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No evidence for the evolution of mass density power-law index γ from strong gravitational lensing observation

No evidence for the evolution of mass density power-law index γ from strong gravitational lensing observation
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摘要 In this paper,we consider the singular isothermal sphere lensing model that has a spherically symmetric power-law mass distributionρ_(tot)(r)~r^(-γ).We investigate whether the mass density power-law indexγis cosmologically evolutionary by using the strong gravitational lensing(SGL)observation,in combination with other cosmological observations.We also check whether the constraint result ofγis affected by the cosmological model,by considering several simple dynamical dark energy models.We find that the constraint onγis mainly decided by the SGL observation and independent of the cosmological model,and we find no evidence for the evolution ofγfrom the SGL observation. In this paper, we consider the singular isothermal sphere lensing model that has a spherically symmetric power-law mass dis- tribution ρtot(r) - r-γ. We investigate whether the mass density power-law index y is cosmologically evolutionary by using the strong gravitational lensing (SGL) observation, in combination with other cosmological observations. We also check whether the constraint result of y is affected by the cosmological model, by considering several simple dynamical dark energy models. We find that the constraint on y is mainly decided by the SGL observation and independent of the cosmological model, and we find no evidence for the evolution of y from the SGL observation.
出处 《Science China(Physics,Mechanics & Astronomy)》 SCIE EI CAS CSCD 2017年第8期8-14,共7页 中国科学:物理学、力学、天文学(英文版)
基金 supported by the National Natural Science Foundation of China(Grant Nos.11522540,and 11690021) the Top-Notch Young Talents Program of China,and the Provincial Department of Education of Liaoning(Grant No.L2012087)
关键词 引力透镜效应 幂律指数 质量密度 证据 宇宙学模型 演化 观测 能量模型 strong gravitational lensing, mass density power-law index, dynamical dark energy, cosmological parameter estimation
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