A tagging algorithm to identify jets that are significantly displaced from the proton-proton (pp) collision region in the CMS detector at the LHC is presented. Displaced jets can arise from the decays of long-lived particles (LLPs), which are predicted by several theoretical extensions of the standard model. The tagger is a multiclass classifier based on a deep neural network, which is parameterised according to the proper decay length c τ ₀ of the LLP. A novel scheme is defined to reliably label jets from LLP decays for supervised learning. Samples of pp collision data, recorded by the CMS detector at a centre-of-mass energy of 13 TeV, and simulated events are used to train the neural network. Domain adaptation by backward propagation is performed to improve the simulation modelling of the jet class probability distributions observed in pp collision data. The potential performance of the tagger is demonstrated with a search for long-lived gluinos, a manifestation of split supersymmetric models. The tagger provides a rejection factor of 10 000 for jets from standard model processes, while maintaining an LLP jet tagging efficiency of 30%–80% for gluinos with 1 mm≤c τ ₀≤ 10 m. The expected coverage of the parameter space for split supersymmetry is presented.

提出了一种标记算法，用于识别在 LHC 的 CMS 探测器中明显偏离质子-质子 (pp) 碰撞区域的射流。置换射流可能来自长寿命粒子 (LLP) 的衰变，这是由标准模型的几个理论扩展预测的。标注器是基于深度神经网络的多类分类器，根据适当的衰减长度c τ _{0进行参数化}LLP的。定义了一种新颖的方案来可靠地标记 LLP 衰减中的射流，以进行监督学习。由 CMS 探测器以 13 TeV 的质心能量记录的 pp 碰撞数据样本和模拟事件用于训练神经网络。通过反向传播进行域自适应以改进在 pp 碰撞数据中观察到的喷射类概率分布的模拟建模。标记器的潜在性能通过寻找长寿命的 Gluinos 来证明，这是分裂超对称模型的一种表现。标记器为来自标准模型过程的喷射提供 10 000 的拒绝因子，同时对于 1 mm≤ c τ _{0的胶粘剂保持 30%–80% 的 LLP 喷射标记效率}≤ 10 米。给出了分裂超对称的参数空间的预期覆盖范围。