比较和改进用于识别和定位初级顶点的混合深度学习算法
Comparing and improving hybrid deep learning algorithms for identifying and locating primary vertices
AI总结：
本文提出一种使用径迹参数替代手工计算KDEs作为输入特征的高性能混合深度学习算法，用于在LHCb中识别和定位初级顶点，解决了计算缓慢问题，为在实时触发系统中部署DNN推理引擎开辟了道路。
摘要：
使用深度神经网络识别和定位LHCb中的质子-质子碰撞点（即初级顶点）已被研究数年。初步结果表明，与标准启发式方法相比，混合深度学习算法能够实现相似或更好的物理性能。先前研究的架构直接依赖手工计算的核密度估计器（KDEs）作为输入特征。计算这些KDEs速度缓慢，使得在实验的实时分析（触发）系统中使用DNN推理引擎变得困难。在此，我们展示了一种高性能混合深度学习算法的最新结果，该算法使用径迹参数而非KDEs作为输入特征，为在实时触发系统中部署开辟了道路。
英文摘要：
Using deep neural networks to identify and locate proton-proton collision points, or primary vertices, in LHCb has been studied for several years. Preliminary results demonstrated the ability for a hybrid deep learning algorithm to achieve similar or better physics performances compared to standard heuristic approaches. The previously studied architectures relied directly on hand-calculated Kernel Density Estimators (KDEs) as input features. Calculating these KDEs was slow, making use of the DNN inference engines in the experiment's real-time analysis (trigger) system problematic. Here we present recent results from a high-performance hybrid deep learning algorithm that uses track parameters as input features rather than KDEs, opening the path to deployment in the real-time trigger system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
