Cosmic Distance Duality Relation Tested with Neural Networks

This research investigates the cosmic Distance Duality Relation (DDR), a cornerstone of metric gravity that relies on photon number conservation. The study employs a model-independent approach using an artificial neural network (ANN) to test the DDR at high redshift (0.01 < z < 8). The analysis incorporates a diverse dataset, including Pantheon+ type Ia supernovae (SN Ia), Fermi gamma-ray bursts (GRBs), Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2) baryon acoustic oscillation (BAO) measurements, and galaxy-scale strong gravitational lensing (SGL) system samples. The results indicate that the standard DDR is consistent with cosmological observations at high redshift within the ~2σ confidence level. This finding reinforces the validity of current cosmological models and the underlying assumptions about gravity and photon behavior. The use of an ANN demonstrates a powerful and flexible tool for analyzing complex cosmological datasets and extracting meaningful information. However, it's important to acknowledge potential limitations and uncertainties. Deviations from the DDR, even within the 2σ confidence level, could hint at the need for refinements to our understanding of gravity or photon number conservation. Further research is warranted to explore potential systematic errors in the data and to push the boundaries of DDR testing to even higher redshifts. The implications of this research extend to the broader field of cosmology, informing the development of more accurate and robust models of the universe.

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