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

IF 8.6 1区 物理与天体物理 Q1 ASTRONOMY & ASTROPHYSICS
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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引用次数: 0

Abstract

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.
基于机器学习的亚稳态研究:宇宙学参数的应用
摘要利用现代自然语言处理技术,建立了一种新的天体物理文献测量值自动提取模型。我们使用该模型从arXiv存储库中提取大约248,000篇天体物理学文章摘要中的测量值,从而产生一个包含超过231,000个天体物理学数值测量值的数据库。此外,我们提供了一个在线界面(Numerical Atlas),允许用户根据参数名称和符号表示查询和探索这个数据库,并下载结果数据集供他们自己的研究使用。为了说明潜在的用例,我们然后使用这个工具收集9个不同的宇宙学参数的值。从这些结果中,我们可以清楚地观察到过去二十年中这些量的报告值的历史趋势,并看到具有里程碑意义的出版物对我们对宇宙学的理解的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Astrophysical Journal Supplement Series
Astrophysical Journal Supplement Series 地学天文-天文与天体物理
CiteScore
14.50
自引率
5.70%
发文量
264
审稿时长
2 months
期刊介绍: The Astrophysical Journal Supplement (ApJS) serves as an open-access journal that publishes significant articles featuring extensive data or calculations in the field of astrophysics. It also facilitates Special Issues, presenting thematically related papers simultaneously in a single volume.
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