Testing the cosmic distance duality relation using Type Ia supernovae and radio quasars through model-independent methods

TL;DR

This paper tests the cosmic distance duality relation using model-independent methods by comparing radio quasars and supernovae data, addressing prior biases, and confirming the relation's consistency with observations.

Contribution

It introduces a bias-free, model-independent method to test the CDDR using QSOs and SNIa data, treating key parameters as nuisance variables and marginalizing over them.

Findings

CDDR is consistent with observational data

QSOs are effective for model-independent CDDR tests

Biases from prior assumptions significantly affect CDDR tests

Abstract

In this work, we perform a cosmological-model-independent test on the cosmic distance duality relation (CDDR) by comparing the angular diameter distance (ADD) obtained from the compact radio quasars (QSOs) with the luminosity distance (LD) from the Pantheon Type Ia supernovae (SNIa) sample. The binning method and Artificial Neural Network (ANN) are employed to match ADD data with LD data at the same redshift, and three different parameterizations are adopted to quantify the possible deviations from the CDDR. We initially investigate the impacts of the specific prior values for the absolute magnitude $M_{\rm B}$ from SNIa and the linear size scaling factor $l$ from QSOs on the CDDR test, demonstrating that these prior values introduce significant biases in the CDDR test. To avoid the biases, we propose a method independent of $M_{\rm B}$ and $l$ to test CDDR, which treats the fiducial value of a new variable $\kappa\equiv10^{M_{\rm B} \over 5}\,l$ as a nuisance parameter and then marginalize its impact with a flat prior in the statistical analysis. The results show that the CDDR is consistent with the observational data, and QSOs can serve as a powerful tool for testing the CDDR independent of cosmological models.