2023ApJS..269...34C


Query : 2023ApJS..269...34C

2023ApJS..269...34C - Astrophys. J., Suppl. Ser., 269, 34 (2023/December-0)

Toward Machine-learning-based Metastudies: Applications to Cosmological Parameters.

CROSSLAND T., STENETORP P., KAWATA D., RIEDEL S., KITCHING T.D., DESHPANDE A., KIMPSON T., LIEW-CAIN C.L., PEDERSEN C., PIRAS D. and SHARMA M.

Abstract (from CDS):

We develop a new model for automatic extraction of reported measurement values from the astrophysical literature, utilizing 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 data sets 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.

Abstract Copyright: © 2023. The Author(s). Published by the American Astronomical Society.

Journal keyword(s): Astronomy data analysis - Cosmological parameters - Astronomy databases

Simbad objects: 2

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Number of rows : 2
N Identifier Otype ICRS (J2000)
RA
ICRS (J2000)
DEC
Mag U Mag B Mag V Mag R Mag I Sp type #ref
1850 - 2026
#notes
1 M 31 AGN 00 42 44.330 +41 16 07.50 4.86 4.36 3.44     ~ 13622 1
2 SN 1998bu SN* 10 46 46.01 +11 50 07.5   12.11       SNIa 328 1

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