An Object Condensation Pipeline for Charged Particle Tracking at the High Luminosity LHC

TL;DR

This paper introduces an object condensation approach for charged particle tracking at the High Luminosity LHC, showing promising results and advantages over traditional edge classification methods, with an open-source implementation.

Contribution

It proposes a novel object condensation framework for particle tracking, outperforming edge classification in certain scenarios, and provides an extensible open-source tool for evaluation.

Findings

OC can recover tracks not found by EC methods

OC demonstrates promising results on the trackML dataset

Open-source implementation facilitates further research

Abstract

Recent work has demonstrated that graph neural networks (GNNs) trained for charged particle tracking can match the performance of traditional algorithms while improving scalability to prepare for the High Luminosity LHC experiment. Most approaches are based on the edge classification (EC) paradigm, wherein tracker hits are connected by edges, and a GNN is trained to prune edges, resulting in a collection of connected components representing tracks. These connected components are usually collected by a clustering algorithm and the resulting hit clusters are passed to downstream modules that may assess track quality or fit track parameters. In this work, we consider an alternative approach based on object condensation (OC), a multi-objective learning framework designed to cluster points belonging to an arbitrary number of objects, in this context tracks, and regress the properties of each object. We demonstrate that OC shows very promising results when applied to the pixel detector of the trackML dataset and can, in some cases, recover tracks that are not reconstructable when relying on the output of an EC alone. The results have been obtained with a modular and extensible open-source implementation that allows us to efficiently train and evaluate the performance of various OC architectures and related approaches.