Second-order weak lensing from modified gravity

We explore the sensitivity of weak gravitational lensing to second-order corrections to the spacetime metric within a cosmological adaptation of the parameterized post-Newtonian framework. Whereas one might expect nonlinearities of the gravitational field to introduce non-Gaussianity into the statistics of the lensing convergence field, we show that such corrections are actually always small within a broad class of scalar-tensor theories of gravity. We show this by first computing the weak lensing convergence within our parameterized framework to second order in the gravitational potential, and then computing the relevant post-Newtonian parameters for scalar-tensor gravity theories. In doing so we show that this potential systematic factor is generically negligible, thus clearing the way for weak lensing to provide a direct tracer of mass on cosmological scales for a wide class of gravity theories despite uncertainties in the precise nature of the departures from general relativity.Comment: 13 pages, 1 figure; v2: minor edits to match the PRD accepted versio

Testing dark energy paradigms with weak gravitational lensing

Any theory invoked to explain cosmic acceleration predicts consistency relations between the expansion history, structure growth, and all related observables. Currently there exist high-quality measurements of the expansion history from Type Ia supernovae, the cosmic microwave background temperature and polarization spectra, and baryon acoustic oscillations. We can use constraints from these datasets to predict what future probes of structure growth should observe. We apply this method to predict what range of cosmic shear power spectra would be expected if we lived in a LambdaCDM universe, with or without spatial curvature, and what results would be inconsistent and therefore falsify the model. Though predictions are relaxed if one allows for an arbitrary quintessence equation of state $-1\le w(z)\le 1$, we find that any observation that rules out LambdaCDM due to excess lensing will also rule out all quintessence models, with or without early dark energy. We further explore how uncertainties in the nonlinear matter power spectrum, e.g. from approximate fitting formulas such as Halofit, warm dark matter, or baryons, impact these limits.Comment: 12 pages, 11 figures, submitted to PR

Luminosity distance in Swiss cheese cosmology with randomized voids. II. Magnification probability distributions

We study the fluctuations in luminosity distances due to gravitational lensing by large scale (> 35 Mpc) structures, specifically voids and sheets. We use a simplified "Swiss cheese" model consisting of a \Lambda -CDM Friedman-Robertson-Walker background in which a number of randomly distributed non-overlapping spherical regions are replaced by mass compensating comoving voids, each with a uniform density interior and a thin shell of matter on the surface. We compute the distribution of magnitude shifts using a variant of the method of Holz & Wald (1998), which includes the effect of lensing shear. The standard deviation of this distribution is ~ 0.027 magnitudes and the mean is ~ 0.003 magnitudes for voids of radius 35 Mpc, sources at redshift z_s=1.0, with the voids chosen so that 90% of the mass is on the shell today. The standard deviation varies from 0.005 to 0.06 magnitudes as we vary the void size, source redshift, and fraction of mass on the shells today. If the shell walls are given a finite thickness of ~ 1 Mpc, the standard deviation is reduced to ~ 0.013 magnitudes. This standard deviation due to voids is a factor ~ 3 smaller than that due to galaxy scale structures. We summarize our results in terms of a fitting formula that is accurate to ~ 20%, and also build a simplified analytic model that reproduces our results to within ~ 30%. Our model also allows us to explore the domain of validity of weak lensing theory for voids. We find that for 35 Mpc voids, corrections to the dispersion due to lens-lens coupling are of order ~ 4%, and corrections to due shear are ~ 3%. Finally, we estimate the bias due to source-lens clustering in our model to be negligible