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 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.

Grantor dc:publisher.institution

Department of Mathematics and Applied Mathematics

Year dc:date.issued

2011

Author dc:creator

• Newling, James

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/11174

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/11174