Multiple measurements of quasars acting as standard probes:Model independent calibration and explori

• 作 者: XiaoGang Zheng Shuo Cao Marek Biesiada XiaoLei Li TongHua Liu YuTing Liu
• 机 构: School of Electrical and Electronic Engineering,Wuhan Polytechnic University,Wuhan 430023,China;Depa
• 出 处: 中国科学：物理学 力学 天文学（英文版）
• 发表日期: 2021年5期
• 关键词: dark energy experiments dark energy theory quasars

Recently,two classes of quasar samples were identified,which are promising as new cosmological probes extending to higher redshifts.The first sample uses the nonlinear relation between the ultraviolet and X-ray luminosities of quasars to derive luminos-ity distances,whereas the linear sizes of compact radio quasars in the second sample can serve as standardized rulers,providing angular-diameter distances.In this study,under the assumption of a fiat universe,we refreshed the calibration of multiple mea-surements of high-redshift quasars (in the framework of a cosmological-model-independent method with the newest Hubble parameters data).Furthermore,we placed constraints on four models that characterize the cosmic equation of state (w).The obtained results show that:(1) the two quasar samples could provide promising complementary probes at much higher redshifts,whereas compact radio quasars perform better than ultraviolet and X-ray quasars.at the current observational level;(2) strong degeneracy between the cosmic equation of state (w) and Hubble constant (H0) is revealed,which highlights the importance of independent determination of H0 from time-delay measurements of strongly lensed quasars;(3) together with other standard ruler probes,such as baryon acoustic oscillation dis tance measurements,the combined QSO+BAO measurements are consistent with the standard ACDM model at a constant equation of state w =-1;(4) ranking the cosmological models,the polynomial parametrization gives a rather good fit among the four cosmic-equation-of-state models,whereas the Jassal-Bagla-Padmanabhan(JBP) parametrization is substantially penalized by the Akaike Information Criterion and Bayesian Information Criterion.