Extragalactic Test of General Relativity from Strong Gravitational Lensing by using Artificial Neural Networks
Jing-Yu Ran, Jun-Jie Wei

TL;DR
This paper tests general relativity on galactic scales using strong gravitational lensing data and a novel neural network approach to reconstruct distance-redshift relations, finding results consistent with GR within uncertainties.
Contribution
Introduces a new nonparametric neural network method to calibrate distances in strong lensing systems, enabling cosmology-independent tests of GR on kiloparsec scales.
Findings
Results support spatial flatness and GR validity within 1σ
Achieves 9.6% precision in PPN parameter estimation
Demonstrates effectiveness of ANN-based distance reconstruction
Abstract
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 and the spatial curvature , 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 and , indicating a spatially flat universe with the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCosmology and Gravitation Theories · Pulsars and Gravitational Waves Research · Gamma-ray bursts and supernovae
