论文编号:
第一作者所在部门:
论文题目:	Exploring the standard model EFT in VH production with machine learning
论文题目英文:
作者:	Freitas, Felipe F.; Khosa, Charanjit K.; Sanz, Veronica
论文出处:
刊物名称:	PHYSICAL REVIEW D
年:	2019
卷:	100
期:	3
页:	35040
联系作者:
收录类别:
影响因子:
摘要:	In this paper we study the use of machine learning techniques to exploit kinematic information in VH, the production of a Higgs in association with a massive vector boson. We parametrize the effect of new physics in terms of the standard model effective field theory (SMEFT) framework. We find that the use of a shallow neural network allows us to dramatically increase the sensitivity to deviations in VH respect to previous estimates. We also discuss the relation between the usual measures of performance in machine learning, such as area under the curve or accuracy, with the more adept measure of Asimov significance. This relation is particularly relevant when parametrizing systematic uncertainties. Our results show the potential of incorporating machine learning techniques to the SMEFT studies using the current datasets.
英文摘要:
外单位作者单位:
备注: