The nucleardatapy toolkit for simple access to experimental nuclear data, astrophysical observations, and theoretical predictions
J\'er\^ome Margueron, Christian Drischler, Mariana Dutra, Stefano Gandolfi, Alexandros Gezerlis, Guilherme Grams, S\'ebastien Guillot, Rohit Kumar, Sudhanva Lalit, Odilon Louren\c{c}o, Rahul Somasundaram, Ingo Tews, Isaac Vida\~na

TL;DR
The nucleardatapy toolkit offers a unified, accessible platform for nuclear physics and astrophysical data, enabling transparent meta-analyses and comparisons of theoretical predictions, experimental results, and observations.
Contribution
This paper introduces nucleardatapy, a Python toolkit that simplifies access to nuclear physics and astrophysical data, facilitating meta-analyses and community contributions.
Findings
Compatibility of pressure predictions with gravitational-wave data
Unified data format for diverse nuclear and astrophysical data
Toolkit supports ongoing and future meta-analyses
Abstract
Systematic comparisons across theoretical predictions for the properties of dense matter, nuclear physics data, and astrophysical observations (also called meta-analyses) are performed. Existing predictions for symmetric nuclear and neutron matter properties are considered, and they are shown in this paper as an illustration of the present knowledge. Asymmetric matter is constructed assuming the isospin asymmetry quadratic approximation. It is employed to predict the pressure at twice saturation energy-density based only on nuclear-physics constraints, and we find it compatible with the one from the gravitational-wave community. To make our meta-analysis transparent, updated in the future, and to publicly share our results, the Python toolkit \texttt{nucleardatapy} is described and released here. Hence, this paper accompanies \texttt{nucleardatapy}, which simplifies access to…
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