Improving clustering methods for the FoCal detector
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The Forward Calorimeter, FoCal for short, is a proposed detector for the ALICE project at CERN. It is an electromagnetic calorimeter with high position granularity layers allowing separation of nearby particles like the decay photons of the neutral pion. Monte Carlo simulations were used to simulate the detection of these particles and a clustering algorithm is used to reconstruct the particle locations and energies based on the detector's response. The methods used in the clustering algorithm that deal with separation of particles that are close together will be discussed in this thesis. Modifications of the algorithm are introduced which slightly improve the efficiency from 85.8% to 87.4%.