Deep learning for supernovae detection

 

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dc.contributor.advisor Bassett, Bruce en_ZA
dc.contributor.author Amar, Gilad en_ZA
dc.date.accessioned 2018-01-30T10:23:06Z
dc.date.available 2018-01-30T10:23:06Z
dc.date.issued 2017 en_ZA
dc.identifier.citation Amar, G. 2017. Deep learning for supernovae detection. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/27090
dc.description.abstract In future astronomical sky surveys it will be humanly impossible to classify the tens of thousands of candidate transients detected per night. This thesis explores the potential of using state-of-the-art machine learning algorithms to handle this burden more accurately and quickly than trained astronomers. To this end Deep Learning methods are applied to classify transients using real-world data from the Sloan Digital Sky Survey. Using cutting-edge training techniques several Convolutional Neural networks are trained and hyper-parameters tuned to outperform previous approaches and find that human labelling errors are the primary obstacle to further improvement. The tuning and optimisation of the deep models took in excess of 700 hours on a 4-Titan X GPU cluster. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Applied Mathematics en_ZA
dc.subject.other Astronomy en_ZA
dc.title Deep learning for supernovae detection en_ZA
dc.type Master Thesis
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Science en_ZA
dc.publisher.department Cosmology and Gravity Group en_ZA
dc.type.qualificationlevel Masters
dc.type.qualificationname MSc en_ZA
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Amar, G. (2017). <i>Deep learning for supernovae detection</i>. (Thesis). University of Cape Town ,Faculty of Science ,Cosmology and Gravity Group. Retrieved from http://hdl.handle.net/11427/27090 en_ZA
dc.identifier.chicagocitation Amar, Gilad. <i>"Deep learning for supernovae detection."</i> Thesis., University of Cape Town ,Faculty of Science ,Cosmology and Gravity Group, 2017. http://hdl.handle.net/11427/27090 en_ZA
dc.identifier.vancouvercitation Amar G. Deep learning for supernovae detection. [Thesis]. University of Cape Town ,Faculty of Science ,Cosmology and Gravity Group, 2017 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/27090 en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Amar, Gilad AB - In future astronomical sky surveys it will be humanly impossible to classify the tens of thousands of candidate transients detected per night. This thesis explores the potential of using state-of-the-art machine learning algorithms to handle this burden more accurately and quickly than trained astronomers. To this end Deep Learning methods are applied to classify transients using real-world data from the Sloan Digital Sky Survey. Using cutting-edge training techniques several Convolutional Neural networks are trained and hyper-parameters tuned to outperform previous approaches and find that human labelling errors are the primary obstacle to further improvement. The tuning and optimisation of the deep models took in excess of 700 hours on a 4-Titan X GPU cluster. DA - 2017 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2017 T1 - Deep learning for supernovae detection TI - Deep learning for supernovae detection UR - http://hdl.handle.net/11427/27090 ER - en_ZA


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