Astronomy and Astrophysics – Astrophysics – Cosmology and Extragalactic Astrophysics
Scientific paper
2012-03-28
Astronomy and Astrophysics
Astrophysics
Cosmology and Extragalactic Astrophysics
15 pages, 10 figures, submitted to A&A; comments are welcome
Scientific paper
The weak gravitational lensing distortion of distant galaxy images ("sources") probes the projected large-scale matter distribution in the Universe. The availability of redshift information in galaxy surveys also allows us to recover the radial matter distribution to a certain degree. To improve the S/N in the mass mapping, we combine the lensing information with the spatial clustering of a population of galaxies that trace the matter density with a known galaxy bias. We construct a minimum-variance estimator for the 3D matter density that incorporates the angular distribution of galaxy tracers, which are coarsely binned in redshift. Merely the second-order biasing of the tracers has to be known, which can in principle be self-consistently constrained in the data by lensing techniques. To study the new estimator, we generate a mock survey with galaxies that trace the matter density with a Gaussian linear stochastic bias. The filter smoothes and linearly mixes the individual lensing mass and tracer number density maps into a combined mass map. The weighting in the mixing depends on the S/N of the individual maps and the correlation, $R$, of the matter and galaxy density. We down-weigh the influence of the dominant tracer number density by rescaling the noise covariance in the filter. Even for a moderate mixing, the S/N in the mass map improves by a factor $\sim2$, most strongly for $z\gtrsim0.5$. Moreover, the systematic offset between a true and apparent mass peak distance in a lensing-only map is eliminated, even for weak correlations of $R\sim0.4$. The mixing, however, introduces a new noise component because of the stochasticity in the matter-tracer density relation. We give a prescription of the noise level and the average radial smoothing in the Gaussian regime. [Abridged]
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