By comparing the dynamical and lensing masses of early-type lens galaxies, one can constrain both the cosmological parameters and the density profiles of galaxies. We explore the constraining power on cosmological parameters and the effect of the lens mass model in this method with 161 galaxy-scale strong lensing systems, which is currently the largest sample with both high resolution imaging and stellar dynamical data. We assume a power-law mass model for the lenses, and consider three different parameterizations for γ (i.e., the slope of the total mass density profile) to include the effect of the dependence of γ on redshift and surface mass density. When treating δ (i.e., the slope of the luminosity density profile) as a universal parameter for all lens galaxies, we find the limits on the cosmological parameter Ω m are quite weak and biased, and also heavily dependent on the lens mass model in the scenarios of parameterizing γ with three different forms. When treating δ as an observable for each lens, the unbiased estimate of Ω m can be obtained only in the scenario of including the dependence of γ on both the redshift and the surface mass density, that is Ω m = 0.381 +0.185 −0.154 at 68% confidence level in the framework of a flat ΛCDM model. We conclude that the significant dependencies of γ on both the redshift and the surface mass density, as well as the intrinsic scatter of δ among the lenses, need to be properly taken into account in this method.
Strong gravitational lensing, which can make a background source galaxy appears multiple times due to its light rays being deflected by the mass of one or more foreground lens galaxies, provides astronomers with a powerful tool to study dark matter, cosmology and the most distant Universe. PyAutoLens is an open-source Python 3.6+ package for strong gravitational lensing, with core features including fully automated strong lens modeling of galaxies and galaxy clusters, support for direct imaging and interferometer datasets and comprehensive tools for simulating samples of strong lenses. The API allows users to perform ray-tracing by using analytic light and mass profiles to build strong lens systems. Accompanying PyAutoLens is the autolens workspace, which includes example scripts, lens datasets and the HowToLens lectures in Jupyter notebook format which introduce non-experts to strong lensing using PyAutoLens. Readers can try PyAutoLens right now by going to the introduction Jupyter notebook on Binder or checkout the readthedocs for a complete overview of PyAutoLens's features.
We introduce the LEnSed laeS in the Eboss suRvey (LESSER) project, which aims to search for lensed Lyman-α Emitters (LAEs) in the Extended Baryon Oscillation Spectroscopic Survey (eBOSS). The final catalog contains 361 candidate lensing systems. The lens galaxies are luminous red galaxies (LRGs) at redshift 0.4 < z < 0.8, and the source galaxies are LAEs at redshift 2 < z < 3. The spectral resolution of eBOSS (∼2000) allows us to further identify the fine structures of Lyman-α ($\rm Ly\alpha$) emissions. Among our lensed LAE candidates, 281 systems present single-peaked line profiles while 80 systems show double-peaked features. Future spectroscopic/imaging follow-up observations of the catalog may shed light on the origin of diverse $\rm Ly\alpha$ line morphology, and provide promising labs for studying low mass dark matter haloes/subhaloes.
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