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arXiv · 2303.17407

Exploring the possible evolution of the mass density power-law index of strong gravitational lenses with a model-independent method

Abstract

In this work, we adopt a cosmological model-independent approach for the first time to test the question of whether the mass density power-law index($\gamma$) of the strong gravitational lensing system(SGLS) evolves with redshift, and the JLA SNe Ia sample and the quasar sample from Risaliti \& Lusso (2019) are used to provide the luminosity distances to be calibrated. Our work is based on the flat universe assumption and the cosmic distance duality relation. A reliable data-matching method is used to pair SGLS-SNe and SGLS-quasar. By using the maximum likelihood method to constrain the luminosity distance and $\gamma$ index, we obtain the likelihood function values for the evolved and non-evolved cases, and then use the Akaike weights and the BIC selection weights to compare the advantages and disadvantages of these two cases. We find that the $\gamma$ index is slightly more likely to be a non-evolutionary model for $\gamma=2$ in the case of the currently used samples with low redshift ($z_l<\sim$0.66). With Akaike weights, the relative probability is 66.3\% versus 33.7\% and 69.9\% versus 30.1\% for the SGLS+SNe Ia sample and SGLS+quasar sample, respectively, and with BIC selection weights, the relative probability is 87.4\% versus 12.6\% and 52.0\% versus 48.0\% for the two samples. In the evolving case for the relatively low redshift lens (SGLS+SNe Ia), with redshift 0.0625 to 0.659, $\gamma= 2.058^{+0.041}_{-0.040}-0.136^{+0.163}_{-0.165}z$. At high redshift (SGLS+quasar ), with redshift 0.0625 to 1.004, $\gamma= 2.051^{+0.076}_{-0.077}-0.171^{+0.214}_{-0.196}z$. Although not the more likely model, this evolved $\gamma$ case also fits the data well, with a negative and mild evolution for both low and high redshift samples.

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Jian Hu. 2023-03-30. Exploring the possible evolution of the mass density power-law index of strong gravitational lenses with a model-independent method. https://doi.org/10.3847/1538-4357%2Facc9aa

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