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particleflow
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# 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The model has not been peer reviewed or published in a peer-reviewed journal.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]