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

TL;DR
This paper develops a model-independent method combining BAO and strong lensing data to test general relativity on galactic scales, avoiding assumptions about cosmological parameters.
Contribution
It introduces a novel framework using neural networks and spline reconstruction to constrain the PPN parameter without relying on cosmological priors.
Findings
Results are consistent with GR within 2σ for most models.
Lens mass model significantly affects the PPN parameter constraints.
No evidence found for deviations from GR on kiloparsec scales.
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 $\gamma_{\rm…
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Taxonomy
TopicsCosmology and Gravitation Theories · Pulsars and Gravitational Waves Research · Galaxies: Formation, Evolution, Phenomena
