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arxiv: 2512.06454 · v2 · submitted 2025-12-06 · 🌌 astro-ph.CO

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Testing the Distance Duality Relation with Cosmological Observations at high Redshift using Artificial Neural Network

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keywords highredshiftartificialcosmologicaldistancedualitynetworkneural
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The cosmic Distance Duality Relation (DDR) is a fundamental prediction of metric gravity under photon number conservation. In this work, we perform a model-independent test of the DDR using Pantheon+ type Ia supernovae (SN Ia), \emph{Fermi} gamma-ray bursts (GRBs) with the FULL and GOLD samples, the Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2) baryon acoustic oscillation (BAO) measurements, and the galaxy-scale strong gravitational lensing (SGL) system samples at high redshift $0.01 < z \lesssim 8$ using an artificial neural network (ANN) approach. Our results show that the standard DDR is consistent with cosmological observations at high redshift within the $\sim 2 \sigma$ confidence level.

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  1. Model-independent test of the cosmic distance duality relation with recent observational data

    astro-ph.CO 2026-03 conditional novelty 6.0

    Two model-independent methods applied to latest SN and BAO data find the cosmic distance duality relation consistent with observations within 1 sigma and no evidence of violation.