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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 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 Removed mass m_Z2 mass is lighter w.r.t. the Image Removed onem_Z1 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 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 momentum Image Removed and pt and the spatial distribution of their tracks (Image Removedη,Φ).

The same comments hold for the background datasets:

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