# Towards Machine-assisted Meta-Studies: The Hubble Constant

## TL;DR

This paper introduces an automated method to extract Hubble constant measurements from astrophysics literature, enabling large-scale meta-analyses and highlighting current measurement discrepancies.

## Contribution

It develops a rules-based NLP approach and neural network classifier to identify and extract measurements and uncertainties from a vast corpus of papers.

## Key findings

- Successfully extracted 298 measurements with uncertainties
- Identified the importance of reporting units and uncertainties
- Recovered the 3.5σ tension in Hubble constant measurements

## Abstract

We present an approach for automatic extraction of measured values from the astrophysical literature, using the Hubble constant for our pilot study. Our rules-based model -- a classical technique in natural language processing -- has successfully extracted 298 measurements of the Hubble constant, with uncertainties, from the 208,541 available arXiv astrophysics papers. We have also created an artificial neural network classifier to identify papers in arXiv which report novel measurements. From the analysis of our results we find that reporting measurements with uncertainties and the correct units is critical information when distinguishing novel measurements in free text. Our results correctly highlight the current tension for measurements of the Hubble constant and recover the $3.5\sigma$ discrepancy -- demonstrating that the tool presented in this paper is useful for meta-studies of astrophysical measurements from a large number of publications.