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

Crossland, Tom; Stenetorp, Pontus; Kawata, Daisuke; Riedel, Sebastian; Kitching, Thomas D; Deshpande, Anurag; Kimpson, Tom; ... Sharma, Monu; + view all (2023) Toward Machine-learning-based Metastudies: Applications to Cosmological Parameters. The Astrophysical Journal Supplement Series , 269 (2) , Article 34. 10.3847/1538-4365/acf76a. Green open access

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Abstract

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.

Type: Article
Title: Toward Machine-learning-based Metastudies: Applications to Cosmological Parameters
Open access status: An open access version is available from UCL Discovery
DOI: 10.3847/1538-4365/acf76a
Publisher version: https://doi.org/10.3847/1538-4365/acf76a
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Space and Climate Physics
URI: https://discovery.ucl.ac.uk/id/eprint/10182367
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