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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 2 Pith papers
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Normalizing Flow-Assisted Nested Sampling on Type-II Seesaw Model
A RealNVP normalizing flow trained inside nested sampling accelerates Bayesian scans of the Type-II seesaw parameter space and yields posterior constraints on scalar masses and couplings.
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DLScanner: A parameter space scanner package assisted by deep learning methods
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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