Efficiently exploring multidimensional parameter spaces beyond the Standard Model
Efficiently exploring multidimensional parameter spaces beyond the Standard Model
We propose a method to ease the challenges of exploring multidimensional parameter spaces in beyond-the-Standard Model theories. We evaluate the model likelihood for any choice of parameters by sampling the theory parameters intelligently and building a kernel density estimator. By reducing the number of expensive Monte Carlo simulations, this method …