Active learning BSM parameter spaces
Mark D. Goodsell, Ari Joury
Abstract
Abstract Active learning (AL) has interesting features for parameter scans of new models. We show on a variety of models that AL scans bring large efficiency gains to the traditionally tedious work of finding boundaries for BSM models. In the MSSM, this approach produces more accurate bounds. In light of our prior publication, we further refine the exploration of the parameter space of the SMSQQ model, and update the maximum mass of a dark matter singlet to 48.4 TeV. Finally we show that this technique is especially useful in more complex models like the MDGSSM.
Topics & Concepts
Parameter spaceDark matterVariety (cybernetics)Computer scienceParticle physicsSpace (punctuation)Work (physics)Artificial intelligencePhysicsMachine learningMathematicsGeometryQuantum mechanicsOperating systemParticle physics theoretical and experimental studiesDark Matter and Cosmic PhenomenaCosmology and Gravitation Theories