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arXiv · 2107.00665

Towards Machine Learning-Based Meta-Studies: Applications to Cosmological Parameters

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

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.

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Tom Crossland, Pontus Stenetorp, Daisuke Kawata, Sebastian Riedel, Thomas D. Kitching, Anurag Deshpande, Tom Kimpson, Choong Ling Liew-Cain, Christian Pedersen, Davide Piras, Monu Sharma. 2021-07-01. Towards Machine Learning-Based Meta-Studies: Applications to Cosmological Parameters. https://arxiv.org/abs/2107.00665

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