We demonstrate two sampling procedures assisted by machine learning models via regression and classification. The main objective is the use of a neural network to suggest points likely inside regions of interest, reducing the number of evaluations of time consuming calculations. We compare results from this approach with results from other sampling methods, namely Markov chain Monte Carlo and MultiNest, obtaining results that range from comparably similar to arguably better. In particular, we augment our classifier method with a boosting technique that rapidly increases the efficiency within a few iterations. We show results from our methods applied to a toy model and the type II 2HDM, using 3 and 7 free parameters, respectively. The code used for this paper and instructions are publicly available on the web.
翻译:我们通过回归和分类,展示了由机器学习模型协助的两个抽样程序,主要目标是使用神经网络,提出可能位于有关区域内的点,减少对时间消耗计算的评价次数,我们将这一方法的结果与其他抽样方法,即Markov链条蒙特卡洛和多纳斯特的结果进行比较,取得的结果从相似到可以说更好。特别是,我们用一种加速技术来扩大分类方法,在几个迭代中迅速提高效率。我们用3个和7个自由参数分别显示我们用于玩具模型和II 2HDM的方法的结果。本文使用的代码和指示可在网上公开查阅。