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The recent upgrade of the LHCb experiment pushes data processing rates up to40 Tbit/s. Out of the whole reconstruction sequence, one of the most timeconsuming algorithms is the calorimeter reconstruction. It aims at performing aclustering of the readout cells from the detector that belong to the sameparticle in order to measure its energy and position. This article presents anew algorithm for the calorimeter reconstruction that makes use of graph datastructures to optimise the clustering process, that will be denoted GraphClustering. It outperforms the previously used method by $65.4\%$ in terms ofcomputational 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 usingsimulation data.

Graph Clustering: a graph-based clustering algorithm for the electromagnetic calorimeter in LHCb