Orateur
Description
The reconstruction of particle signals relies on local reconstrution, which involves clustering of granular hits within detector subsystems, followed by global reconstruction, combining signals across detector subsystems for a high-level particle representation of the event. Calorimeter clustering is a local reconstruction method that aims to segment calorimeter hits according to their particle origin. Recently, in light of the future high-granularity detector configurations, considerable progress has been made in disentangling overlapping showers in highly granular detectors using machine learning. Once clusters and tracks are reconstructed, particle-flow algorithms combine the information globally across the detector for an optimized particle-level reconstruction. Machine learning approaches have recently been demonstrated to offer comparable performance to heuristic particle flow algorithms, while potentially allowing for native deployment on heterogeneous platforms. I will give a summary of the progress towards ML-based calorimeter reconstruction and particle flow.
