Dr. Andreas Maier

State-of-the-art Monte Carlo event simulations typically involve a sizeable fraction of events with negative weights. These negative weights greatly hamper the statistical convergence. In many cases, orders of magnitude more events have to be generated compared to purely positive weight samples. This problem can be addressed effectively by using the newly developed cell resampling method, which redistributes weights between events that are similar enough to be practically indistinguishable. I discuss the method and show applications to large high-multiplicity event samples.