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Department of Mathematics and Applied Mathematics
2015
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| _version_ | 1867614301643079680 |
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| access_status_str | Open Access |
| author | Newling, James |
| author_browse | Newling, James |
| author_facet | Newling, James |
| author_sort | Newling, James |
| collection | Thesis |
| description | Future photometric surveys will provide vastly more supernovae than have presently been observed, the majority of which will not be spectroscopically typed. Key to extracting information from these future datasets will be the efficient use of light-curves. In the first part of this thesis we introduce two methods for distinguishing type Ia supernovae from their contaminating counterparts, kernel density estimation and boosting. In the second half of this thesis we shift focus from classification to the related problem of type probability estimation, and ask how best to use type probabilities. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/11174 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:49:52.430Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | Department of Mathematics and Applied Mathematics |
| publisherStr | Department of Mathematics and Applied Mathematics |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/11174 Novel methods of supernova classification and type probability estimation Newling, James Maths and Applied Mathematics Future photometric surveys will provide vastly more supernovae than have presently been observed, the majority of which will not be spectroscopically typed. Key to extracting information from these future datasets will be the efficient use of light-curves. In the first part of this thesis we introduce two methods for distinguishing type Ia supernovae from their contaminating counterparts, kernel density estimation and boosting. In the second half of this thesis we shift focus from classification to the related problem of type probability estimation, and ask how best to use type probabilities. 2015-01-03T18:14:21Z 2015-01-03T18:14:21Z 2011 Master Thesis Masters MSc http://hdl.handle.net/11427/11174 eng application/pdf Department of Mathematics and Applied Mathematics Faculty of Science University of Cape Town |
| spellingShingle | Maths and Applied Mathematics Newling, James Novel methods of supernova classification and type probability estimation |
| thesis_degree_str | Master's |
| title | Novel methods of supernova classification and type probability estimation |
| title_full | Novel methods of supernova classification and type probability estimation |
| title_fullStr | Novel methods of supernova classification and type probability estimation |
| title_full_unstemmed | Novel methods of supernova classification and type probability estimation |
| title_short | Novel methods of supernova classification and type probability estimation |
| title_sort | novel methods of supernova classification and type probability estimation |
| topic | Maths and Applied Mathematics |
| url | http://hdl.handle.net/11427/11174 |
| work_keys_str_mv | AT newlingjames novelmethodsofsupernovaclassificationandtypeprobabilityestimation |