论文标题
铸造图网以吸引黑暗淋浴
Casting a graph net to catch dark showers
论文作者
论文摘要
强烈相互作用的黑暗扇区预测了新型LHC特征，例如由含有稳定和不稳定的黑膜的黑暗阵雨产生的半透明喷射。将这种半可见喷气机与大QCD背景区分开是困难的，这是喷气式分类的令人兴奋的挑战。在本文中，我们探讨了有监督的深神经网络的潜力，以识别半可见喷气机。我们表明，在所谓的粒子云上运行的动态图卷积神经网络优于卷积神经网络，分析了基于Lorentz载体的JET图像以及其他神经网络。我们研究了性能如何取决于黑淋浴的特性，并讨论了对混合样品的培训，以减少模型依赖性的策略。通过修改现有的单射流分析，我们表明，通过将动态图网络用作深色淋浴标记器，可以通过将LHC对黑暗扇区的敏感性超过一个数量级来增强。
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.
