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hhh

1. Pull and start the Docker container

docker pull jmduarte/hhh
docker run -it jmduarte/hhh bash

2. Check out the GitHub repository

cd work
git clone https://github.com/ucsd-hep-ex/hhh

3. Install the Python package(s)

cd hhh
pip install -e .
cd ..

4. Copy and convert the dataset(s)

Copy the Delphes ROOT TTree datasets from:

  • CERN EOS: /eos/user/m/mstamenk/CxAOD31run/hhh-6b/delphes-samples/GF_HHH_SM_c3_0_d4_0_14TeV/sample_*.root, or
  • UCSD UAF: /ceph/cms/store/user/woodson/GF_HHH_SM_c3_0_d4_0_14TeV/sample_*.root

to the data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV directory

Convert to training and testing HDF5 files.

python -m src.data.delphes.convert_to_h5 data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_*.root --out-file data/delphes/v2/hhh_training.h5
python -m src.data.delphes.convert_to_h5 data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_*.root --out-file data/delphes/v2/hhh_testing.h5

5. Run the SPANet training

Override options file with --gpus 0 if no GPUs are available.

python -m spanet.train -of options_files/delphes/hhh_v2.json [--gpus 0]

6. Evaluate the SPANet training

Assuming the output log directory is spanet_output/version_0. Add --gpu if a GPU is available.

python -m spanet.test spanet_output/version_0 -tf data/delphes/v2/hhh_testing.h5 [--gpu]

7. Evaluate the baseline method

python -m src.models.test_baseline --test-file data/delphes/v2/hhh_testing.h5

Instructions for CMS data set baseline

The CMS dataset was updated to run with the v26 setup (nAK4 >= 4 and HLT selection). The update includes the possibility to apply the b-jet energy correction. By keeping events with at a least 4 jets, the boosted training can be performed on a maximum number of events and topologies.

List of samples (currently setup validated using 2018):

/eos/user/m/mstamenk/CxAOD31run/hhh-6b/cms-samples-spanet/v26/GluGluToHHHTo6B_SM_spanet_v26_2016APV.root
/eos/user/m/mstamenk/CxAOD31run/hhh-6b/cms-samples-spanet/v26/GluGluToHHHTo6B_SM_spanet_v26_2016.root
/eos/user/m/mstamenk/CxAOD31run/hhh-6b/cms-samples-spanet/v26/GluGluToHHHTo6B_SM_spanet_v26_2017.root
/eos/user/m/mstamenk/CxAOD31run/hhh-6b/cms-samples-spanet/v26/GluGluToHHHTo6B_SM_spanet_v26_2018.root

To run the framework, first convert the samples (this will allow to use both jets pt or ptcorr, steerable from the configuration file:

mkdir data/cms/v26/
python -m src.data.cms.convert_to_h5 /eos/user/m/mstamenk/CxAOD31run/hhh-6b/cms-samples-spanet/v26/GluGluToHHHTo6B_SM_spanet_v26_2018.root --out-file data/cms/v26/hhh_training.h5
python -m src.data.cms.convert_to_h5 /eos/user/m/mstamenk/CxAOD31run/hhh-6b/cms-samples-spanet/v26/GluGluToHHHTo6B_SM_spanet_v26_2018.root --out-file data/cms/v26/hhh_testing.h5

Then training can be done via:

python -m spanet.train -of options_files/cms/hhh_v26.json --gpus 1

Two config files exist for the event options:

event_files/cms/hhh.yaml # regular jet pT
event_files/cms/hhh_bregcorr.yaml # jet pT with b-jet energy correction scale factors applied

Note: to run the training with the b-jet energy correction applied, the log_normalize of the input variable was removed. Keeping it caused a 'Assignement collision'.