| # 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. | |
| <details> | |
| <summary>Jet performance</summary> | |
| <img src="plots_checkpoint-05-1.995116/clic_edm_ttbar_pf/jet_response_iqr_over_med_pt.png" alt="ttbar jet resolution" width="300"/> | |
| <img src="plots_checkpoint-05-1.995116/clic_edm_qq_pf/jet_response_iqr_over_med_pt.png" alt="qq jet resolution" width="300"/> | |
| <img src="plots_checkpoint-05-1.995116/clic_edm_ww_fullhad_pf/jet_response_iqr_over_med_pt.png" alt="ttbar jet resolution" width="300"/> | |
| </details> | |
| <details> | |
| <summary>MET performance</summary> | |
| <img src="plots_checkpoint-05-1.995116/clic_edm_ttbar_pf/met_response_iqr_over_med.png" alt="ttbar MET resolution" width="300"/> | |
| <img src="plots_checkpoint-05-1.995116/clic_edm_qq_pf/met_response_iqr_over_med.png" alt="qq MET resolution" width="300"/> | |
| <img src="plots_checkpoint-05-1.995116/clic_edm_ww_fullhad_pf/met_response_iqr_over_med.png" alt="ttbar MET resolution" width="300"/> | |
| </details> | |
| ### 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| The model has not been peer reviewed or published in a peer-reviewed journal.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, [email protected] | |