Massively Parallel Ground-Based Follow-Up by Amateur and Educational Communities Beyond the Next Decade
David Hogg (New York University)
Computer vision and geometric hashing techniques from computer science have made possible automated data analysis, archiving, search, and retrieval among highly heterogeneous and badly archived data sets. We have shown that we can solve the totally "blind astrometry" problem (determine precise image pointing and field of view using *nothing* but the information in the image pixels) for a wide range of astronomical images, quickly and robustly. This blind system brings all of the data in digitized plate archives, amateur basements, online photo-sharing sites, and educational-observatory computers into the professional domain for research and discovery. In the coming decade, it will also generate enormous amounts of knowledge about amateur and educational-observatory capabilities (both the hardware and the personnel), reward the competent with involvement in research activity, and create two-way communications among the research, educational, and amateur communities. One long-term goal is to organize these communities into an "always-up" rapid response system, with heterogeneously taken but uniformly processed, calibrated, and archived data. Such a system would have a large impact on the context of space missions beyond the next decade.