26TH INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY & NUCLEAR PHYSICS (CHEP2023)

Improved Clustering in the Belle II Electromagnetic Calorimeter with Graph Neural Networks

May 11, 2023, 2:45 PM

15m

Hampton Roads VII (Norfolk Waterside Marriott)

Oral Track 9 - Artificial Intelligence and Machine Learning Track 9 - Artificial Intelligence and Machine Learning

Speaker

Haide, Isabel (Karlsruhe Institute of Technology)

Description

For the Belle II experiment, the electromagnetic calorimeter (ECL) plays a crucial role in both the trigger decisions and the offline analysis.
The performance of existing clustering algorithms degrades with rising backgrounds that are expected for the increasing luminosity in Belle II. In offline analyses, this mostly impacts the energy resolution for low-energy photons; for the trigger, it's most challenging to keep a high efficiency with low fake rates, especially for low-energy or overlapping clusters.
In the case of offline reconstruction, we developed a soft clustering algorithm based on graph neural networks (GNN) that improves the energy resolution for photons. We report a significant improvement over the current Belle II algorithm with better resolution for low-energy photons, particularly for the increased background rates expected with higher instantaneous luminosity.
For online reconstruction, we implemented a resource-efficient GNN-based algorithm for object condensation that is able to detect an unknown number of clusters and their respective position and energy inside the calorimeter, despite the presence of background energy and the irregular geometry of the ECL. This is compared to the current trigger algorithm in Belle II and could provide an improved trigger decision, especially for higher background rates.

Presentation materials

IsabelHaide_CHEP_Talk_110523.pdf