Dokumentdetails
ID
oai:arXiv.org:2404.17154
Thema
Astrophysics - Cosmology and Nonga... Astrophysics - High Energy Astroph... General Relativity and Quantum Cos... High Energy Physics - Phenomenolog...Autor
Ran, Jing-Yu Wang, Bao Wei, Jun-JieKategorie
Wissenschaften: Astrophysik
Jahr
2024
Auflistungsdatum
01.05.2024
Schlüsselwörter
fast astrophysics photons dispersion mass \times photonZusammenfassung
A hypothetical photon mass, $m_{\gamma}$, 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 $\Lambda$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 $H(z)$ function from 34 cosmic-chronometer measurements.
By combining observations of 32 well-localized FRBs and the $H(z)$ function reconstructed by ANN, we obtain an upper limit of $m_{\gamma} \le 3.5 \times 10^{-51}\;\rm{kg}$, or equivalently $m_{\gamma} \le 2.0 \times 10^{-15}\;\rm{eV/c^2}$ ($m_{\gamma} \le 6.5 \times 10^{-51}\;\rm{kg}$, or equivalently $m_{\gamma} \le 3.6 \times 10^{-15}\;\rm{eV/c^2}$) at the $1\sigma$ ($2\sigma$) confidence level.
This is the first cosmology-independent photon mass limit derived from extragalactic sources.
;Comment: 8 pages, 3 figures, 3 tables.
Accepted for publication in Chinese Physics Letters.
Invited article to special issue "FAST"
Ran, Jing-Yu,Wang, Bao,Wei, Jun-Jie, 2024, Cosmology-independent Photon Mass Limits from Localized Fast Radio Bursts by using Artificial Neural Networks
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