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Deep-Learning the Time Domain

Published online by Cambridge University Press:  29 August 2019

A. Mahabal ,
K. Sheth ,
F. Gieseke ,
A. Drake ,
G. Djorgovski  and
M. J. Graham
A. Mahabal
Affiliation:
Department of Astronomy, California Institute of Technology, Pasadena, CA, USA email: aam@astro.caltech.edu Center for Data Driven Discovery, California Institute of Technology, Pasadena, CA, USA
K. Sheth
Affiliation:
Indian Institute of Technology Gandhinagar, India,
F. Gieseke
Affiliation:
Department of Computer Science, University of Copenhagen, Denmark
A. Drake
Affiliation:
Center for Data Driven Discovery, California Institute of Technology, Pasadena, CA, USA
G. Djorgovski
Affiliation:
Department of Astronomy, California Institute of Technology, Pasadena, CA, USA email: aam@astro.caltech.edu Center for Data Driven Discovery, California Institute of Technology, Pasadena, CA, USA
M. J. Graham
Affiliation:
Department of Astronomy, California Institute of Technology, Pasadena, CA, USA email: aam@astro.caltech.edu Center for Data Driven Discovery, California Institute of Technology, Pasadena, CA, USA
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Abstract

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“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.

Keywords

Surveysmethods: data analysisTechniques: image processing
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 14 , Symposium S339: Southern Horizons in Time-Domain Astronomy , November 2017 , pp. 165 - 171
Copyright
© International Astronomical Union 2019 

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