Repository logo
  • English
  • العربية
  • বাংলা
  • Català
  • Čeština
  • Deutsch
  • Ελληνικά
  • Español
  • Suomi
  • Français
  • Gàidhlig
  • हिंदी
  • Magyar
  • Italiano
  • Қазақ
  • Latviešu
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Srpski (lat)
  • Српски
  • Svenska
  • Türkçe
  • Yкраї́нська
  • Tiếng Việt
Log In
New user? Click here to register.Have you forgotten your password?
  1. Home
  2. Scholalry Output
  3. Publications
  4. Deep-learning the time domain
 
  • Details

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.
DOI
10.1017/S1743921318002491
Volume
14
Issue
S339
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.
Unpaywall
URI
https://d8.irins.org/handle/IITG2025/23477
Subjects
methods: data analysis | Surveys | Techniques: image processing
IITGN Knowledge Repository Developed and Managed by Library

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Privacy policy
  • End User Agreement
  • Send Feedback
Repository logo COAR Notify