MLPF results on the simulated CLIC dataset

Trained models and evaluation results for the upcoming paper "Scalable neural network models and terascale
datasets for particle-flow reconstruction".

The archive contains the following subfolders:

• clusters_best_tuned_gnn_clic_v130
  • MLPF GNN model after hypertuning
  • the inputs are reconstructed tracks and Pandora clusters
  • the outputs are reconstructed PF candidates
  • trained on tt and qq v1.3.0 (1M events each)
•   hits
  • MLPF GNN model
  • inputs are reconstructed tracks and calorimeter hits
  • outputs are reconstructed PF candidates
  • trained on tt, qq and gun samples (K0L, gamma, pi+-, pi0, neutron, ele, mu) v1.2.0
  • training was restarted several times from previous checkpoints
• hypertuning
  • GNN and transformer model before and after hypertuning
• timing
  • scaling study of baseline PF with number of gun particles on CPU
  • scaling study of GNN model with number of input elements on GPU
• gpu_scaling
  • the scaling study of model training on multiple accelerator cards

The training dataset is available at

• Pata, Joosep, Wulff, Eric, Duarte, Javier, Mokhtar, Farouk, Zhang, Mengke, Girone, Maria, & Southwick, David. (2023). Simulated datasets for detector and particle flow reconstruction: CLIC detector (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8260741