Testing General Relativity on Galactic Scales via DESI-BAO and Strong Lensing: Circumventing Assumptions on the Hubble Constant, Sound Horizon, and Dark Energy

Abstract

We present a cosmological model-independent framework for testing general relativity (GR) on galactic scales by combining baryon acoustic oscillation (BAO) angular scale measurements with 120 galaxy-scale strong gravitational lensing systems. Using artificial neural networks (ANNs) and cubic spline reconstruction, we reconstruct the BAO angular scale from SDSS, BOSS, eBOSS, and DESI Data Release 2 (DR2), and infer the angular diameter distances to lenses and sources. Crucially, All the quantities used in the GR test are derived from observations and are independent of cosmological parameters such as the Hubble constant, the sound horizon, or the dark energy equation of state, minimizing potential biases from model-dependent distance priors. These distances are then incorporated into the strong lensing likelihood to constrain the parameterized post-Newtonian (PPN) parameter $γ_{\rm PPN}$ under two lens mass models: a constant-density-slope model ($P_1$) and a redshift-evolving model ($P_2$). For the $P_1$ model, the ANN reconstruction yields $γ_{\rm PPN} = 1.102^{+0.148}_{-0.125}$, consistent with GR at $1σ$ confidence level, while the cubic spline gives $γ_{\rm PPN} = 1.150^{+0.139}_{-0.118}$, consistent with GR at $2σ$ confidence level. For the $P_2$ model, the ANN reconstruction gives $γ_{\rm PPN} = 1.315^{+0.181}_{-0.155}$, compatible with GR at $2σ$, while the spline gives $γ_{\rm PPN} = 1.485^{+0.193}_{-0.168}$, showing mild tension at $\sim2.5σ$. The constraints exhibit a clear dependence on the adopted lens mass model, underscoring the critical role of lens modeling. No significant correlation is observed between $γ_{\rm PPN}$ and the Einstein radius. Overall, current galaxy-scale observations are consistent with GR, providing no evidence for deviations from Einstein's theory on kiloparsec scales.

Figures (5)

• Figure 1: Comparison of BAO angular scale reconstructions using different methods: cubic spline with blue line, ANN with green line, alongside 2D BAO, 3D BAO, and DESI DR2 measurements with error bars. The shaded regions represent the corresponding $1\sigma$ prediction uncertainties.
• Figure 2: Posterior distributions of $(\gamma_{\rm PPN},\gamma_0)$ for the $P_1$ model. The contours show the 68% and 95% confidence regions. Blue contours correspond to the ANN reconstruction using raw data, while red contours correspond to the cubic spline reconstruction.
• Figure 3: Posterior distributions of $(\gamma_{\rm PPN},\gamma_0,\gamma_z)$ for the $P_2$ model. The contours show the 68% and 95% confidence regions. Blue contours correspond to the ANN reconstruction using raw data, while red contours correspond to the cubic spline reconstruction.
• Figure 4: Based on the P1 model ($\gamma=2$), the distribution of $\gamma_{\rm PPN}$ estimates inferred from 120 SGL systems as a function of lens redshift $z_l$. The horizontal dashed line indicates the GR prediction ($\gamma_{\rm PPN}=1$). Data points from different surveys are distinguished by different markers.
• Figure 5: Based on the P1 model ($\gamma=2$), the distribution of $\gamma_{\rm PPN}$ estimates inferred from 120 SGL systems as a function of Einstein radius $R_E$. The horizontal dashed line indicates the GR prediction ($\gamma_{\rm PPN}=1$). Data points from different surveys are distinguished by different markers.