2021/07/01 by Tom Crossland, Pontus Stenetorp, Crossland, Tom +20
Computer Science · Decision Sciences · Engineering · Physics and Astronomy · #Artificial intelligence #Big Data Technologies and Applications #Computational Physics and Python Applications #Computer science #Engineering #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Machine learning #Meta learning (computer science) #Systems engineering #Time Series Analysis and Forecasting #astro-ph.IM
paper · pdf · doi:10.48550/arxiv.2107.00665
published in arXiv (Cornell University) (Cornell University) · 23 pages, 14 figures. Submitted to Monthly Notices of the Royal Astronomical Society. Astronomical measurement database available at http://numericalatlas.cs.ucl.ac.uk/
arxiv created 2021/07/01 · openalex publication_date 2021/07/01 · arxiv updated 2021/07/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We develop a new model for automatic extraction of reported measurement values from the astrophysical literature, utilising modern Natural Language Processing techniques. We use this model to extract measurements present in the abstracts of the approximately 248,000 astrophysics articles from the arXiv repository, yielding a database containing over 231,000 astrophysical numerical measurements. Furthermore, we present an online interface (Numerical Atlas) to allow users to query and explore this database, based on parameter names and symbolic representations, and download the resulting datasets for their own research uses. To illustrate potential use cases we then collect values for nine different cosmological parameters using this tool. From these results we can clearly observe the historical trends in the reported values of these quantities over the past two decades, and see the impacts of landmark publications on our understanding of cosmology.