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Active learning BSM parameter spaces
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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.
Forward citations
Cited by 3 Pith papers
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A new scanner package combines a similarity-learning neural network with VEGAS adaptive sampling to collect valid points in BSM parameter scans faster than earlier ML-based methods.
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Interplay of $95$ GeV Diphoton Excess and Dark Matter in Supersymmetric Triplet Model
A parameter scan shows the triplet-extended MSSM has regions that fit the 95 GeV diphoton signal, the 125 GeV Higgs mass, and the dark matter relic density simultaneously.
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