Mathematics – Logic
Scientific paper
May 2010
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010aas...21631806k&link_type=abstract
American Astronomical Society, AAS Meeting #216, #318.06; Bulletin of the American Astronomical Society, Vol. 41, p.908
Mathematics
Logic
Scientific paper
The new method of counting peaks in weak lensing (WL) maps, as a function of their height, to probe models of dark energy and to constrain cosmological parameters offers advantages over similar, more traditional statistics like cluster counts: Because peaks can be identified in two-dimensional WL maps directly, they can provide constraints which are free from potential selection effects and biases involved in identifying and determining the masses of galaxy clusters. Our pilot study (Kratochvil, Haiman, May 2009), where we investigated three cosmological models with different constant values of the dark energy equation of state parameter w=-0.8, w=-1, w=-1.2 and with a fixed normalization of the primordial power spectrum, revealed a parameter sensitivity of w which warrants a numerically very costly in-depth study when marginalization over other uncertain cosmological parameters is included. Towards that goal, we present recent results from a new, extensive simulation suite of ninety 5123-particle N-body simulations, run on New York Blue at BNL, allowing us to vary several cosmological parameters individually and yielding vastly improved statistics.
Studies have shown that weak lensing maps likely contain as much information in the nonlinear regime as in the linear one. While being a powerful probe of the nonlinear regime, weak lensing peak counts capture by definition only a subset of the total available information in the convergence maps. In particular, we found that relatively low-amplitude peaks account for most of the parameter sensitivity detected, hinting to a more complicated structure. Therefore, we extend our work by probing the morphology of the maps with Minkowski functionals, in our quest to extract the most possible information from the nonlinear aspects of large-scale structure formation.
Haiman Zoltan
Kratochvil Jan Michael
Lim Eugene A.
May Martin
Wang Shuang
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