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Active learning BSM parameter spaces
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OA
AI
DOI：10.1140/epjc/s10052-023-11368-3.png)
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Abstract
En 中文
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.
Keywords:
PERTURBATIVE UNITARITY CONSTRAINTS
HIGGS-MASS
SUSY
FLEXIBLESUSY
SPECTRA
MSSM
TOOL