Petri A., May M., Haiman Z., Kratochvil J.M.
Department of Physics, Columbia University, New York, NY, United States; Physics Department, Brookhaven National Laboratory, Upton, NY, United States; Department of Astronomy, Columbia University, New York, NY, United States; Astrophysics and Cosmology Research Unit, University of KwaZulu-Natal, Westville, Durban, South Africa
Petri, A., Department of Physics, Columbia University, New York, NY, United States, Physics Department, Brookhaven National Laboratory, Upton, NY, United States; May, M., Physics Department, Brookhaven National Laboratory, Upton, NY, United States; Haiman, Z., Department of Astronomy, Columbia University, New York, NY, United States; Kratochvil, J.M., Astrophysics and Cosmology Research Unit, University of KwaZulu-Natal, Westville, Durban, South Africa
Residual errors in shear measurements, after corrections for instrument systematics and atmospheric effects, can impact cosmological parameters derived from weak lensing observations. Here we combine convergence maps from our suite of ray-tracing simulations with random realizations of spurious shear. This allows us to quantify the errors and biases of the triplet (Ωm,w,σ8) derived from the power spectrum (PS), as well as from three different sets of non-Gaussian statistics of the lensing convergence field: Minkowski functionals (MFs), low-order moments (LMs), and peak counts (PKs). Our main results are as follows: (i) We find an order of magnitude smaller biases from the PS than in previous work. (ii) The PS and LM yield biases much smaller than the morphological statistics (MF, PK). (iii) For strictly Gaussian spurious shear with integrated amplitude as low as its current estimate of σsys2≈10-7, biases from the PS and LM would be unimportant even for a survey with the statistical power of Large Synoptic Survey Telescope. However, we find that for surveys larger than ≈100deg2, non-Gaussianity in the noise (not included in our analysis) will likely be important and must be quantified to assess the biases. (iv) The morphological statistics (MF, PK) introduce important biases even for Gaussian noise, which must be corrected in large surveys. The biases are in different directions in (Ωm,w,σ8) parameter space, allowing self-calibration by combining multiple statistics. Our results warrant follow-up studies with more extensive lensing simulations and more accurate spurious shear estimates. © 2014 American Physical Society.