Extragalactic Test of General Relativity from Strong Gravitational Lensing by using Artificial Neural Networks
This study aims to test the validity of general relativity (GR) on kiloparsec scales by employing a newly compiled galaxy-scale strong gravitational lensing (SGL) sample. We utilize the distance sum rule within the Friedmann-Lema\^{\i}tre-Robertson-Walker metric to obtain cosmology-independent const...
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Main Authors | , |
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Format | Journal Article |
Language | English |
Published |
21.09.2023
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Subjects | |
Online Access | Get full text |
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Summary: | This study aims to test the validity of general relativity (GR) on kiloparsec
scales by employing a newly compiled galaxy-scale strong gravitational lensing
(SGL) sample. We utilize the distance sum rule within the
Friedmann-Lema\^{\i}tre-Robertson-Walker metric to obtain cosmology-independent
constraints on both the parameterized post-Newtonian parameter $\gamma_{\rm
PPN}$ and the spatial curvature $\Omega_{k}$, which overcomes the circularity
problem induced by the presumption of a cosmological model grounded in GR. To
calibrate the distances in the SGL systems, we introduce a novel nonparametric
approach, Artificial Neural Network (ANN), to reconstruct a smooth
distance--redshift relation from the Pantheon+ sample of type Ia supernovae.
Our results show that $\gamma_{\rm PPN}=1.16_{-0.12}^{+0.15}$ and
$\Omega_k=0.89_{-1.00}^{+1.97}$, indicating a spatially flat universe with the
conservation of GR (i.e., $\Omega_k=0$ and $\gamma_{\rm PPN}=1$) is basically
supported within $1\sigma$ confidence level. Assuming a zero spatial curvature,
we find $\gamma_{\rm PPN}=1.09_{-0.10}^{+0.11}$, representing an agreement with
the prediction of 1 from GR to a 9.6\% precision. If we instead assume GR holds
(i.e., $\gamma_{\rm PPN}=1$), the curvature parameter constraint can be further
improved to be $\Omega_k=0.11_{-0.47}^{+0.78}$. These resulting constraints
demonstrate the effectiveness of our method in testing GR on galactic scales by
combining observations of strong lensing and the distance--redshift relation
reconstructed by ANN. |
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DOI: | 10.48550/arxiv.2309.11810 |