SMuK 2023 – wissenschaftliches Programm

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T: Fachverband Teilchenphysik

T 72: Exp. Methods II

T 72.2: Vortrag

Mittwoch, 22. März 2023, 16:05–16:20, POT/0106

Graph Neural Network based Track Finding in the Central Drift Chamber at Belle II — •Lea Reuter, Philipp Dorwath, Torben Ferber, and Slavomira Stefkova — Institute of Experimental Particle Physics (ETP), Karlsruhe Institute of Technology (KIT)

In many new physics extensions of the Standard Model, new mediator particles may decay into charged particles leaving a unique signature of a displaced vertex and charged tracks. These displaced decay products are an important signature in searches for dark sector candidates in collider experiments. The current Belle II trigger algorithm is not designed for events with displaced vertices and therefore insufficient to detect these events. Traditional tracking algorithms scale poorly with the high beam-background, which is expected to increase significantly in the upcoming data-taking of the Belle II experiment.

Therefore, we develop a Graph Neural Network (GNN) based approach to find particle tracks and displaced vertices in the Central Drift Chamber of Belle II, where we realize track measurements using a graph representation of detector hits. We use GNN-based object condensation for track finding to identify the varying number of tracks per event. The goal of this project is to improve the track finding for Belle II. Furthermore, we also implement track fitting simultaneously to the track finding, to investigate if this GNN approach can also be used in real-time application in the level 1 trigger system.