In large-scale-structure projects, when galaxy (or other tracer) clustering is measured in real space, the computation involves spatial positions of the tracers, and spatial positions of a large set of random points, distributed uniformly (within the window function). These latter points can be thought of as a comparison population. However, it is equally true that they can be thought of as performing some simple integrals by Monte Carlo method. If you see them that way—as a tool for integrating—it becomes obvious that there must be far better and far faster ways to do this! After all, non-adaptive Monte Carlo methods are far inferior to even stupidly adaptive schemes. I discussed all this with Kate Storey-Fisher (NYU) yesterday and today.
No comments:
Post a Comment