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Strong lensing as a test of the vacuum energy density in the LCDM model

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Title: Strong lensing as a test of the vacuum energy density in the LCDM model
Author(s): Kupri, Artem
Contributor: University of Helsinki, Faculty of Science, Department of Physics
Discipline: Astronomy
Language: English
Acceptance year: 2014
In this Master's thesis, I present for the first time the consideration of the effect of uncertainty associated with the astrophysical modeling of lens systems on the derived value of Omega_Lambda. I show that strong gravitational lensing, in the Universe described by the LCDM model, can be used for determination of a value of the vacuum energy density. The models used in this work are based on differences between the observed stellar velocity dispersions and the actual stellar velocity dispersions of elliptical and lenticular galaxies, which play a role of lenses in lensing systems. It will be shown that the accuracy of observations is very important in such kind of calculations. I present an own derivation of the formulas used in this work for determination of Omega_Lambda, which was never done in a reference work [Schwab et al., 2010]. Moreover, in difference from [Schwab et al., 2010], I will represent these formulas in the correct form. In this work, I use 30 lensing systems, observed by the Sloan Digital Sky Survey and the Hubble Space Telescope. I calculate the angular diameter distances of lensing and background galaxies and combine them with the stellar velocity dispersion measurements, taking into account the total mass density, the luminosity density and the anisotropy of the lensing galaxy. Finally, I have performed chi-squared test to identify the acceptable model. The value of the vacuum energy density determined in this thesis, by using a restricted sample (26 galaxy lensing systems), has realistic confidence intervals, much larger than previously published. I show that the strong constraints on the value of Omega_Lambda determined in some other similar works ([Schwab et al., 2010] and [Cao et al., 2012]) depend on inclusion of systems that are not described by the model.

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