Astronomy and Astrophysics – Astronomy
Scientific paper
Dec 2004
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004aas...205.6914i&link_type=abstract
American Astronomical Society Meeting 205, #69.14; Bulletin of the American Astronomical Society, Vol. 36, p.1461
Astronomy and Astrophysics
Astronomy
Scientific paper
We present new results on how well cosmic shear tomography can improve dark energy parameter constraints. In view of the parameterization dependence problems, we probe the dark energy using its density and also two variants of its equation of state. For the former, we model the density as a continuous function interpolated using 2 dimensionless amplitude parameters evaluated at equally spaced redshits. Unlike previous studies, we look here for combinations of experiments that will bring the 2 sigma errors on these parameters to a satisfactory level of precision, say 0.02. Many dark energy models occupy a very narrow range of this parameter sub-space, and only such precision will decisively settle the question. For CMB, we consider future constraints from one year of data from Planck, 8 years of data from WMAP, and one year of data from the Atacama Cosmology Telescope (ACT). We use two sets of 2000 supernovae with maximum redshifts 0.8 and 1.5 respectively, and consider two cosmic shear reference surveys: a wide survey with two tomographic bins and a deep survey with 10 bins. The 2-sigma constraints found from Planck, supernovae, and the wide lensing survey are impressive but not enough to close the question. When the deep survey with 10 bin tomography is used, the constraints reduce to the required level of precision. Thus, we find that after the combination of the three probes, cosmic shear tomography with many bins and several thousands square degrees can add the necessary improvement to the constraints on dark energy. However, the probes considered here are faced with serious systematic effects that need to be well understood and tightly controlled in order to match the statistical accuracy and make these constraints achievable.
Ishak Mustapha
Spergel David N.
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