Cosmology-independent Photon Mass Limits from Localized Fast Radio Bursts by using Artificial Neural Networks
Jing-Yu Ran, Bao Wang, Jun-Jie Wei

TL;DR
This paper introduces a model-independent method using artificial neural networks to constrain the photon mass from fast radio bursts without relying on specific cosmological models, providing the first such extragalactic limit.
Contribution
The study applies a novel ANN-based smoothing technique to reconstruct the Hubble parameter, enabling a cosmology-independent photon mass constraint from FRB data.
Findings
Upper limit on photon mass: 3.5 x 10^{-51} kg at 1σ
First cosmology-independent photon mass limit from extragalactic sources
Demonstrates ANN effectiveness in cosmological parameter reconstruction
Abstract
A hypothetical photon mass, , can produce a frequency-dependent vacuum dispersion of light, which leads to an additional time delay between photons with different frequencies when they propagate through a fixed distance. The dispersion measure--redshift measurements of fast radio bursts (FRBs) have been widely used to constrain the rest mass of the photon. However, all current studies analyzed the effect of the frequency-dependent dispersion for massive photons in the standard CDM cosmological context. In order to alleviate the circularity problem induced by the presumption of a specific cosmological model based on the fundamental postulate of the masslessness of photons, here we employ a new model-independent smoothing technique, Artificial Neural Network (ANN), to reconstruct the Hubble parameter function from 34 cosmic-chronometer measurements. By…
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Taxonomy
TopicsStatistical and numerical algorithms · Cosmology and Gravitation Theories · Computational Physics and Python Applications
