Machine learning based pipeline for particle flow at FCC.
Latest documentation/ presentations can be found here:
Additional information and setup instructions can be found in the wiki!
- One event is formed by hits (which can be tracks or calo hits). An input is an event in the form of a graph, and the output is a single particle (in coming versions of the dataset there will be more).
- Models: The goal of the current model is to first custer and then regress the particle's information (PID and energy). Currently the best approach is the object condensation, since it allows to regress a variable number of particles.
- The pipeline includes the following four steps: training the clustering, training energy correction and PID, evaluation, plotting
- Training: To train a model check the wiki/Training section
Runs for this project can be found in the following work space: https://wandb.ai/imdea_dolo/mlpf?workspace=user-imdea_dolo
You can use the docker container docker://dologarcia/gatr:v9 and use singularity to run the container as:
export APPTAINER_CACHEDIR=/home/ADDUSER/cache/
singularity shell -B /eos -B /afs --nv docker://dologarcia/gatr:v9
You might need to set up a conda env to run notebooks. To set up the env create a conda env following the instructions from Weaver and also install the packages in the requirements.sh script above Alternatively, you can try to use a pre-built environment from this link - the .tar.gz file was built using conda-pack on fcc-gpu-04v2.cern.ch.