Speaker
Description
The ATLAS calorimeter system measures the energy of particles. These are large volumes segmented into many individual cells, each recording a small deposit of energy as a particle passes through. The cell-by-cell structure has a natural geometric and topological form well suited to graph neural networks (GNNs), a class of machine learning models designed to learn from data with irregular, connected structure rather than a fixed grid. In ATL-PHYS-PUB-2022-040, the ATLAS Collaboration showed that by using calorimeter information alone, GNNs can accurately perform calibration on topoclusters originating from both neutral and charged pions.
This presentation extends that approach to topoclusters arising from more complex event topologies, including neutral hadrons and dijets. It further applies the technique to partially-subtracted clusters, which arise in ParticleFlow, a reconstruction strategy that combines calorimeter and tracking-detector information to improve energy measurement by removing the contribution of charged particles already measured elsewhere. The resulting calibration scheme demonstrates improved energy resolution and linearity relative to the baseline local hadronic calibration across the studied topologies.