The Power of Genetic Algorithms: what remains of the pMSSM?
This work addresses the challenge of probing new physics in high-dimensional parameter spaces for physicists, offering a faster alternative to traditional methods like MCMC scans.
The researchers tackled the problem of efficiently analyzing high-dimensional physics models like the 19-dimensional pMSSM using Genetic Algorithms, showing that GAs achieve accurate results with only about 10^4 model evaluations, matching more costly methods, and they used this to falsify explanations for specific observables such as the muon anomalous magnetic moment and Galactic Centre gamma-ray excess.
Genetic Algorithms (GAs) are explored as a tool for probing new physics with high dimensionality. We study the 19-dimensional pMSSM, including experimental constraints from all sources and assessing the consistency of potential signals of new physics. We show that GAs excel at making a fast and accurate diagnosis of the cross-compatibility of a set of experimental constraints in such high dimensional models. In the case of the pMSSM, it is found that only ${\cal O}(10^4)$ model evaluations are required to obtain a best fit point in agreement with much more costly MCMC scans. This efficiency allows higher dimensional models to be falsified, and patterns in the spectrum identified, orders of magnitude more quickly. As examples of falsification, we consider the muon anomalous magnetic moment, and the Galactic Centre gamma-ray excess observed by Fermi-LAT, which could in principle be explained in terms of neutralino dark matter. We show that both observables cannot be explained within the pMSSM, and that they provide the leading contribution to the total goodness of the fit, with $χ^2_{δa_μ^{\mathrm{SUSY}}}\approx12$ and $χ^2_{\rm GCE}\approx 155$, respectively.