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 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.

Grantor dc:publisher.institution

Cosmology and Gravity Group

Year dc:date.issued

2017

Author dc:creator

• Amar, Gilad

Advisor dc:contributor.advisor

• Bassett, Bruce

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/27090

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/27090