标题： 模型无关的寻找质子-质子对撞中具有反常喷注亚结构的二喷注共振的搜索，碰撞能量$\sqrt{s}$ = 13 TeV 显示英文标题

标题： Model-agnostic search for dijet resonances with anomalous jet substructure in proton-proton collisions at $\sqrt{s}$ = 13 TeV

摘要： 本文提出了一种与模型无关的方法，在双喷终态中搜索1.8-6 TeV质量范围内的窄共振。 假定信号会产生具有非典型子结构的喷注，这些子结构不同于由轻夸克或胶子引发的喷注，并且假设最少。 通过利用多变量机器学习方法选择具有异常子结构的喷注来获得搜索区域。 一组互补的异常检测方法——基于无监督、弱监督和半监督算法——被用来最大化对未知新物理特征的敏感性。 这些算法应用于CMS实验在LHC收集的数据，对应于积分为138 fb$^{-1}$的积分亮度，在质心能量为13 TeV的情况下。 未观察到超出背景预期的显著超额。 排除限值被推导出，针对不同共振质量、喷注质量和喷注子结构的基准信号模型的产生截面。 其中许多特征以前未曾被搜索过，因此对于相应的基准模型的一些限制是首次报告。 当与基准包含和基于子结构的搜索策略相比时，异常检测方法被发现显著提高了对多种模型的敏感性。 显示英文摘要

摘要： This paper presents a model-agnostic search for narrow resonances in the dijet final state in the mass range 1.8-6 TeV. The signal is assumed to produce jets with substructure atypical of jets initiated by light quarks or gluons, with minimal additional assumptions. Search regions are obtained by utilizing multivariate machine-learning methods to select jets with anomalous substructure. A collection of complementary anomaly detection methods - based on unsupervised, weakly supervised, and semisupervised algorithms - are used in order to maximize the sensitivity to unknown new physics signatures. These algorithms are applied to data corresponding to an integrated luminosity of 138 fb$^{-1}$, recorded by the CMS experiment at the LHC, at a center-of-mass energy of 13 TeV. No significant excesses above background expectations are seen. Exclusion limits are derived on the production cross section of benchmark signal models varying in resonance mass, jet mass, and jet substructure. Many of these signatures have not been previously sought, making several of the limits reported on the corresponding benchmark models the first ever. When compared to benchmark inclusive and substructure-based search strategies, the anomaly detection methods are found to significantly enhance the sensitivity to a variety of models.