الضوء والمسافة: اختبار هندسة الكون سريع

الملخص الأصلي

كما كتبه المؤلفون، بالإنجليزية arXiv:2602.16869

We test the Etherington cosmic distance-duality relation (CDDR), by comparing Type Ia supernova (SNIa) luminosity-distance information from the Pantheon+ compilation with an angular-diameter-distance reconstructed from localized Fast Radio Bursts (FRBs). The core of our methodology is a data-driven reconstruction from FRBs using artificial neural networks (ANNs): we infer a smooth mean extragalactic dispersion-measure relation and use its redshift derivative to recover $$H(z)$$ and hence $$D_\mathrm{A}^{\rm FRB}(z)$$ without assuming a parametric form for the expansion history. Possible deviations from CDDR are parameterized through three one-parameter models of $$\eta(z)\equiv D_\mathrm{L}/[(1+z)^2D_\mathrm{A}]$$. We implement two complementary likelihoods: (i) a direct approach using individual SNIa with the full Pantheon+ covariance, and (ii) a machine-learning approach in which we reconstruct the SN Hubble diagram on the FRB redshift grid, propagating SN and FRB uncertainties into non-diagonal covariance matrices via Monte Carlo and bootstrap realizations. Within the FRB reconstruction, we anchor the mean extragalactic dispersion measure at $$z=0$$, which yields a data-driven constraint on the average host/near-source contribution $$\mathrm{DM}_{\rm host}=128.8\pm 34.1\,\mathrm{pc\,cm^{-3}}$$ ($$3\sigma$$ of statistical confidence). We find that both likelihood implementations give consistent posteriors and no statistically significant evidence for departures from CDDR at the current precision.