Exploring parameter spaces with artificial intelligence and machine learning black-box optimization algorithms

Abstract

Constraining beyond the Standard Model theories usually involves scanning highly multidimensional parameter spaces and checking observable predictions against experimental bounds and theoretical constraints. Such a task is often timely and computationally expensive, especially when the model is severely constrained and thus leading to very low random sampling efficiency. In this work we tackled this challenge using artificial intelligence and machine learning search algorithms used for black-box optimization problems. Using the constrained minimal supersymmetric standard model and the phenomenological minimal supersymmetric standard model parameter spaces, we consider both the Higgs mass and the dark matter relic density constraints to study their sampling efficiency and parameter space coverage. We find our methodology to produce orders of magnitude improvement of sampling efficiency while reasonably covering the parameter space.