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Deep learning for supernovae detection
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 astro...
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| Format: | Thesis |
| Language: | English |
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Cosmology and Gravity Group
2018
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| _version_ | 1867613182782078976 |
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| access_status_str | Open Access |
| author | Amar, Gilad |
| author2 | Bassett, Bruce |
| author_browse | Amar, Gilad Bassett, Bruce |
| author_facet | Bassett, Bruce Amar, Gilad |
| author_sort | Amar, Gilad |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/27090 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:05.102Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | Cosmology and Gravity Group |
| publisherStr | Cosmology and Gravity Group |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/27090 Deep learning for supernovae detection Amar, Gilad Bassett, Bruce Applied Mathematics Astronomy 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. 2018-01-30T10:23:06Z 2018-01-30T10:23:06Z 2017 Master Thesis Masters MSc http://hdl.handle.net/11427/27090 eng application/pdf Cosmology and Gravity Group Faculty of Science University of Cape Town |
| spellingShingle | Applied Mathematics Astronomy Amar, Gilad Deep learning for supernovae detection |
| thesis_degree_str | Master's |
| title | Deep learning for supernovae detection |
| title_full | Deep learning for supernovae detection |
| title_fullStr | Deep learning for supernovae detection |
| title_full_unstemmed | Deep learning for supernovae detection |
| title_short | Deep learning for supernovae detection |
| title_sort | deep learning for supernovae detection |
| topic | Applied Mathematics Astronomy |
| url | http://hdl.handle.net/11427/27090 |
| work_keys_str_mv | AT amargilad deeplearningforsupernovaedetection |