Simulation based inference for efficient theory space sampling: An application to supersymmetric explanations of the anomalous muon g -2

For the purpose of minimizing the number of sample model evaluations, we propose and study algorithms that utilize (sequential) versions of likelihood-to-evidence ratio neural estimation. We apply our algorithms to a supersymmetric interpretation of the anomalous muon magnetic dipole moment in the context of a phenomenological minimal supersymmetric extension of the standard model, and recover nontrivial models in an experimentally constrained theory space. Finally we summarize further potential possible uses of these algorithms in future studies.