Using Graph Neural Networks for hadronic clustering and to reduce beam background in the Belle~II electromagnetic calorimeter

TL;DR

This paper explores the use of graph neural networks to improve hadronic clustering and reduce beam background in the Belle II electromagnetic calorimeter, addressing challenges from increased background and complex hadronic interactions.

Contribution

It introduces a novel GNN-based method to identify and remove unwanted energy depositions before clustering, enhancing detector performance.

Findings

GNN effectively distinguishes real hadronic signals from background noise.

The method improves photon energy resolution and reduces fake clusters.

GNN addresses the irregular energy depositions caused by hadronic interactions.

Abstract

The Belle~II electromagnetic calorimeter consists of 8376 CsI(Tl) scintillation crystals and is not only used for measuring electromagnetic particles but also for identifying and determining the position of hadrons, particularly neutral\textbf{} hadrons. Recent data-taking periods have presented challenges for the current clustering method: Firstly, the record-breaking luminosities achieved by the SuperKEKB accelerator have increased background rates, leading to a higher number of crystals with energy depositions, and an overall increase in the total energy measured in the calorimeter. This resulted in poorer photon energy resolution and the reconstruction of more fake photon clusters. Secondly, challenges arise from the nature of hadronic interactions. In contrast to $\gamma$ and $e^{\pm}$, hadrons interacting in the calorimeter result in irregular, sometimes even disconnected energy depositions. These clusters can be misinterpreted as photon clusters, thereby reducing the position resolution of neutral hadrons or causing a complete misidentification of the hadron. Graph neural networks offer a promising solution to both challenges. By representing only crystals with an energy measurement as nodes, graphs capture the sparsity of the input. Using message-passing layers that learn the graph edges also helps to address the asymmetric sensor layout of Belle~II's ECL. In these proceedings, we will present a novel approach to identify the challenges in the detector simulation. Using this information, we train a Graph Neural Network to identify and remove unwanted depositions abefore clustering.