# Model Card for mlpf-clic-clusters-v2.2.0 This model reconstructs particles in a detector, based on the tracks and calorimeter clusters recorded by the detector. ## Model Details The performance is measured with respect to generator-level jets and MET computed from Pythia particles, i.e. the truth-level jets and MET. The primary difference with respect to v2.1.0 is the inclusion of the sqrt(pt) weight in the pT and energy loss term.
Jet performance ttbar jet resolution qq jet resolution ttbar jet resolution
MET performance ttbar MET resolution qq MET resolution ttbar MET resolution
### Model Description - **Developed by:** Joosep Pata, Eric Wulff, Farouk Mokhtar, Mengke Zhang, David Southwick, Maria Girone, David Southwick, Javier Duarte, Michael Kagan - **Model type:** transformer - **License:** Apache License ### Model Sources - **Repository:** https://github.com/jpata/particleflow/releases/tag/v2.2.0 ## Uses ### Direct Use This model may be used to study the physics and computational performance on ML-based reconstruction in simulation. ### Out-of-Scope Use This model is not intended for physics measurements on real data. ## Bias, Risks, and Limitations The model has only been trained on simulation data and has not been validated against real data. The model has not been peer reviewed or published in a peer-reviewed journal. ## How to Get Started with the Model Use the code below to get started with the model. ``` #get the code git clone https://github.com/jpata/particleflow cd particleflow git checkout v2.2.0 #get the models git clone https://huggingface.co/jpata/particleflow models ``` ## Training Details Trained on 1x A100 for 5 epochs over ~6 days. The training was continued from a checkpoint due to a runtime limit. ### Training Data The following datasets were used: ``` 4.7G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_qq_pf/1/2.5.0 4.8G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_qq_pf/2/2.5.0 4.7G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_qq_pf/3/2.5.0 4.7G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_qq_pf/4/2.5.0 4.7G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_qq_pf/5/2.5.0 4.7G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_qq_pf/6/2.5.0 4.7G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_qq_pf/7/2.5.0 4.7G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_qq_pf/8/2.5.0 4.7G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_qq_pf/9/2.5.0 4.8G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_qq_pf/10/2.5.0 9.3G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ttbar_pf/1/2.5.0 9.3G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ttbar_pf/2/2.5.0 9.3G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ttbar_pf/3/2.5.0 9.3G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ttbar_pf/4/2.5.0 9.3G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ttbar_pf/5/2.5.0 9.3G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ttbar_pf/6/2.5.0 9.3G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ttbar_pf/7/2.5.0 9.3G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ttbar_pf/8/2.5.0 9.3G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ttbar_pf/9/2.5.0 9.3G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ttbar_pf/10/2.5.0 7.4G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ww_fullhad_pf/1/2.5.0 7.4G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ww_fullhad_pf/2/2.5.0 7.4G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ww_fullhad_pf/3/2.5.0 7.4G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ww_fullhad_pf/4/2.5.0 7.4G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ww_fullhad_pf/5/2.5.0 7.4G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ww_fullhad_pf/6/2.5.0 7.4G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ww_fullhad_pf/7/2.5.0 7.4G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ww_fullhad_pf/8/2.5.0 7.4G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ww_fullhad_pf/9/2.5.0 7.4G /scratch/persistent/joosep/tensorflow_datasets/clic_edm_ww_fullhad_pf/10/2.5.0 ``` The datasets were generated using Key4HEP with the following scripts: - https://github.com/HEP-KBFI/key4hep-sim/releases/tag/v1.1.0 - https://github.com/HEP-KBFI/key4hep-sim/blob/v1.1.0/clic/run_sim.sh ## Training Procedure ```bash #!/bin/bash #SBATCH --partition gpu #SBATCH --gres gpu:a100:1 #SBATCH --mem-per-gpu 250G #SBATCH -o logs/slurm-%x-%j-%N.out IMG=/home/software/singularity/pytorch.simg:2024-12-03 cd ~/particleflow ulimit -n 100000 singularity exec -B /scratch/persistent --nv \ --env PYTHONPATH=`pwd` \ --env KERAS_BACKEND=torch \ $IMG python3 mlpf/pipeline.py --gpus 1 \ --data-dir /scratch/persistent/joosep/tensorflow_datasets --config parameters/pytorch/pyg-clic.yaml \ --train --conv-type attention \ --gpu-batch-multiplier 256 --checkpoint-freq 1 --num-workers 8 --prefetch-factor 100 --comet --ntest 2000 --test-datasets clic_edm_qq_pf ``` ## Evaluation ```bash #!/bin/bash #SBATCH --partition gpu #SBATCH --gres gpu:a100-mig:1 #SBATCH --mem-per-gpu 100G #SBATCH -o logs/slurm-%x-%j-%N.out IMG=/home/software/singularity/pytorch.simg:2024-12-03 cd ~/particleflow WEIGHTS=experiments/pyg-clic_20250106_193536_269746/checkpoints/checkpoint-05-1.995116.pth singularity exec -B /scratch/persistent --nv \ --env PYTHONPATH=`pwd` \ --env KERAS_BACKEND=torch \ $IMG python3 mlpf/pipeline.py --gpus 1 \ --data-dir /scratch/persistent/joosep/tensorflow_datasets --config parameters/pytorch/pyg-clic.yaml \ --test --make-plots --gpu-batch-multiplier 100 --load $WEIGHTS --dtype bfloat16 --num-workers 0 --ntest 50000 ``` ## Citation ## Glossary - PF: particle flow reconstruction - MLPF: machine learning for particle flow - CLIC: Compact Linear Collider ## Model Card Contact Joosep Pata, joosep.pata@cern.ch