Deep-learning the time domain
Source
Proceedings of the International Astronomical Union
ISSN
17439213
Date Issued
2017-01-01
Author(s)
Mahabal, A.
Sheth, K.
Gieseke, F.
Drake, A.
Djorgovski, G.
Graham, M. J.
Abstract
Deep learning is finding more and more applications everywhere, and astronomy is not an exception. This talk described the application of convolutional neural networks to time-domain astronomy, specifically to light-curves of sources. The work that is discussed is based on a published paper to which reference can be made for more detail. The talk finished with a note cautioning new practitioners about the pitfalls lurking in out-of-The-box use of deep-learning techniques.
Subjects
methods: data analysis | Surveys | Techniques: image processing
