Umami

Installation

Docker image

singularity exec docker://gitlab-registry.cern.ch/mguth/umami/umami-cpu:latest bash

Manual installation

you can check out this repository via git clone and then run python setupy.py install this will install the umami package if you want to modify the code you should install it via python setupy.py develop

Testing

The unit test you can run via

pytest ./umami/tests/ -v

Preprocessing

For the training of umami the ntuples are used as specified in the section MC Samples.

Training ntuples are produced using the training-dataset-dumper which dumps them directly into hdf5 files. The finished ntuples are also listed in the table in the section MC Samples.

There are two different labeling available, the HadronConeExclTruthLabelID and the HadronConeExclExtendedTruthLabelID which includes extended jet categories:

For the HadronConeExclTruthLabelID labeling, the categories 4 and 44 as well as 5 and 55 are combined.

Ntuple preparation for b-,c- & light-jets

These jets are taken from ttbar and Z' events.

After the ntuple production the samples have to be further processed using the script create_hybrid-large_files.py

In case of the default umami (3 categories:b, c, light) the label HadronConeExclTruthLabelID is used.

There are several training and validation/test samples to produce. See below a list of all the necessary ones

Training Samples (even EventNumber)

• ttbar (pT < 250 GeV)
  • b-jets

    python ${SCRIPT}/create_hybrid.py --n_split 4 --even --bjets -Z ${ZPRIME} -t ${TTBAR} -n 10000000 -c 1.0 -o ${FPATH}/hybrids/MC16d_hybrid-bjets_even_1_PFlow-merged.h5 --write_tracks 

  • c-jets

    python ${SCRIPT}/create_hybrid-large_files.py --n_split 4 --even --cjets -Z ${ZPRIME} -t ${TTBAR} -n 12745953 -c 1.0 -o ${FPATH}/hybrids/MC16d_hybrid-cjets_even_1_PFlow-merged.h5 --write_tracks

  • light-jets

    python ${SCRIPT}/create_hybrid-large_files.py --n_split 5 --even --ujets -Z ${ZPRIME} -t ${TTBAR} -n 20000000 -c 1.0 -o ${FPATH}/hybrids/MC16d_hybrid-ujets_even_1_PFlow-merged.h5 --write_tracks

• Z' (pT > 250 GeV) -> extended Z'
  • b, c, light-jets combined

    python ${SCRIPT}/create_hybrid-large_files.py --even -Z ${ZPRIME} -t ${TTBAR} -n 9593092 -c 0.0 -o ${FPATH}/hybrids/MC16d_hybrid-ext_even_0_PFlow-merged.h5 --write_tracks

Validation and Test Samples (odd EventNumber)

• ttbar

  python ${SCRIPT}/create_hybrid-large_files.py --n_split 2 --odd --no_cut -Z ${ZPRIME} -t ${TTBAR} -n 4000000 -c 1.0 -o ${FPATH}/hybrids/MC16d_hybrid_odd_100_PFlow-no_pTcuts.h5 --write_tracks

• Z' (extended and standard)

  python ${SCRIPT}/create_hybrid-large_files.py --n_split 2 --odd --no_cut -Z ${ZPRIME} -t ${TTBAR} -n 4000000 -c 0.0 -o ${FPATH}/hybrids/MC16d_hybrid-ext_odd_0_PFlow-no_pTcuts.h5 --write_tracks

The above script will output several files per sample which can be merged using the merge_big.py script.

Ntuple Preparation for bb-jets

The double b-jets will be taken from Znunu and Zmumu samples.

Since the double b-jets represent only a fraction of the jets, they can be filtered out using the merge_ntuples.py script from the hdf5-manipulator.

MC Samples

The FTAG1 derivations and the most recent ntuples for PFlow with the new RNNIP, SMT and the latest DL1* recommendations inside are shown in the following table

The Z' FTAG 1 derivations were requested here

In addition there are the Z+jets (Z->mumu/nunu) samples for the bb extension

ntuples on slac cluster: /u/ki/nhartman/gpfs/public/btag_hdf5/umami-stdTrkCuts

ntuples on Freiburg cluster: /work/ws/nemo/fr_mg1150-umami-0/ntuples-p3985/