Confining dark sectors with pseudo-conformal dynamics can produce Soft Unclustered Energy Patterns (SUEP), 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 O(100) MeV. Assuming Yukawa-like couplings of the scalar portal state, the dominant production mode is gluon fusion, and the dominant background comes from 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. Due to the sparse nature of the data, only ~0.5% of the total ~300 k image pixels have non-zero values. To tackle this challenge, a non-standard loss function, the inverse of the so-called Dice Loss, is 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受限暗区可以在大型强子对撞机上产生软未聚类能量模式（SUEP）：质子-质子碰撞中产生的暗夸克引发暗簇射，进而大量产生暗强子。最终的实验特征表现为：大量横能量约为O(100) MeV的软标准模型粒子形成球对称的能量沉积，其数量异常庞大。假设标量门户态存在类汤川耦合，则主导产生模式为胶子融合，主要背景来源于多喷注QCD事例。我们开发了一种基于深度学习的异常检测技术，用于在大型强子对撞机紧凑μ子螺线管实验的高级触发系统中实时剔除QCD喷注并识别包括SUEP在内的任何异常信号。通过将内径迹探测器、电磁量能器和强子量能器的横能量沉积作为三通道图像数据，使用QCD事例训练了一个深度卷积神经自编码器网络。由于数据具有稀疏性，在总计约30万个图像像素中仅有约0.5%的像素具有非零值。为应对这一挑战，我们采用了一种非标准损失函数——即所谓Dice Loss的倒数。经过训练的自编码器已学习到QCD喷注的空间特征，能够以低至2%的QCD事例误标率检测出40%的SUEP事例。使用英特尔酷睿TM i5-9600KF处理器测得模型推理时间约为20毫秒，完全满足高级触发系统O(100)毫秒的延迟要求。凭借自编码器无监督学习的优势，训练好的模型可应用于任何预测到不同于QCD喷注的实验特征的新物理模型。