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model = keras.models.load_model('ANN_model.h5')

Description of the Random Forest (RF) and Scikit-learn library

In this section you will find the following subsections:

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# Plot Jet2 eta for signal, background and NN/RF selected events 
plt.xlabel('$\eta$(Jet2)')
X = np.linspace(-5.,5.,100)
plt.rcParams['figure.figsize'] = (10,5)
df_all['f_jet2_eta'][(df_all['isSignal'] == 0)].plot.hist(bins=X, label='bkg',histtype='step', density=1)
df_all['f_jet2_eta'][(df_all['isSignal'] == 1)].plot.hist(bins=X, label='signal',histtype='step', density=1)
df_sel['f_jet2_eta'].plot.hist(bins=X, label='NN',histtype='step', density=1)
df_sel_rf['f_jet2_eta'].plot.hist(bins=X, label='RF',histtype='step', density=1)
plt.title('$jet2(\eta)$ normalized distribution',fontsize=12,fontweight='bold', color='r')
plt.legend(loc='upper right')
plt.xlim(-5,5)

Optional Exercise 1 - Change the decay channel

Question to students: What happens if you switch to the decay channel? You can submit your model (see the ML challenge below) for this physical process as well!

Optional Exercise 2 - Merge the backgrounds

Question to students: Merge the backgrounds used up to now for the training of our ML algorithms together with the ROOT File named ttH_HToZZ_4L.root. In this case, you will use also the QCD irreducible background. Uncomment the correct lines of code to proceed!

Machine Learning challenge

Once you manage to improve the network (random forest) performances, you can submit your results and participate in our ML challenge. The challenge samples are available in this workspace, but the true labels (isSignal) are removed so that you can't compute the AUC.

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(164560, 5)
(164560, 1)
[[1.7398037e-05]
 [3.2408145e-01]
 [1.1487612e-04]
 ...
 [2.4130943e-01]
 [1.4921818e-05]
 [8.3920550e-01]]

Out[ ]:

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