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Machine learning, digital imaging & big data are revolutionizing astronomy

Telescope projects now routinely obtain massive digital movies of the dynamic night’s sky. But given the growing data volumes, coupled with the need to respond to transient events quickly with appropriate followup resources, it is no longer possible for people to be involved in the real-time loop.

At the SIAM Conference on Computational Science and Engineering held in Boston in February 2013, Dr. Joshua Bloom discussed the development of robotic telescopes, autonomous follow-up networks, and a machine-learning framework that act as a scalable, deterministic human surrogate for discovery and classification in astronomical imaging.

View a brief video overview of Dr. Bloom’s talk and an interview:

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