Fast Radio Bursts and Artificial Neural Networks: a cosmological-model-independent estimation of the Hubble Constant

Jéferson A. S. Fortunato, David J. Bacon, Wiliam S. Hipólito-Ricaldi, David Wands

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Abstract

Fast Radio Bursts (FRBs) have emerged as powerful cosmological probes in recent years offering valuable insights into cosmic expansion. These predominantly extragalactic transients encode information on the expansion of the Universe through their dispersion measure, reflecting interactions with the intervening medium along the line of sight. In this study, we introduce a novel method for reconstructing the late-time cosmic expansion rate and estimating the Hubble constant, solely derived from FRBs measurements coupled with their redshift information while employing Artificial Neural Networks. Our approach yields a Hubble constant estimate of H0 = 67.2 ± 7.1 km s-1 Mpc-1. With a dataset comprising 21 localised data points, we demonstrate a precision of ∼ 10%. However, our forecasts using simulated datasets indicate that in the future it could be possible to achieve precision comparable to the SH0ES collaboration or the Planck satellite. Our findings underscore the potential of FRBs as alternative, independent tools for probing cosmic dynamics.
Original languageEnglish
Article number018
Number of pages19
JournalJournal of Cosmology and Astroparticle Physics
Volume2025
Issue number1
DOIs
Publication statusPublished - 8 Jan 2025

Keywords

  • fast radio bursts
  • TC_highlight
  • Hubble constant
  • Artificial neural networks
  • UKRI
  • STFC
  • ST/W001225/1

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