Deep-learning the time domain

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dc.contributor.author Seth, Kshiteej et al.
dc.date.accessioned 2020-05-15T12:42:39Z
dc.date.available 2020-05-15T12:42:39Z
dc.date.issued 2017-11
dc.identifier.citation Seth, Kshiteej et al., "Deep-learning the time domain", Proceedings of the International Astronomical Union, DOI: 10.1017/S1743921318002491, vol. 14, no. S339, pp. 165-171, Nov. 2017. en_US
dc.identifier.issn 1743-9213
dc.identifier.issn 1743-9221
dc.identifier.uri http://dx.doi.org/10.1017/S1743921318002491
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/5394
dc.description.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.
dc.description.statementofresponsibility by Kshiteej Seth et al.
dc.format.extent vol. 14, no. S339, pp. 165-171
dc.language.iso en_US en_US
dc.publisher Cambridge University Press en_US
dc.title Deep-learning the time domain en_US
dc.type Article en_US
dc.relation.journal Proceedings of the International Astronomical Union


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