https://doi.org/10.1140/epjc/s10052-023-11368-3
Regular Article - Theoretical Physics
Active learning BSM parameter spaces
Laboratoire de Physique Théorique et Hautes Energies (LPTHE), UMR 7589, Sorbonne Université et CNRS, 4 place Jussieu, 75252, Paris Cedex 05, France
Received:
5
August
2022
Accepted:
26
February
2023
Published online:
1
April
2023
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.
© The Author(s) 2023
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