Search

Constraining Beyond the Standard Model theories usually involves scanning highly multi-dimensional parameter spaces and check observable predictions against experimental bounds and theoretical constraints. Such task is often timely and computationally expensive, especially when the model is severely constrained and thus leading to very low random sampling efficiency. In this work we tackled this challenge using Artificial Intelligence and Machine Learning search algorithms used for Black-Box optimisation problems. Using the cMSSM and the pMSSM parameter spaces, we consider both the Higgs mass and the Dark Matter Relic Density constraints to study their sampling efficiency and parameter space coverage. We find our methodology to produce orders of magnitude improvement of sampling efficiency whilst reasonably covering the parameter space., We thank José Santiago Pérez and Jorge Romão for the careful reading of the paper draft and for the useful discussions. This work is supported by FCT - Fundação para a Ciência e a Tecnologia, I.P. under project CERN/FIS-PAR/0024/2019. FAS is also supported by FCT under the research grant with reference UI/BD/153105/2022. The computational work was partially done using the resources made available by RNCA and INCD under project CPCA/A1/401197/2021, info:eu-repo/semantics/publishedVersion

We propose a simple clockwork model of flavor which successfully generates the Standard Model flavor hierarchies from random order-one couplings. With very few parameters we achieve distributions of models in excellent agreement with observation. We explain in some detail the interpretation of our mechanism as random localization of zero modes in theory space. The scale of the vectorlike fermions is mostly constrained by lepton flavor violation with secondary constraints arising from rare meson decays., 27 pages + appendices