USCMS Researcher: Matteo Marchegiani



Postdoc dates: Jul 2025 - Jun 2026

Home Institution: Carnegie Mellon University


Project: GNN-based End-to-End Reconstruction in the CMS Phase 2 High-Granularity Calorimeter

The goal of the project is to develop new machine learning based algorithms for fast and efficient reconstruction of the High-Granularity Calorimeter at the High-Luminosity LHC.

More information: My project proposal

Mentors:

  • Matteo Cremonesi - (Carnegie Mellon University)

Presentations
Current Status


2025 Q3

  • Progress
    • Learned how to train the GNN employing GravConv layers in combination with the object condensation loss for the reconstruction of energy clusters in the HGCAL
    • Studied the performance and energy resolution of the GNN-based reconstruction in zero-pileup environments, considering simulated photons, pions and tau leptons
    • Ported the training datasets production to CMSSW_15_1_X, making use of the FineCalo simulation


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