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Technical and Observational Challenges for Future Time-Domain Surveys

Published online by Cambridge University Press:  20 April 2012

Joshua S. Bloom
Joshua S. Bloom*
Affiliation:
University of California, Berkeley, CA 94720, USA email: jbloom@astro.berkeley.edu
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Abstract

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By the end of the last decade, robotic telescopes were established as effective alternatives to the traditional role of astronomer in planning, conducting and reducing time-domain observations. By the end of this decade, machines will play a much more central role in the discovery and classification of time-domain events observed by such robots. While this abstraction of humans away from the real-time loop (and the nightly slog of the nominal scientific process) is inevitable, just how we will get there as a community is uncertain. I discuss the importance of machine learning in astronomy today, and project where we might consider heading in the future. I will also touch on the role of people and organisations in shaping and maximising the scientific returns of the coming data deluge.

Keywords

methods: data analysis—methods: statistical—techniques: photometric—surveys—stars: variables—sociology of astronomy—history and philosophy of astronomy
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 7 , Symposium S285: New Horizons in Time-Domain Astronomy , September 2011 , pp. 165 - 170
Copyright
Copyright © International Astronomical Union 2012

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