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。

使用条件双自编码器触发暗淋浴

Triggering Dark Showers with Conditional Dual Auto-Encoders

Using variational autoencoders trained on known physics processes, we develop
a one-sided threshold test to isolate previously unseen processes as outlier
events. Since the autoencoder training does not depend on any specific new
physics signature, the proposed procedure doesn't make specific assumptions on
the nature of new physics. An event selection based on this algorithm would be
complementary to classic LHC searches, typically based on model-dependent
hypothesis testing. Such an algorithm would deliver a list of anomalous events,
that the experimental collaborations could further scrutinize and even release
as a catalog, similarly to what is typically done in other scientific domains.
Event topologies repeating in this dataset could inspire new-physics model
building and new experimental searches. Running in the trigger system of the
LHC experiments, such an application could identify anomalous events that would
be otherwise lost, extending the scientific reach of the LHC.

使用物理过程训练的变分自编码器，开发了一种单边阈值测试，以隔离先前未见的过程作为异常事件，并且这种算法可以识别丢失的异常事件，扩展了 LHC 的科学范围。