论文标题
图形聚类：一种基于图的聚类算法，用于LHCB中电磁量热仪
Graph Clustering: a graph-based clustering algorithm for the electromagnetic calorimeter in LHCb
论文作者
论文摘要
LHCB实验的最新升级将数据处理速率提高到40 tbit/s。在整个重建序列中，最耗时的算法之一是量热计重建。它旨在执行属于同一粒子的检测器的读数单元的聚类，以测量其能量和位置。本文提出了一种用于量热计重建的新算法，该算法利用图数据结构来优化聚类过程，这将表示为图形群集。在计算时间平均而言，具有同等效率和分辨率的计算时间方面，它的表现超过了先前使用的方法$ 65.4 \％$。本文详细介绍了图形聚类方法的实现，并使用仿真数据在LHCB框架内的性能结果。
The recent upgrade of the LHCb experiment pushes data processing rates up to 40 Tbit/s. Out of the whole reconstruction sequence, one of the most time consuming algorithms is the calorimeter reconstruction. It aims at performing a clustering of the readout cells from the detector that belong to the same particle in order to measure its energy and position. This article presents a new algorithm for the calorimeter reconstruction that makes use of graph data structures to optimise the clustering process, that will be denoted Graph Clustering. It outperforms the previously used method by $65.4\%$ in terms of computational time on average, with an equivalent efficiency and resolution. The implementation of the Graph Clustering method is detailed in this article, together with its performance results inside the LHCb framework using simulation data.
