Hostname: page-component-745bb68f8f-grxwn Total loading time: 0 Render date: 2025-01-23T21:27:44.633Z Has data issue: false hasContentIssue false
  • English
  • Français

Challenges and Opportunities for Machine Learning in Time-Domain Astronomy

Invited talk

Published online by Cambridge University Press:  29 August 2019

B. Bassett
B. Bassett*
Affiliation:
AIMS, SAAO, and University of Cape Town, South Africa email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The recent spectacular progress in machine learning and artificial intelligence opens up exciting opportunities for time-domain and transient astronomy. This talk outlined the fundamental challenges that we will need to overcome in order to realise that potential, from discovering completely new classes to fully-automated follow-up campaigns.

Keywords

SurveysMethods: statistical
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 14 , Symposium S339: Southern Horizons in Time-Domain Astronomy , November 2017 , pp. 202
Copyright
© International Astronomical Union 2019