Confining dark sectors with pseudo-conformal dynamics can produce Soft Unclustered Energy Patterns, or SUEPs, at the Large Hadron Collider: the production of dark quarks in proton-proton collisions leading to a dark shower and the high-multiplicity production of dark hadrons. The final experimental signature is spherically-symmetric energy deposits by an anomalously large number of soft Standard Model particles with a transverse energy of a few hundred MeV. The dominant background for the SUEP search, if it gets produced via gluon-gluon fusion, is multi-jet QCD events. We have developed a deep learning-based Anomaly Detection technique to reject QCD jets and identify any anomalous signature, including SUEP, in real-time in the High-Level Trigger system of the Compact Muon Solenoid experiment at the Large Hadron Collider. A deep convolutional neural autoencoder network has been trained using QCD events by taking transverse energy deposits in the inner tracker, electromagnetic calorimeter, and hadron calorimeter sub-detectors as 3-channel image data. To tackle the biggest challenge of the task, due to the sparse nature of the data: only ~0.5% of the total ~300 k image pixels have non-zero values, a non-standard loss function, the inverse of the so-called Dice Loss, has been exploited. The trained autoencoder with learned spatial features of QCD jets can detect 40% of the SUEP events, with a QCD event mistagging rate as low as 2%. The model inference time has been measured using the Intel CoreTM i5-9600KF processor and found to be ~20 ms, which perfectly satisfies the High-Level Trigger system's latency of O(100) ms. Given the virtue of the unsupervised learning of the autoencoders, the trained model can be applied to any new physics model that predicts an experimental signature anomalous to QCD jets.

翻译：具有伪共形动力学的禁闭暗扇区可在大型强子对撞机中产生软无簇射能量模式(SUEP)：质子-质子碰撞中产生的暗夸克引发暗簇射，进而产生高多重性暗强子。最终实验特征表现为横动量数百MeV的异常大量软标准模型粒子所沉积的球对称能量分布。若SUEP通过胶子-胶子融合产生，其主要本底为多喷注QCD事件。我们开发了基于深度学习的异常检测技术，可在大型强子对撞机紧凑μ子螺线管实验的高阶触发系统中实时识别包括SUEP在内的任何异常特征。通过将内径迹探测器、电磁量能器和强子量能器子探测器的横向能量沉积作为三通道图像数据，利用QCD事件训练了深度卷积神经自编码器网络。针对数据稀疏性带来的最大挑战——约30万图像像素中仅~0.5%具有非零值，我们采用非标准损失函数——所谓的Dice损失的逆函数。该自编码器通过习得QCD喷注的空间特征，能够以2%的QCD事件误标率检测40%的SUEP事件。基于Intel Core i5-9600KF处理器测得模型推理时间约20毫秒，完美满足高阶触发系统百毫秒量级的延迟要求。得益于自编码器的无监督学习特性，该训练模型可适用于任何预测异常于QCD喷注实验特征的新物理模型。