Testing the cosmic distance duality relation with baryon acoustic oscillations and supernovae data
Tian-Nuo Li, Guo-Hong Du, Peng-Ju Wu, Jing-Zhao Qi, Jing-Fei Zhang, Xin Zhang

TL;DR
This study tests the cosmic distance duality relation using combined BAO and supernova data with neural network matching, finding no significant deviations when accounting for calibration uncertainties.
Contribution
It introduces a neural network approach to match BAO and supernova data at the same redshift for testing the CDDR, considering different parameterizations and calibration effects.
Findings
No evidence of CDDR violation when M_B is free.
Deviations appear when fixing M_B to certain priors.
Results are consistent across different datasets.
Abstract
One of the most fundamental relationships in modern cosmology is the cosmic distance duality relation (CDDR), which describes the relationship between the angular diameter distance () and the luminosity distance (), and is expressed as: . In this work, we conduct a comprehensive test of the CDDR by combining baryon acoustic oscillation (BAO) data from the SDSS and DESI surveys with type Ia supernova (SN) data from PantheonPlus and DESY5. We utilize an artificial neural network approach to match the SN and BAO data at the same redshift. To explore potential violations of the CDDR, we consider three different parameterizations: (i) ; (ii) ; (iii) . Our results indicate that the calibration of the SN absolute magnitude plays a crucial role…
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