SpaceMath v.2.0 with machine learning. A Mathematica package for beyond the standard model parameter space searches

Abstract

SpaceMath v.2.0 with Machine Learning is an extension of SpaceMath v.1.0 and implements processes with Flavor-Changing Neutral Currents at tree and one-loop level, namely, i) Radiative decays $\ell_i\to\ell_j \gamma$, ii) $\ell_i\to \ell_j \ell_k \bar{\ell}_k$ decays ($\ell_i=\tau,\,\mu$, $\ell_{j,\,k}=\mu,\,e$, with $\ell_i \neq\ell_j \neq\ell_k$), iii) anomalous magnetic moment of the muon $\delta a_\mu$ and iv) decays $B_{s,d}^0\to \mu^-\mu^+$. In addition to scanning parameter spaces, \texttt{SpaceMath v.2.0} has a novel implementation that make use of Machine Learning algorithms to predict Benchmark Points for numerical evaluations of observables in particle physics. A detailed example applied to the \textit{Two-Higgs Doublet Model of type III} parameter space is developed.