标题： 基于Pantheon和eBOSS DR16类星体样本的宇宙距离对偶关系的模型无关检验 显示英文标题

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

摘要： 本文通过结合最新的五个重子声波振荡（BAO）测量数据和Pantheon型Ia超新星（SNIa）样本，进行了一个全新的模型无关的宇宙学检验，以验证宇宙距离对偶关系（CDDR）。特别是，使用了来自扩展重子振荡光谱巡天（eBOSS）数据发布（DR）16类星体样本在有效红移$z=1.48$的BAO测量，并采用两种方法来克服红移匹配问题，即Pantheon样本的压缩形式以及人工神经网络（ANN）与分箱SNIa方法的结合。我们的结果显示，CDDR与观测结果一致，并且高红移的BAO和SNIa数据能够有效地加强对CDDR违反参数的约束，置信区间减少了20%以上。此外，我们发现观测数据的压缩形式可以对CDDR提供更严格的约束，因此可以推广到测试CDDR时对有限样本大小的实际观测数据的应用中。 显示英文摘要

摘要： 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.