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We analyze the correlations between central dark matter (DM) content of early-type galaxies and their sizes and ages, using a sample of intermediate-redshift (z ~ 0.2) gravitational lenses from the SLACS survey, and by comparing them to a larger sample of z ~ 0 galaxies. We decompose the deprojected galaxy masses into DM and stellar components using combinations of strong lensing, stellar dynamics, and stellar populations modeling. For a given stellar mass, we find that for galaxies with larger sizes, the DM fraction increases and the mean DM density decreases, consistently with the cuspy halos expected in cosmological formation scenarios. The DM fraction also decreases with stellar age, which can be partially explained by the inverse correlation between size and age. The residual trend may point to systematic dependencies on formation epoch of halo contraction or stellar initial mass functions. These results are in agreement with recent findings based on local galaxies by Napolitano, Romanowsky & Tortora (2010) and suggest negligible evidence of galaxy evolution over the last ~ 2.5 Gyr other than passive stellar aging.
Dynamical studies of local ETGs and the Fundamental Plane point to a strong dependence of M/L ratio on luminosity (and stellar mass) with a relation of the form $M/L propto L^{gamma}$. The tilt $gamma$ may be caused by various factors, including stel
Using a combined analysis of strong lensing and galaxy dynamics, we characterize the mass distributions and M/L ratios of galaxy groups, which form an important transition regime in Lambda-CDM cosmology. By mapping the underlying mass distribution, w
We have acquired intermediate resolution spectra in the 3700-7000 A wavelength range for a sample of 65 early-type galaxies predominantly located in low density environments, a large fraction of which show emission lines. The spectral coverage and th
This work aims to study the distribution of luminous and dark matter in Coma early-type galaxies. Dynamical masses obtained under the assumption that mass follows light do not match with the masses of strong gravitational lens systems of similar velo
We investigate the possibility of applying machine learning techniques to images of strongly lensed galaxies to detect a low mass cut-off in the spectrum of dark matter sub-halos within the lens system. We generate lensed images of systems containing