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In the following, the most important excerpts are described.
Annotated Description
Load data using PANDAS data frames
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Let's see what you have uploaded in your Colab notebook!
In [ ]:
# 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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Let's print out the first rows of this data set!
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df['sig'].head()
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| 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 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 385228 | 0.000176 | 667.271423 | 3.739947 | 124.966576 | 90.768616 | 20.508274 | 82.890457 | 0.822203 | 1.343706 | 65.486946 | 0.382922 | 2.568485 | 39.838531 | 0.546917 | 2.497204 | 28.562206 | 0.174666 | 2.013540 | 116.326035 | -1.126533 | -1.759238 | 90.333893 | 2.613415 | -0.096671 |
| 1 | 1 | 385233 | 0.000127 | 129.085892 | 0.046317 | 120.231926 | 80.782318 | 34.261726 | 41.195362 | -0.534245 | 2.802684 | 24.911942 | -2.065928 | 0.371150 | 21.959597 | -1.219900 | -2.938914 | 16.676077 | -0.162915 | 1.783374 | 105.491882 | 3.253374 | -1.297283 | 38.978493 | 3.207056 | 1.553476 |
| 2 | 1 | 385254 | 0.000037 | 285.165222 | 3.166899 | 125.254646 | 91.392693 | 25.695290 | 80.788002 | 0.943778 | 0.729632 | 35.549721 | 0.935241 | 1.288549 | 23.206284 | 0.236346 | -2.670540 | 14.581854 | 1.516623 | 0.284658 | 69.315170 | 2.573589 | -2.030811 | 51.972664 | -0.593310 | -2.799394 |
| 3 | 1 | 385260 | 0.000043 | 52.006794 | 0.150803 | 125.067009 | 91.183708 | 19.631315 | 129.883423 | 0.235406 | -1.729384 | 37.950790 | 1.226075 | -2.540356 | 17.678413 | 0.096546 | -1.533120 | 8.197763 | -0.157577 | 0.339215 | 202.689468 | 2.530802 | 1.325786 | 41.343758 | 2.681605 | 0.858582 |
| 4 | 1 | 385263 | 0.000092 | 1044.083496 | 4.315164 | 124.305748 | 72.480515 | 43.826504 | 86.220734 | -0.226653 | 0.117277 | 80.451378 | -0.536749 | 0.385678 | 27.497240 | 0.827591 | -0.072236 | 21.243813 | -0.579560 | -0.884727 | 127.192223 | -2.362456 | -2.945257 | 115.200272 | 1.952708 | 2.053301 |
The first 2 columns contain information which are 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 of the detector, and kinematic phase-space. It is very important for the trainings phase and you will use it later.The variables
f_massjj,f_deltajj,f_mass4l,f_Z1mass, andf_Z2massare 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 themass is lighter w.r.t. the
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 remnant 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 and the spatial distribution of their tracks ().
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# Part of the code in "#" can be used in the second part of the exercise # for trying to use alternative datasets for the training of our ML algorithms #df['bkg'] = pd.DataFrame(upfile['bkg'][treename].arrays(VARS, library="np"),columns=VARS) #df['bkg'].head() df['bkg_ggHtoZZto4mu'] = pd.DataFrame(upfile['bkg_ggHtoZZto4mu'][treename].arrays(VARS, library="np"),columns=VARS) df['bkg_ggHtoZZto4mu'].head() #df['bkg_ggHtoZZto4e'] = pd.DataFrame(upfile['bkg_ggHtoZZto4e'][treename].arrays(VARS, library="np"),columns=VARS) #df['bkg_ggHtoZZto4e'].head() #df['bkg_ZZto4e'] = pd.DataFrame(upfile['bkg_ZZto4e'][treename].arrays(VARS, library="np"),columns=VARS) #df['bkg_ZZto4e'].head()
Out[ ]:
| 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 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 581632 | 0.000225 | -999.0 | -999.0 | 120.101105 | 88.262352 | 22.051540 | 57.572330 | -0.433627 | -0.886073 | 56.933735 | 0.496556 | 0.404675 | 33.584896 | -0.037387 | 0.291866 | 10.881461 | -1.112960 | 0.051097 | 73.541260 | 1.683280 | 2.736636 | -999.0 | -999.0 | -999.0 |
| 1 | 1 | 581659 | 0.000277 | -999.0 | -999.0 | 124.592812 | 82.174683 | 17.613417 | 50.365120 | 0.001362 | 0.933713 | 31.548225 | 0.598417 | -1.863556 | 22.758055 | 0.220867 | -2.767246 | 17.264626 | 0.361964 | -1.859138 | -999.000000 | -999.000000 | -999.000000 | -999.0 | -999.0 | -999.0 |
| 2 | 1 | 581671 | 0.000278 | -999.0 | -999.0 | 125.692230 | 79.915764 | 29.998011 | 72.355927 | -0.238323 | -2.335623 | 20.644920 | -0.241560 | 1.855536 | 16.031651 | -1.446993 | 1.185016 | 11.068296 | 0.366903 | -0.606845 | 64.440544 | 1.886244 | 1.635723 | -999.0 | -999.0 | -999.0 |
| 3 | 1 | 581724 | 0.000336 | -999.0 | -999.0 | 125.027504 | 85.200958 | 23.440151 | 43.059235 | 0.759979 | -1.714778 | 19.248983 | 0.535979 | 0.420337 | 16.595169 | -1.330326 | 1.656061 | 11.407483 | -0.686118 | 1.295116 | -999.000000 | -999.000000 | -999.000000 | -999.0 | -999.0 | -999.0 |
| 4 | 1 | 581744 | 0.000273 | -999.0 | -999.0 | 124.917282 | 65.971390 | 14.968305 | 52.585011 | -0.656421 | -2.933651 | 35.095982 | -1.002568 | 0.865173 | 28.146715 | -0.730926 | -0.876442 | 8.034222 | -1.094436 | 1.783626 | -999.000000 |
df['bkg_ZZto4mu'] = pd.DataFrame(upfile['bkg_ZZto4mu'][treename].arrays(VARS, library="np"),columns=VARS) df['bkg_ZZto4mu'].head()
| 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 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 1991117 | 0.001420 | 384.394165 | 0.235409 | 309.921478 | 93.538399 | 87.436043 | 84.918190 | -0.073681 | -1.339234 | 60.143539 | -1.229701 | -1.409149 | 54.892681 | 1.125339 | -1.046433 | 42.139397 | 0.966109 | 2.593184 | 240.828506 | 0.103300 | 2.408482 | 195.838226 | 0.338708 | 0.285348 |
| 1 | 1 | 1991192 | 0.000893 | 110.589844 | 0.956070 | 326.481903 | 92.948936 | 85.379288 | 124.270218 | 1.388811 | -1.738097 | 87.379723 | 0.766540 | 2.502843 | 54.472603 | -0.106614 | 1.626933 | 18.505959 | 2.012172 | 2.229677 | 77.210411 | 2.061765 | -0.532572 | 48.432365 | 1.105695 | 1.128457 |
| 2 | 1 | 1991331 | 0.000839 | -999.000000 | -999.000000 | 91.167046 | 56.161217 | 14.535084 | 25.241573 | 1.410529 | 2.080089 | 21.971258 | 1.465800 | 1.868505 | 20.648312 | 0.787121 | 2.017863 | 9.831321 | -1.329539 | 2.286600 | 66.642792 | 1.176917 | -1.089489 | -999.000000 | -999.000000 | -999.000000 |
| 3 | 1 | 1991364 | 0.000906 | -999.000000 | -999.000000 | 323.428345 | 88.717270 | 94.940346 | 65.728729 | -0.561113 | 2.596448 | 50.528595 | 2.227971 | 0.101310 | 39.392380 | 0.294608 | -1.756674 | 33.169487 | 0.367907 | -0.241346 | -999.000000 | -999.000000 | -999.000000 | -999.000000 | -999.000000 | -999.000000 |
| 4 | 1 | 1991360 | 0.001034 | -999.000000 | -999.000000 | 274.207916 | 90.799271 | 90.156898 | 101.931305 | 0.828778 | 2.440133 | 89.171135 | -0.052834 |
# Let's merge our background processes together! df['bkg'] = pd.concat([df['bkg_ZZto4mu'],df['bkg_ggHtoZZto4mu']]) # Let's shuffle them! df['bkg']= shuffle(df['bkg']) # Let's see its shape! print(df['bkg'].shape) #print(len(df['bkg'])) #print(len(df['bkg_ZZto4mu'])) #print(len(df['bkg_ggHtoZZto4mu'])) #print(len(df['bkg_ggHtoZZto4e'])) #print(len(df['bkg_ZZto4e']))
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Number of Signal events = 14260 Number of Background events = 100724
#Showing that the variable isSignal is correctly assigned for VBF signal events print(df['sig']['isSignal'])
0 1.0
1 1.0
2 1.0
3 1.0
4 1.0
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24858 1.0
24859 1.0
24860 1.0
24861 1.0
24862 1.0
Name: isSignal, Length: 14260, dtype: float64