开发用于识别和定位主顶点的混合深度学习算法的进展
Progress in developing a hybrid deep learning algorithm for identifying and locating primary vertices
摘要：
在LHC实验中，质子-质子碰撞点的位置被称为主顶点（PV）。用于识别和定位这些的混合深度学习算法的初步结果，针对LHCb的Run 3化身，已在2019年和2020年的会议上进行了描述。过去一年，我们在各种相关领域取得了重大进展。使用两个较新的核密度估计器（KDE）作为输入特征集提高了模型的保真度，就像使用完整的LHCb模拟而不是最初（并且仍然）用于开发模型的“玩具蒙特卡洛”一样。我们还构建了一个深度学习模型，用于从跟踪信息中计算KDE。将tracks-to-KDE模型连接到用于查找PV的KDE-to-hists模型提供了一个概念验证，即单个深度学习模型可以使用跟踪信息以高效率和高保真度查找PV。我们已经系统地研究了各种模型，以了解其架构中的变化如何影响性能。虽然这里报告的研究是特定于LHCb的几何形状和操作条件，结果表明，ATLAS和CMS实验可以使用相同的方法。
英文摘要：
The locations of proton-proton collision points in LHC experiments are called primary vertices (PVs). Preliminary results of a hybrid deep learning algorithm for identifying and locating these, targeting the Run 3 incarnation of LHCb, have been described at conferences in 2019 and 2020. In the past year we have made significant progress in a variety of related areas. Using two newer Kernel Density Estimators (KDEs) as input feature sets improves the fidelity of the models, as does using full LHCb simulation rather than the "toy Monte Carlo" originally (and still) used to develop models. We have also built a deep learning model to calculate the KDEs from track information. Connecting a tracks-to-KDE model to a KDE-to-hists model used to find PVs provides a proof-of-concept that a single deep learning model can use track information to find PVs with high efficiency and high fidelity. We have studied a variety of models systematically to understand how variations in their architectures affect performance. While the studies reported here are specific to the LHCb geometry and operating conditions, the results suggest that the same approach could be used by the ATLAS and CMS experiments.
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