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

TL;DR

This paper introduces a graph-based clustering algorithm for the LHCb electromagnetic calorimeter that significantly reduces computational time while maintaining accuracy, enhancing data processing efficiency for high-rate particle physics experiments.

Contribution

The paper presents a novel graph clustering algorithm for calorimeter reconstruction that outperforms previous methods in speed without sacrificing efficiency or resolution.

Findings

65.4% reduction in computational time

Maintains equivalent efficiency and resolution

Effective within the LHCb simulation framework

Abstract

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.