论文标题
探索机器学习协助的参数空间
Exploration of Parameter Spaces Assisted by Machine Learning
论文作者
论文摘要
我们演示了两个通过回归和分类辅助的采样程序。主要目的是使用神经网络来提出可能感兴趣的区域内部的观点，从而减少了耗时计算的评估数量。我们将这种方法的结果与其他抽样方法的结果（即马尔可夫链蒙特卡洛和多节）进行了比较，获得的结果范围从相当相似，与可以说的更好。特别是，我们使用一种提升技术来增强分类器方法，从而在一些迭代中迅速提高效率。我们分别使用3和7个自由参数显示了应用于玩具模型和II型2HDM的方法的结果。本文使用的代码和说明在网络上公开可用。
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.
