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Testing the cosmic distance duality relation using Type Ia supernovae and radio quasars through model-independent methods
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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.
Forward citations
Cited by 4 Pith papers
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Testing the cosmic distance duality relation using Type Ia supernovae and BAO observations
BAO and supernova data are consistent with the cosmic distance duality relation, and a new marginalization over the absolute magnitude and sound horizon scale makes the test independent of those nuisance parameters.
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