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# Fix random seed for reproducibility

# The below is necessary for starting Numpy generated random numbers
# in a well-defined initial state.
seed = 7
np.random.seed(seed)

# The below is necessary for starting core Python generated random numbers
# in a well-defined state.
python_random.seed(seed)

# The below set_seed() will make random number generation
# in the TensorFlow backend have a well-defined initial state.
# For further details, see: https://www.tensorflow.org/api_docs/python/tf/random/set_seed
tf_random.set_seed(seed)

treename = 'HZZ4LeptonsAnalysisReduced'
filename = {}
upfile = {}
params = {}
df = {}

# Define what are the ROOT files we are interested in (for the two categories,
# signal and background)

filename['sig'] = 'VBF_HToZZTo4mu.root'
filename['bkg_ggHtoZZto4mu'] = 'GluGluHToZZTo4mu.root'
filename['bkg_ZZto4mu'] = 'ZZTo4mu.root'

# Variables from Root Tree that must be copyed to PANDA dataframe (df)
VARS = [ 'f_run', 'f_event', 'f_weight', \
        'f_massjj', 'f_deltajj', 'f_mass4l', 'f_Z1mass' , 'f_Z2mass', \
        'f_lept1_pt','f_lept1_eta','f_lept1_phi', \
        'f_lept2_pt','f_lept2_eta','f_lept2_phi', \
        'f_lept3_pt','f_lept3_eta','f_lept3_phi', \
        'f_lept4_pt','f_lept4_eta','f_lept4_phi', \
        'f_jet1_pt','f_jet1_eta','f_jet1_phi', \
        'f_jet2_pt','f_jet2_eta','f_jet2_phi' ]

#checking the dimensions of the df , 26 variables
NDIM = len(VARS)

print("Number of kinematic variables imported from the ROOT files = %d"% NDIM)

upfile['sig'] = uproot.open(filename['sig'])
upfile['bkg_ggHtoZZto4mu'] = uproot.open(filename['bkg_ggHtoZZto4mu'])
upfile['bkg_ZZto4mu'] = uproot.open(filename['bkg_ZZto4mu'])
Number of kinematic variables imported from the ROOT files = 26

Let's see what you have uploaded in your Colab notebook!

# Look at the signal and bkg events before applying physical requirement

df['sig'] = pd.DataFrame(upfile['sig'][treename].arrays(VARS, library="np"),columns=VARS)
print(df['sig'].shape)

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• The first 2 columns contain information that is provided by experiments at the LHC that will not be used in the training of our Machine Learning algorithms, therefore we skip our explanation to the next columns.

• The next variable is the f_weights. This corresponds to the probability of having that particular kind of physical process on the whole experiment. Indeed, it is a product of Branching Ratio (BR), geometrical acceptance

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• and kinematic phase-space (generator level). It is very important for the training phase and you will use it later.

• The variables f_massjj,f_deltajj,f_mass4l,f_Z1mass, and f_Z2mass are named high-level features (event features) since they contain overall information about the final-state particles (the mass of the two jets, their separation in space, the invariant mass of the four leptons, the masses of the two Z bosons). Note that the Image Modified mass is lighter w.r.t. the Image Modified one. Why is that? In the Higgs boson production (hypothesis of mass = 125 GeV) only one of the Z bosons is an actual particle that has the nominal mass of 91.18 GeV. The other one is a virtual (off-mass shell) particle.

• The

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• other columns represent the low-level features (object kinematics observables), the basic measurements which are made by the detectors for the individual final state objects (in our case four charged leptons and jets) such as f_lept1(2,3,4)_pt(phi,eta) corresponding to their transverse

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• momentum Image Added and the spatial distribution of their tracks (Image Modified).

The same comments hold for the background datasets:

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