论文标题
主动学习BSM参数空间
Active learning BSM parameter spaces
论文作者
论文摘要
主动学习（AL）具有有趣的新模型参数扫描功能。我们展示了各种模型，这些模型为传统的繁琐工作带来了BSM模型的界限。在MSSM中，此方法产生更准确的界限。鉴于我们先前的出版物，我们进一步完善了SMSQQ模型的参数空间的探索，并将暗物质singlet的最大质量更新为48.4 TEV。最后，我们证明该技术在MDGSSM等更复杂的模型中特别有用。
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.
