End-to-End Multi-Track Reconstruction using Graph Neural Networks at Belle II

Abstract

We present the study of an end-to-end multi-track reconstruction algorithm for the central drift chamber of the Belle II experiment at the SuperKEKB collider using Graph Neural Networks for an unknown number of particles. The algorithm uses detector hits as inputs without pre-filtering to simultaneously predict the number of track candidates in an event and their kinematic properties. In a second step, we cluster detector hits for each track candidate to pass to a track fitting algorithm. Using a realistic full detector simulation including beam-induced backgrounds and detector noise taken from actual collision data, we find significant improvements in track finding efficiencies for tracks in a variety of different event topologies compared to the existing baseline algorithm used in Belle II. For events with a hypothetical long-lived massive particle with a mass in the GeV-range decaying uniformly along its flight direction into two charged particles, the GNN achieves a combined track finding and fitting efficiency of 85.4% with a fake rate of 2.5%, compared to 52.2% and 4.1% for the baseline algorithm. This is the first end-to-end multi-track machine learning algorithm for a drift chamber detector that has been utilized in a realistic particle physics environment.

References in corpus (19)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• The Precision Monte Carlo Event Generator KK For Two-Fermion Final States In e+e- Collisions
• The Belle II Physics Book
• The Belle II Core Software
• Graph Neural Networks in Particle Physics
• Learning representations of irregular particle-detector geometry with distance-weighted graph networks
• A Novel Generic Framework for Track Fitting in Complex Detector Systems
• Invisible and displaced dark matter signatures at Belle II
• The Tracking Machine Learning challenge : Accuracy phase
• Track Finding at Belle II
• GENFIT - a Generic Track-Fitting Toolkit
• Long-lived Dark Higgs and Inelastic Dark Matter at Belle II
• Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data
• Belle II sensitivity to long-lived dark photons
• The Tracking Machine Learning challenge : Throughput phase
• Measurements of Beam Backgrounds in SuperKEKB Phase 2
• Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks
• Real-time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics
• Graph Neural Network-Based Track Finding in the LHCb Vertex Detector

Cited by in corpus (1)

• Kink Finder at Belle II