Galaxy-Scale Test of General Relativity with Strong Gravitational Lensing

TL;DR

This study tests general relativity at galactic scales using strong gravitational lensing data and supernovae, finding results consistent with GR and providing the most precise constraint to date.

Contribution

It introduces a model-independent distance calibration using Gaussian Processes and compares multiple lens models to improve constraints on GR at galactic scales.

Findings

The most reliable lens model yields gamma_{PPN}=1.065^{+0.064}_{-0.074}

Constraint accuracy is 6.4%, the best among recent SGL studies

Lens model choice significantly affects the gamma_{PPN} constraints

Abstract

Although general relativity (GR) has been precisely tested at the solar system scale, precise tests at a galactic or cosmological scale are still relatively insufficient. Here, in order to test GR at the galactic scale, we use the newly compiled galaxy-scale strong gravitational lensing (SGL) sample to constrain the parameter $\gamma_{PPN}$ in the parametrized post-Newtonian (PPN) formalism. We employ the Pantheon sample of type Ia supernovae observation to calibrate the distances in the SGL systems using the Gaussian Process method, which avoids the logical problem caused by assuming a cosmological model within GR to determine the distances in the SGL sample. Furthermore, we consider three typical lens models in this work to investigate the influences of the lens mass distributions on the fitting results. We find that the choice of the lens models has a significant impact on the constraints on the PPN parameter $\gamma_{PPN}$. We use the Bayesian information criterion as an evaluation tool to make a comparison for the fitting results of the three lens models, and we find that the most reliable lens model gives the result of $\gamma_{PPN}=1.065^{+0.064}_{-0.074}$, which is in good agreement with the prediction of $\gamma_{PPN}=1$ by GR. As far as we know, our 6.4% constraint result is the best result so far among the recent works using the SGL method.