One of the challenges of high granularity calorimeters, such as that to be built to cover the endcap region in the CMS Phase-2 Upgrade for HL-LHC, is that the large number of channels causes a surge in the computing load when clustering numerous digitised energy deposits (hits) in the reconstruction stage. In this article, we propose a fast and fully-parallelizable density-based clustering algorithm, optimized for high occupancy scenarios, where the number of clusters is much larger than the average number of hits in a cluster. The algorithm uses a grid spatial index for fast querying of neighbours and its timing scales linearly with the number of hits within the range considered. We also show a comparison of the performance on CPU and GPU implementations, demonstrating the power of algorithmic parallelization in the coming era of heterogeneous computing in high energy physics.
@article{arxiv.2001.09761,
title = {CLUE: A Fast Parallel Clustering Algorithm for High Granularity Calorimeters in High Energy Physics},
author = {Marco Rovere and Ziheng Chen and Antonio Di Pilato and Felice Pantaleo and Chris Seez},
journal= {arXiv preprint arXiv:2001.09761},
year = {2020}
}