Adaptive scanning - a proposal how to scan theoretical predictions over a multi-dimensional parameter space efficiently
arXiv:hep-ph/0407340 · doi:10.1016/j.cpc.2005.03.104
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.
8 pages, 4 figures
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