Astronomy and Astrophysics – Astrophysics – Cosmology and Extragalactic Astrophysics
Scientific paper
2011-09-21
Astronomy and Astrophysics
Astrophysics
Cosmology and Extragalactic Astrophysics
13 pages, 6 figures. Fixed an error in equation 19 which changes the right hand panels of figures 1 and 2, and modifies conclu
Scientific paper
We consider how upcoming photometric large scale structure surveys can be optimized to measure the properties of dark energy and possible cosmic scale modifications to General Relativity in light of realistic astrophysical and instrumental systematic uncertainities. In particular we include flexible descriptions of intrinsic alignments, galaxy bias and photometric redshift uncertainties in a Fisher Matrix analysis of shear, position and position-shear correlations, including complementary cosmological constraints from the CMB. We study the impact of survey tradeoffs in depth versus breadth, and redshift quality. We parameterise the results in terms of the Dark Energy Task Force figure of merit, and deviations from General Relativity through an analagous Modified Gravity figure of merit. We find that intrinsic alignments weaken the dependence of figure of merit on area and that, for a fixed observing time, a fiducial Stage IV survey plateaus above roughly 10,000deg2 for DE and peaks at about 5,000deg2 as the relative importance of IAs at low redshift penalises wide, shallow surveys. While reducing photometric redshift scatter improves constraining power, the dependence is shallow. The variation in constraining power is stronger once IAs are included and is slightly more pronounced for MG constraints than for DE. The inclusion of intrinsic alignments and galaxy position information reduces the required prior on photometric redshift accuracy by an order of magnitude for both the fiducial Stage III and IV surveys, equivalent to a factor of 100 reduction in the number of spectroscopic galaxies required to calibrate the photometric sample.
Bean Rachel
Bridle Sarah
Kirk Donnacha
Laszlo Istvan
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