Probing the Cosmic Distance Duality Relation via Non-Parametric Reconstruction for High Redshifts

We test the validity of the cosmic distance duality relation (CDDR) by combining angular diameter distance and luminosity distance measurements from recent cosmological observations. For the angular diameter distance, we use data from transverse baryon acoustic oscillations and galaxy clusters. On the other hand, the luminosity distance is obtained from Type Ia supernovae in the Pantheon+ sample and from quasar catalogs. To reduce the large dispersion in quasar luminosity distances, we apply a selection criterion based on their deviation from the $\Lambda$CDM model and implement a binning procedure to suppress statistical noise. We reconstruct the CDDR using Gaussian Processes, a non-parametric supervised machine learning method. Our results show no significant deviation from the CDDR within the $2\sigma$ confidence level across the redshift range explored, supporting its validity even at high redshifts.