Adaptive scanning - a proposal how to scan theoretical predictions over a multi-dimensional parameter space efficiently

Abstract

A method is presented to exploit adaptive integration algorithms using importance sampling, like VEGAS, for the task of scanning theoretical predictions depending on a multi-dimensional parameter space. Usually, a parameter scan is performed with emphasis on certain features of a theoretical prediction. Adaptive integration algorithms are well-suited to perform this task very efficiently. Predictions which depend on parameter spaces with many dimensions call for such an adaptive scanning algorithm.