Determining Research Priorities for Astronomy Using Machine Learning
Publication Date
2022-01-11Journal Title
Research Notes of the AAS
ISSN
2515-5172
Publisher
American Astronomical Society
Volume
6
Issue
1
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Thomas, B., Thronson, H., Buonomo, A., & Barbier, L. (2022). Determining Research Priorities for Astronomy Using Machine Learning. Research Notes of the AAS, 6 (1) https://doi.org/10.3847/2515-5172/ac4990
Abstract
<jats:title>Abstract</jats:title>
<jats:p>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. 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>
Keywords
370, Laboratory Astrophysics, Instrumentation, Software, and Data
Identifiers
rnaasac4990, ac4990, aas36602
External DOI: https://doi.org/10.3847/2515-5172/ac4990
This record's URL: https://www.repository.cam.ac.uk/handle/1810/333004
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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