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