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Update progress status 2025 Q4 for Matteo Marchegiani #155

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1 change: 1 addition & 0 deletions Gemfile.lock
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Original file line number Diff line number Diff line change
Expand Up @@ -77,6 +77,7 @@ GEM
rb-fsevent (~> 0.10, >= 0.10.3)
rb-inotify (~> 0.9, >= 0.9.10)
mercenary (0.4.0)
mini_portile2 (2.8.9)
minima (2.5.1)
jekyll (>= 3.5, < 5.0)
jekyll-feed (~> 0.9)
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49 changes: 49 additions & 0 deletions pages/postdocs/postdoc-matteomarchegiani.md
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Expand Up @@ -20,13 +20,60 @@ project_goal: >
proposal: /assets/pdfs/Matteo-Marchegiani_proposal_2025.pdf

presentations:
- title: Maskformers for offline reconstruction
date: February 10, 2026
url: https://indico.cern.ch/event/1649883/contributions/6935005/attachments/3217627/5732543/26.02.10_ML4reco_maskformers_offline.pdf
meeting: ML4RECO
meetingurl: https://indico.cern.ch/event/1649883/
- title: Studies on time resolution in GNN-based reco
date: December 3, 2025
url: https://indico.cern.ch/event/1617166/contributions/6819529/attachments/3185911/5668415/hgcal-dpg_gnn-time-resolution.pdf
meeting: HGCAL DPG
meetingurl: https://indico.cern.ch/event/1617166/
- title: Studies on time resolution with the latest HGCAL GNN model
date: December 2, 2025
url: https://indico.cern.ch/event/1615761/contributions/6818543/attachments/3184951/5666518/ml4reco_gnn-time-resolution-update.pdf
meeting: ML4RECO
meetingurl: https://indico.cern.ch/event/1615761/
- title: "Update on performance of the latest HGCAL GNN model"
date: "November 18, 2025"
url: https://indico.cern.ch/event/1612036/contributions/6792820/attachments/3176426/5649043/ml4reco_gnn-based-particle-reconstruction_update.pdf
meeting: ML4RECO
meetingurl: https://indico.cern.ch/event/1612036/
- title: "ML4RECO: GNN and Transformer Based HGCAL Reconstruction"
date: "October 13, 2025"
url: https://indico.cern.ch/event/1593611/contributions/6737584/attachments/3153545/5600652/25.10.13_MLG_TownHall_HGCAL_reco.pdf
meeting: CMS Machine Learning Town Hall
meetingurl: https://indico.cern.ch/event/1593611/contributions/6737584/
- title: Single-particle energy resolution with latest GNN model
date: "September 17, 2025"
url: https://indico.cern.ch/event/1587360/contributions/6689475/attachments/3137672/5567817/ml4reco-gnn-new-training-physics-metrics-update.pdf
meeting: ML4RECO
meetingurl: https://indico.cern.ch/event/1587360/
- title: Updated GNN training after bugfixes in CMSSW 15_0_X
date: "August 27, 2025"
url: https://indico.cern.ch/event/1579980/contributions/6658969/attachments/3124101/5540514/ml4reco-gnn-finecalo-simtrack-error-fix-training-update.pdf
meeting: ML4RECO
meetingurl: https://indico.cern.ch/event/1579980/
- title: FineCalo SimTrack Reconstruction Error
date: "July 16, 2025"
url: https://indico.cern.ch/event/1569687/contributions/6612371/attachments/3105373/5503441/ml4reco-gnn-finecalo-simtrack-error.pdf
meeting: ML4RECO
meetingurl: https://indico.cern.ch/event/1569687/

current_status: >
<br>
<b>2025 Q4 </b>
<br>

* 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

<br>
<b>2025 Q3 </b>
<br>
Expand All @@ -35,5 +82,7 @@ current_status: >
* 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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