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.

中文翻译：

---

在质子-质子碰撞中，以模型不可知的方式搜索具有异常射流子结构的二喷流共振 ...

本文提出了一种与模型无关的搜索，用于在质量范围为 1.8-6 TeV 的 dijet 最终状态下出现窄共振。假设该信号产生具有非典型亚结构的射流，这些射流的子结构与由轻夸克或胶子引发的射流不同，并且附加假设最少。搜索区域是通过利用多变量机器学习方法来选择具有异常子结构的喷流而获得的。使用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），以最大限度地提高对未知新物理特征的灵敏度。这些算法应用于对应于 138 fb-1 积分光度的数据，该数据由 LHC 的 CMS 实验记录，质心能量为 13 TeV。没有发现超出背景预期的重大超额。排除限值是根据基准信号模型的产生横截面得出的，这些模型的谐振质量、射流质量和射流子结构各不相同。其中许多签名以前从未被寻求过，这使得相应基准模型上报告的几个限制成为有史以来第一次。与基准包含和基于子结构的搜索策略相比，发现异常检测方法显著提高了对各种模型的敏感性。