Simulation Based Inference for Efficient Theory Space Sampling: an Application to Supersymmetric Explanations of the Anomalous Muon (g-2)

TL;DR

This paper introduces simulation-based inference algorithms that efficiently explore supersymmetric theory spaces, successfully identifying models explaining the muon g-2 anomaly with fewer evaluations.

Contribution

The paper presents novel sequential likelihood-to-evidence ratio neural estimation algorithms for efficient theory space sampling in particle physics.

Findings

Successfully identified supersymmetric models explaining muon g-2

Reduced number of model evaluations needed

Demonstrated potential for future applications in physics

Abstract

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 non-trivial models in an experimentally-constrained theory space. Finally we summarize further potential possible uses of these algorithms in future studies.