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Constraining Cosmological and Galaxy Parameters using Strong Gravitational Lensing Systems

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 نشر من قبل Darshan Kumar
 تاريخ النشر 2020
  مجال البحث فيزياء
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Strong gravitational lensing along with the distance sum rule method can constrain both cosmological parameters as well as density profiles of galaxies without assuming any fiducial cosmological model. To constrain galaxy parameters and cosmic curvature $(Omega_{k0})$, we use the distance ratio data from a recently compiled database of $161$ galactic scale strong lensing systems. We use databases of supernovae type-Ia (Pantheon) and Gamma Ray Bursts (GRBs) for calculating the luminosity distance. To study the model of the lens galaxy, we consider a general lens model namely, the Extended Power-Law model. Further, we take into account two different parametrisations of the mass density power-law index $(gamma)$ to study the dependence of $gamma$ on redshift. The best value of $Omega_{k0}$ suggests a closed universe, though a flat universe is accommodated at $68%$ confidence level. We find that parametrisations of $gamma$ have a negligible impact on the best fit value of the cosmic curvature parameter. Furthermore, measurement of time delay can be a promising cosmographic probe via time delay distance that includes the ratio of distances between the observer, the lens and the source. We again use the distance sum rule method with time-delay distance dataset of H0LiCOW to put constraints on the Cosmic Distance Duality Relation (CDDR) and the cosmic curvature parameter $(Omega_{k0})$. For this we consider two different redshift-dependent parametrisations of the distance duality parameter $(eta)$. The best fit value of $Omega_{k0}$ clearly indicates an open universe. However, a flat universe can be accommodated at $95%$ confidence level. Further, at $95%$ confidence level, no violation of CDDR is observed. We believe that a larger sample of strong gravitational lensing systems is needed in order to improve the constraints on the cosmic curvature and distance duality parameter.



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