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


2026 Q1

  • Progress on optimized GNN model
    • Study impact of pileup on the training of the GNN-based reconstruction algorithm
    • GPU Memory profiling of GNN model trained on dataset with 5x more hits
    • Integration of CUDA kernels from FastGraphCompute to speed up KNN and object condensation loss
  • Working on training of GNN model with 200 pileup simulation
    • First HGCAL simulation with FineCalo + merging algorithm in CMSSW_15_1_0
    • Simulated 10 single-electron events + 30 PU interactions from minimum bias events
    • Implementation of custom NANO step to save true clusters containing SimHits from primary interaction and pileup
    • At 30 PU, the average number of RecHits is ~60k, with ~8k true clusters
    • Study a dedicated implementation of the merging algorithm which is compatible with pileup
    • Study a dedicated pileup mixing library to save the event history for minimum bias events in the merging
  • Study new alternative ML architectures for HGCAL reconstruction
    • Masked transformers to reduce the quadratic complexity of self-attention
    • Generated 0 PU dataset with RecHits and TICL objects: 2D LayerClusters, 3D Tracksters and TICL Candidates
    • Computing the incidence matrix between RecHits and true particles using sparse tensor representation
    • First proof-of-concept training using recHits features as input: incidence matrix regression and regression of cluster properties


2025 Q4

  • Progress
    • Study energy, position and time resolution of the GNN clustering using simulated photons, pions and tau leptons
    • Study issue in time resolution in CMSSW_15_X: change in the timing simulation with respect to CMSSW_11_X
    • Finalize publication on performance metrics of GNN clustering with 0 pileup simulation
    • Study the impact of increased pileup on the GNN training
    • Memory profiling of the training with a dataset including ~250k reconstructed hits
    • Working on pileup simulation: generate hard process alongside minimum bias events


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
    • Ported the GNN training to Pytorch 2.6.0 and CUDA 12.4
    • Study memory profiling of the model on GPU


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