Strongly interacting dark sectors predict novel LHC signatures such as semi-visible jets resulting from dark showers that contain both stable and unstable dark mesons. Distinguishing such semi-visible jets from large QCD backgrounds is difficult and constitutes an exciting challenge for jet classification. In this article we explore the potential of supervised deep neural networks to identify semi-visible jets. We show that dynamic graph convolutional neural networks operating on so-called particle clouds outperform convolutional neural networks analysing jet images as well as other neural networks based on Lorentz vectors. We investigate how the performance depends on the properties of the dark shower and discuss training on mixed samples as a strategy to reduce model dependence. By modifying an existing mono-jet analysis we show that LHC sensitivity to dark sectors can be enhanced by more than an order of magnitude by using the dynamic graph network as a dark shower tagger.

中文翻译：

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投放图表网以捕捉黑暗的阵雨

强烈相互作用的暗区预示着新的LHC信号，例如包含稳定和不稳定暗介子的暗雨产生的半可见射流。很难从大型QCD背景中区分出这种半可见的喷射器，这对喷射器的分类构成了令人兴奋的挑战。在本文中，我们探索了监督型深度神经网络识别半可见射流的潜力。我们展示了在所谓的粒子云上运行的动态图卷积神经网络优于分析喷气图像的卷积神经网络以及其他基于Lorentz矢量的神经网络。我们研究了性能如何取决于深色淋浴的性能，并讨论了对混合样本的训练以减少模型依赖性的策略。