Determining Research Priorities for Astronomy Using Machine Learning


Change log
Authors
Thronson, Harley 
Buonomo, Anthony 
Barbier, Louis 
Abstract

jats:titleAbstract</jats:title> jats:pWe summarize the first exploratory investigation into whether Machine Learning techniques can augment science strategic planning. We find that an approach based on Latent Dirichlet Allocation using abstracts drawn from high-impact astronomy journals may provide a leading indicator of future interest in a research topic. We show two topic metrics that correlate well with the high-priority research areas identified by the 2010 National Academies’ Astronomy and Astrophysics Decadal Survey. One metric is based on a sum of the fractional contribution to each topic by all scientific papers (“counts”) while the other is the Compound Annual Growth Rate of counts. These same metrics also show the same degree of correlation with the whitepapers submitted to the same Decadal Survey. Our results suggest that the Decadal Survey may under-emphasize fast growing research. A preliminary version of our work was presented by Thronson et al.</jats:p>

Description
Keywords
46 Information and Computing Sciences, 4610 Library and Information Studies
Journal Title
Research Notes of the AAS
Conference Name
Journal ISSN
2515-5172
2515-5172
Volume Title
6
Publisher
American Astronomical Society