2023/09/21 by Jing-Yu Ran, Ran, Jing-Yu, Jun-Jie Wei +1 · 1 citation
Physics and Astronomy · #Cosmology and Gravitation Theories #Pulsars and Gravitational Waves Research #Gamma-ray bursts and supernovae
paper · pdf · doi:10.48550/arxiv.2309.11810
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\^ıtre-Robertson-Walker metric to obtain cosmology-independent constraints on both the parameterized post-Newtonian parameter γ\rm PPN and the spatial curvature Ωk, 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 γ\rm PPN=1.16-0.12+0.15 and Ωk=0.89-1.00+1.97, indicating a spatially flat universe with the conservation of GR (i.e., Ωk=0 and γ\rm PPN=1) is basically supported within 1σ confidence level. Assuming a zero spatial curvature, we find γ\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., γ\rm PPN=1), the curvature parameter constraint can be further improved to be Ωk=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.