Details

Autor(en) / Beteiligte

• Crossland, Tom
• Stenetorp, Pontus
• Kawata, Daisuke
• Riedel, Sebastian
• Kitching, Thomas D.
• Deshpande, Anurag
• Kimpson, Tom
• Liew-Cain, Choong Ling
• Pedersen, Christian
• Piras, Davide
• Sharma, Monu

Titel

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

Ist Teil von

• The Astrophysical journal. Supplement series, 2023-12, Vol.269 (2), p.34

Ort / Verlag

Saskatoon: IOP Publishing

Erscheinungsjahr

2023

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.