Publication: Lens galaxies in the Illustris simulation: power-law models and the bias of the Hubble constant from time delays
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2016
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Oxford University Press
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Xu, Dandan, Dominique Sluse, Peter Schneider, Volker Springel, Mark Vogelsberger, Dylan Nelson, and Lars Hernquist. 2015. “Lens Galaxies in the Illustris Simulation: Power-Law Models and the Bias of the Hubble Constant from Time Delays.” Monthly Notices of the Royal Astronomical Society 456 (1): 739–55. https://doi.org/10.1093/mnras/stv2708.
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Abstract
A power-law density model, i.e. rho(r) proportional to r(-gamma)', has been commonly employed in strong gravitational lensing studies, including the so-called time-delay technique used to infer the Hubble constant H-0. However, since the radial scale at which strong lensing features are formed corresponds to the transition from the dominance of baryonic matter to dark matter, there is no known reason why galaxies should follow a power law in density. The assumption of a power law artificially breaks the mass-sheet degeneracy, a well-known invariance transformation in gravitational lensing which affects the product of Hubble constant and time delay and can therefore cause a bias in the determination of H-0 from the time-delay technique. In this paper, we use the Illustris hydrodynamical simulations to estimate the amplitude of this bias, and to understand how it is related to observational properties of galaxies. Investigating a large sample of Illustris galaxies that have velocity dispersion sigma(SIE) >= 160 km s(-1) at redshifts below z = 1, we find that the bias on H-0 introduced by the power-law assumption can reach 20-50 per cent, with a scatter of 10-30 per cent (rms). However, we find that by selecting galaxies with an inferred power-law model slope close to isothermal, it is possible to reduce the bias on H-0 to less than or similar to 5 per cent and the scatter to less than or similar to 10 per cent. This could potentially be used to form less biased statistical samples for H-0 measurements in the upcoming large survey era.
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