Data mining the EXFOR database using network theory

TL;DR

This paper applies network theory to the EXFOR nuclear reaction database, creating a graph of observables to analyze their usage and identify key reference measurements for nuclear data evaluation.

Contribution

It introduces a novel network-based approach to analyze the EXFOR database, revealing the structure and importance of various observables in nuclear science.

Findings

Identified key observables used as reference measurements

Highlighted observables that need more evaluation attention

Discovered classes of observables disconnected from references

Abstract

The EXFOR database contains the largest collection of experimental nuclear reaction data available as well as the data's bibliographic information and experimental details. We created an undirected graph from the EXFOR datasets with graph nodes representing single observables and graph links representing the various types of connections between these observables. This graph is an abstract representation of the connections in EXFOR, similar to graphs of social networks, authorship networks, etc. By analyzing this abstract graph, we are able to address very specific questions such as 1) what observables are being used as reference measurements by the experimental nuclear science community? 2) are these observables given the attention needed by various nuclear data evaluation projects? 3) are there classes of observables that are not connected to these reference measurements? In addressing these questions, we propose several (mostly cross section) observables that should be evaluated and made into reaction reference standards.