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.

使用自动编码器进行异常检测，并发现自动编码器可以较好地区分多个黑暗粒子模型，因此这种方法适用于高能物理实验中的触发阶段。