Efficient sampling of constrained high-dimensional theoretical spaces with machine learning

TL;DR

This paper introduces a machine learning-based generative modeling approach to efficiently sample high-dimensional constrained parameter spaces in physics models, significantly reducing computational costs compared to traditional brute-force methods.

Contribution

The authors develop a novel generative modeling technique that improves sampling efficiency in high-dimensional physics parameter spaces with constraints.

Findings

Achieves orders of magnitude faster sampling than brute force methods.

Successfully samples the constrained MSSM consistent with Higgs mass measurements.

Demonstrates applicability to complex high-dimensional models.

Abstract

Models of physics beyond the Standard Model often contain a large number of parameters. These form a high-dimensional space that is computationally intractable to fully explore. Experimental constraints project onto a subspace of viable parameters, but mapping these constraints to the underlying parameters is also typically intractable. Instead, physicists often resort to scanning small subsets of the full parameter space and testing for experimental consistency. We propose an alternative approach that uses generative models to significantly improve the computational efficiency of sampling high-dimensional parameter spaces. To demonstrate this, we sample the constrained and phenomenological Minimal Supersymmetric Standard Models subject to the requirement that the sampled points are consistent with the measured Higgs boson mass. Our method achieves orders of magnitude improvements in sampling efficiency compared to a brute force search.