Model-independent test for the cosmic distance duality relation with Pantheon and eBOSS DR16 quasar sample

TL;DR

This study performs a model-independent test of the cosmic distance duality relation using recent BAO and SNIa data, employing novel methods to improve redshift matching and constraint precision.

Contribution

It introduces a combined approach using ANN and data compression to enhance constraints on CDDR violation parameters with high-redshift data.

Findings

CDDR is consistent with current observations.

High-redshift data improves constraint accuracy by over 20%.

Data compression yields more rigorous CDDR constraints.

Abstract

In this paper, we carry out a new model-independent cosmological test for the cosmic distance duality relation~(CDDR) by combining the latest five baryon acoustic oscillations (BAO) measurements and the Pantheon type Ia supernova (SNIa) sample. Particularly, the BAO measurement from extended Baryon Oscillation Spectroscopic Survey~(eBOSS) data release~(DR) 16 quasar sample at effective redshift $z=1.48$ is used, and two methods, i.e. a compressed form of Pantheon sample and the Artificial Neural Network~(ANN) combined with the binning SNIa method, are applied to overcome the redshift-matching problem. Our results suggest that the CDDR is compatible with the observations, and the high-redshift BAO and SNIa data can effectively strengthen the constraints on the violation parameters of CDDR with the confidence interval decreasing by more than 20 percent. In addition, we find that the compressed form of observational data can provide a more rigorous constraint on the CDDR, and thus can be generalized to the applications of other actual observational data with limited sample size in the test for CDDR.