Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb^-1 at √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.

中文翻译：

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一种新的魅力射流识别校准方法已通过 √s = 13 TeV 的质子-质子碰撞事件进行验证

大型强子对撞机的许多测量需要有效识别重味喷流，即源自底夸克 (b) 或魅力 (c) 夸克的喷流。概述了用于识别 c 射流的算法，并提出了一种校准它们的新方法。当算法应用于不同口味的喷气机时，这种新方法会调整获得的输出的整个分布。它基于迭代方法，利用三个不同的控制区域，这些区域富含 b 射流、c 射流或淡味和胶子射流。结果以使用质子-质子碰撞数据评估的校正因子的形式呈现，综合光度为 41.5 fb ^-1在√= 13 TeV，由 CMS 实验于 2017 年收集。通过将测量的校正因子应用于模拟数据集并检查调整后的模拟与碰撞数据之间的一致性来测试该方法的封闭性。此外，通过在模拟各种错误建模条件的伪数据上测试该方法来执行验证。校准结果可以使用重味识别算法输出的完整分布，例如作为机器学习模型的输入。因此，它们有望提高未来物理分析的灵敏度。