SIMBAD references

Weak-lensing peak counts have been shown to be a powerful tool for cosmology. They provide non-Gaussian information of large scale structures and are complementary to second-order statistics. We propose a new flexible method for predicting weak-lensing peak counts, which can be adapted to realistic scenarios, such as a real source distribution, intrinsic galaxy alignment, mask effects, and photo-z errors from surveys. The new model is also suitable for applying the tomography technique and nonlinear filters. A probabilistic approach to modeling peak counts is presented. First, we sample halos from a mass function. Second, we assign them density profiles. Third, we place those halos randomly on the field of view. The creation of these ``fast simulations'' requires much less computing time than do N-body runs. Then, we perform ray-tracing through these fast simulation boxes and select peaks from weak-lensing maps to predict peak number counts. The computation is achieved by our Camelus algorithm. We compare our results to N-body simulations to validate our model. We find that our approach is in good agreement with full N-body runs. We show that the lensing signal dominates shape noise and Poisson noise for peaks with S/N between 4 and 6. Also, counts from the same S/N range are sensitive to Ω_m and σ₈. We show how our model can distinguish between various combinations of those two parameters. In this paper, we offer a powerful tool for studying weak-lensing peaks. The potential of our forward model is its high flexibility, which makes the using peak counts under realistic survey conditions feasible.