论文标题
使用深层神经网络搜索$ b \ b $ b $最终状态中的Mono-Higgs信号
Search for Mono-Higgs Signals in $b\bar b$ Final States Using Deep Neural Networks
论文作者
论文摘要
我们研究了在涉及标准模型的玻色子腐烂的新物理学场景中出现的单孔标志，该场景使用混合深神经网络腐烂到底部的夸克对。我们使用多层感知器来分析运动学可观察物并优化信噪比的歧视。从具有不同颜色电荷的重颗粒的衰减中出现的硬射流的全局颜色流量结构对于单张单基金签名至关重要。将信号和背景的不同颜色流结构嵌入到构造的图像中后，我们使用卷积神经网络来分析后者。具体而言，该方法最初将单型数据作为输入，从而消除了宝贵的多源和多尺度信息。然后，我们讨论混合深神经网络的一般体系结构，该网络支持混合输入数据。与单个输入深神经网络（如多层感知器或卷积神经网络）相比，混合深度神经网络在特征提取方面提供了更高的能力，因此在信号与背景分类性能中提供了更高的能力。我们为高光度大强壮的强子对撞机提供参考结果。
We study mono-Higgs signatures emerging in an illustrative new physics scenario involving Standard Model Higgs boson decays to bottom quark pairs using Hybrid Deep Neural Networks. We use a Multi-Layer Perceptron to analyze the kinematic observables and optimize the signal-to-background discrimination. The global color flow structure of hard jets emerging from the decay of heavy particles with different color charges is crucial to single out the mono-Higgs signature. Upon embedding the different color flow structures for signal and backgrounds into constructed images, we use a Convolution Neural Network to analyze the latter. Specifically, the approach takes initially a mono-type data as input, frittering away invaluable multi-source and multi-scale information. We then discuss a general architecture of Hybrid Deep Neural Networks that supports instead mixed input data. In comparison with single input Deep Neural Networks, like MultiLayers Perceptron or Convolution Neural Network, the Hybrid Deep Neural Networks provide higher capacity in feature extraction and thus in signal vs background classification performance. We provide reference results for the case of the High-Luminosity Large Hadron Collider.
