A hybrid deep learning approach to vertexing
A hybrid deep learning approach to vertexing
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混合深度学习顶点方法
DOI：
10.1088/1742-6596/1525/1/012079
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Williams, Mike
中科院分区：
文献类型：
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作者：
Fang, Rui;Schreiner, Henry F;Sokoloff, Michael D;Weisser, Constantin;Williams, Mike
在LHC运行3期间，LHCb接收到的瞬时光度将从每个事件的1.1个预期可见主顶点（PV）增加到5.6个。为了应对这一挑战，LHCb探测器将进行升级，并将采用纯软件触发。这推动了对用于跟踪和重建的替代高度并行和GPU友好算法的兴趣增加。我们将提出一个新的原型算法的顶点在LHCb升级条件。我们使用自定义内核将稀疏的3D命中和跟踪空间转换为密集的1D数据集，然后应用深度学习技术来查找PV位置。通过使用几个卷积神经网络层在我们的内核上训练网络，我们已经实现了超过90%的效率，每个事件不超过0.2个假阳性（FP）。除了其物理性能之外，该算法还为1D卷积网络的可视化和研究提供了丰富的可能性。我们将讨论设计，性能和未来潜在的改进和研究领域，例如恢复完整3D顶点信息的可能方法。
During the LHC Run 3, the instantaneous luminosity received by LHCb will be increased going from 1.1 to 5.6 expected visible Primary Vertices (PVs) per event. To face this challenge, the LHCb detector will be upgraded and will adopt a purely software trigger. This has fueled increased interest in alternative highly-parallel and GPU friendly algorithms for tracking and reconstruction. We will present a novel prototype algorithm for vertexing in the LHCb upgrade conditions. We use a custom kernel to transform the sparse 3D space of hits and tracks into a dense 1D dataset, and then apply Deep Learning techniques to find PV locations. By training networks on our kernels using several Convolutional Neural Network layers, we have achieved better than 90% efficiency with no more than 0.2 False Positives (FPs) per event. Beyond its physics performance, this algorithm also provides a rich collection of possibilities for visualization and study of 1D convolutional networks. We will discuss the design, performance, and future potential areas of improvement and study, such as possible ways to recover the full 3D vertex information.