VirtualSpace: A vision of a machine-learned virtual space environment
Abstract
Space borne instrumentation tends to come and go. A typical instrument will go through a phase of design and construction, be deployed on a spacecraft for several years while it collects data, and then be decommissioned and fade into obscurity. The data collected from that instrument will typically receive much attention while it is being collected, perhaps in the form of event studies, conjunctions with other instruments, or a few statistical surveys, but once the instrument or spacecraft is decommissioned, the data will be archived and receive progressively less attention with every passing year. This is the fate of all historical data, and will be the fate of data being collected by instruments even at the present time. But what if those instruments could come alive, and all be simultaneously present at any and every point in time and space? Imagine the scientific insights, and societal gains that could be achieved with a grand (virtual) heliophysical observatory that consists of every current and historical mission ever deployed? We propose that this is not just fantasy but is imminently doable with the data currently available, with the present computational resources, and with currently available algorithms. This project revitalizes existing data resources and lays the groundwork for incorporating data from every future mission to expand the scope and refine the resolution of the virtual observatory. We call this project VirtualSpace: a machine-learned virtual space environment.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2017
- Bibcode:
- 2017AGUFMSM13G..07B
- Keywords:
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- 9820 Techniques applicable in three or more fields;
- GENERAL OR MISCELLANEOUS;
- 2499 General or miscellaneous;
- IONOSPHERE;
- 6699 General or miscellaneous;
- PUBLIC ISSUES;
- 7899 General or miscellaneous;
- SPACE PLASMA PHYSICS