Auto-encoders (AEs) have the potential to be effective and generic tools for new physics searches at colliders, requiring little to no model-dependent assumptions. New hypothetical physics signals can be considered anomalies that deviate from the well-known background processes generally expected to describe the whole dataset. We present a search formulated as an anomaly detection (AD) problem, using an AE to define a criterion to decide about the physics nature of an event. In this work, we perform an AD search for manifestations of a dark version of strong force using raw detector images, which are large and very sparse, without leveraging any physics-based pre-processing or assumption on the signals. We propose a dual-encoder design which can learn a compact latent space through conditioning. In the context of multiple AD metrics, we present a clear improvement over competitive baselines and prior approaches. It is the first time that an AE is shown to exhibit excellent discrimination against multiple dark shower models, illustrating the suitability of this method as a performant, model-independent algorithm to deploy, e.g., in the trigger stage of LHC experiments such as ATLAS and CMS.

翻译：自编码器（AE）有潜力成为对撞机上新物理搜索的有效通用工具，几乎不需要依赖模型假设。新的假设性物理信号可被视为偏离已知背景过程的异常，而这些背景通常被认为能描述整个数据集。我们提出了一种以异常检测（AD）问题形式呈现的搜索，利用自编码器定义判断事件物理性质的准则。在此研究中，我们使用原始探测器图像（这些图像规模大且极度稀疏）对暗性强力表现进行异常检测搜索，未利用任何基于物理的预处理或信号假设。我们提出了一种双编码器设计，该设计能通过条件化学习紧凑的潜空间。在多种异常检测指标下，我们的方法展现出对竞争基线和先前方法的显著改进。这是首次证明自编码器能对多种暗簇射模型展现出色的区分能力，说明该方法作为高性能、模型无关算法，适合部署于ATLAS和CMS等大型强子对撞机实验的触发阶段。