Research Exchange: Abstracting Traditional Roles in Scientific Discovery and Inference: Application to Real-Time Astronomy

Josh Bloom [Assistant Professor of Astronomy, UC Berkeley]

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As always, these talks are free, open to the public and broadcast live on-line at mms:// the day and time of the event. Questions can be sent via Yahoo IM to username: citrisevents. Sponsored by Infineon Technologies. The schedule for the spring semester is at

The collection of new data in any discipline does not, in general, lead to the creation of new knowledge. As a stream of data transforms to a deluge, the human role in scientific discovery, traditionally so important, must be partially fulfilled by powerful algorithms. However, current tools and technology start to break down when discovery and understanding, by the very nature of the science at hand, must happen quickly and in near real-time. New astronomical surveys coming online in the next few years, many observing the same regions of the sky repeatedly in time, will collect more data in the next decade than in all of human history so far. Opening up truly new vistas on the dynamic universe requires both rapid data processing and quick decisions about what available resources (e.g., telescopes) worldwide must be marshalled to study newly discovered phenomena. This necessitates an intelligent “real-time” machine-based decision or “classification” framework that should be able to deal with incomplete (and in some cases spurious) information.
I describe our emerging framework for extracting novel science from large amounts of data in an environment where the computational needs vastly outweigh the available facilities, and intelligent (as well as dynamic) resource allocation is required.  I will also comment on the role of “Citizen Scientists” (crowd-sourced non-experts) in this new paradigm.