Revealing Connections in QCD with Machine Learning
P.L.S. Connor* and
A. Sulc*: corresponding author
Pre-published on:
December 17, 2024
Published on:
April 29, 2025
Abstract
This work utilises text analysis techniques to uncover connections and trends in quantum chromodynamics (QCD) research over time. Through embedding-based analysis, we are able to draw conceptual connections between disparate works across QCD subfields. Examining topic clustering and trajectories over time provides insights into new phenomena gaining momentum and experimental approaches coming to prominence in the QCD research area. Furthermore, we construct citation graphs between influential papers to reveal impactful contributions and relationships, compare them with respect to their topic, and propose intertopical and citation-related recommendations.
DOI: https://doi.org/10.22323/1.476.1054
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