Determining Research Priorities for Astronomy Using Machine Learning. (arXiv:2203.00713v1 [astro-ph.IM])

We 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 science frontier panels. 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 these 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.



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