DiJetGAN

Check out package

git clone https://gitlab.cern.ch/disipio/DiJetGAN.git
cd DiJetGAN

Create input files

The scripts accept as input a text file that contains the full path of ROOT files, which in turn are loaded to memory as a TChain object. It is assumed that ntuples are created using MadGraph5 + Pythia8 + Delphes3.

mkdir -p filelists
ls /home/disipio/mcgenerators/MG5_aMC_v2_6_4/pp2jj_lo/Events/run_04/tag_1_delphes_events.root > filelists/mg5_dijet_ht500.delphes.pt250.txt

The ROOT files create by Delphes3 are very large. Only a small fraction of the information is needed for the purpose of training the GAN. Thus, a smaller ntuples (using the "AnalysisTop mc16a" format) has to be created.

mkdir -p ntuples_MC
./delphes2tree.py -i filelists/mg5_dijet_ht500.delphes.pt250.txt -l reco
./delphes2tree.py -i filelists/mg5_dijet_ht500.delphes.pt250.txt -l ptcl

For your convenience, these files can be downloaded from these URLs with curl or wget:

wget https://dijetgan.s3.ca-central-1.amazonaws.com/ntuples_MC/tree.mg5_dijet_ht500.ptcl.pt250.nominal.root
wget https://dijetgan.s3.ca-central-1.amazonaws.com/ntuples_MC/tree.mg5_dijet_ht500.reco.pt250.nominal.root
wget https://dijetgan.s3.ca-central-1.amazonaws.com/ntuples_MC/tree.mg5_ttbar_ht700.ptcl.pt250.nominal.root
wget https://dijetgan.s3.ca-central-1.amazonaws.com/ntuples_MC/tree.mg5_ttbar_ht700.reco.pt250.nominal.root

For reference, we also made available some files containing events generated by the GAN which we used in the paper:

wget https://dijetgan.s3.ca-central-1.amazonaws.com/ntuples_GAN/tree.mg5_dijet_ht500.ptcl.pt250.nominal.root
wget https://dijetgan.s3.ca-central-1.amazonaws.com/ntuples_GAN/tree.mg5_dijet_ht500.reco.pt250.nominal.root

Now it is possible to convert ROOT file to CSV. This operation includes some pre-processing, e.g. all jets are phi-rotated by the same amount so that the leading jet phi is always zero:

mkdir -p csv
ls ntuples_MC/tree.mg5_dijet_ht500.ptcl.pt250.nominal.root > filelists/mg5_dijet_ht500.ptcl.pt250.MC.txt
ls ntuples_MC/tree.mg5_dijet_ht500.reco.pt250.nominal.root > filelists/mg5_dijet_ht500.reco.pt250.MC.txt

./root2csv.py -i filelists/mg5_dijet_ht500.ptcl.pt250.MC.txt -l ptcl
./root2csv.py -i filelists/mg5_dijet_ht500.reco.pt250.MC.txt -l reco

Again, for your convenience, you can download these files from this URL:

wget https://dijetgan.s3.ca-central-1.amazonaws.com/csv/mg5_dijet_ht500.ptcl.pt250.large.csv
wget https://dijetgan.s3.ca-central-1.amazonaws.com/csv/mg5_dijet_ht500.reco.pt250.nominal.csv

wget https://dijetgan.s3.ca-central-1.amazonaws.com/csv/mg5_ttbar_ht700.ptcl.pt250.nominal.csv
wget https://dijetgan.s3.ca-central-1.amazonaws.com/csv/mg5_ttbar_ht700.reco.pt250.nominal.csv

Initialize scaler

mkdir -p GAN
./init_scaler.py csv/mg5_dijet_ht500.reco.pt250.nominal.csv
./init_scaler.py csv/mg5_dijet_ht500.ptcl.pt250.nominal.csv 

Train the generative-adversarial network (GAN):

mkdir -p GAN
mkdir -p img
./train_GAN.py -e 5000 -d mg5_dijet_ht500 -l reco
./train_GAN.py -e 5000 -d mg5_dijet_ht500 -l ptcl

The generator model and the scaler have been saved to the GAN folder.

Plot training history:

./plot_traninig.py reco
./plot_traninig.py ptcl

# only if you have already created the MG5 histograms:
./plot_training_observables.py ptcl
./plot_training_observables.py reco

Generate events

mkdir -p ntuples_GAN
./generate_events.py -l reco -n 500000
./generate_events.py -l ptcl -n 500000

ls ntuples_GAN/tree.mg5_dijet_ht500.ptcl.pt250.nominal.root > filelists/mg5_dijet_ht500.ptcl.pt250.GAN.txt
ls ntuples_GAN/tree.mg5_dijet_ht500.reco.pt250.nominal.root > filelists/mg5_dijet_ht500.reco.pt250.GAN.txt 

Fill histograms

We want to compare three series of data:

• Original MG5 distributions, p4's are calculated using Lorentz vectors kinematics (tag: p4_tlv)
• GAN-generated distributions, p4's are calculated using Lorentz vectors kinematics from the (pT,eta,phi,M) of the two leading jets (tag: p4_tlv)
• GAN-generated distributions, all distributions are taken from the GAN's output (tag: p4_gan)

mkdir -p histograms
./fill_histograms.py filelists/mg5_dijet_ht500.ptcl.pt250.MC.txt
./fill_histograms.py filelists/mg5_dijet_ht500.ptcl.pt250.GAN.txt

Make final plots

cat observables.txt | parallel ./plot_observables.py {} ptcl mg5_dijet_ht500
cat observables.txt | parallel ./plot_observables.py {} reco mg5_dijet_ht500

You can do all the above with the following script:

./workflow.sh -d mg5_dijet_ht500 -l reco -e ${n_training_epochs} -n ${n_generate_events}
./workflow.sh -d mg5_dijet_ht500 -l ptcl -e ${n_training_epochs} -n ${n_generate_events}