Quantifying the Impact of 2D and 3D BAO Measurements on the Cosmic Distance Duality Relation with HII Galaxy observation (2507.17113v1)

Abstract: The cosmic distance duality relation (CDDR) is a fundamental and practical condition in observational cosmology that connects the luminosity distance and angular diameter distance. Testing its validity offers a powerful tool to probe new physics beyond the standard cosmological model. In this work, for the first time, we present a novel consistency test of CDDR by combining HII galaxy data with a comprehensive set of Baryon Acoustic Oscillations (BAO) measurements. The BAO measurements include two-dimensional (2D) BAO and three-dimensional (3D) BAO, as well as the latest 3D BAO data from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2). We adopt four different parameterizations of the CDDR parameter, $\eta(z)$, to investigate possible deviations and their evolution with cosmic time. To ensure accurate redshift matching across datasets, we reconstruct the distance measures through a model-independent Artificial Neural Network (ANN) approach. Our analysis uniquely examines two distinct approaches: $i)$ marginalization over the BAO sound horizon $r_d$, and $ii)$ fixing $r_d$ to specific values. We find no significant deviation from the CDDR (less than 68% confidence level) in either the marginalized $r_d$ or the $r_d=147.05$ Mpc scenario. However, a slight deviation at the 68% confidence level is found when applying 2D-BAO data with $r_d=139.5$ Mpc. Furthermore, our analysis shows that all BAO data considered in this work support the validity of the CDDR, where 3D-DESI BAO provides the tightest constraints. We find no tension between 2D and 3D BAO measurements, which confirms their mutual consistency. In addition, the treatment of the sound horizon $r_d$ significantly impacts $\eta(z)$ constraints, which proves its importance in CDDR tests. Finally, the consistency of our results supports the standard CDDR and demonstrates the robustness of our analytical approach.