# Machine Learning Applications for Jet Tagging in the CMS Experiment
## 4 Analysis Applications
A parametric DNN algorithm is used to increase the sensitivity of the signal against the main SM background, represented by \(\rm t\bar{t}\) events. A total of \(11\) kinematic variables are used for training, with the addition of two parameters: the top squark and neutralino masses. The choice of network parameters strongly depends on the masses of the new particles, and so a specific model is adopted for each signal point. Training is performed with TensorFlow. All the hyperparameters are optimised with the aim of avoiding overfitting and achieving the highest possible classification accuracy. The final DNN structure is made up of seven hidden layers with a ReLU activation function (300, 200, 100, 100, 100, 100, and 10 neurons). The output consists of two neurons with a softmax normalisation function to allow interpretation of the output numbers as probabilities. The optimiser selected is Adam with a learning rate of 0.0001. Figure 10 shows the DNN output for two different mass parameters for signal and \(\rm t\bar{t}\) background; it also shows the discrimination power of the DNN. The gain in sensitivity by using the DNN score increases with increasing \(\Delta m_{cor}\) and with increasing neutralino mass for a fixed \(\Delta m_{cor}\). Another very important analysis to be discussed is Higgs boson pair production via vector (V) boson fusion in highly Lorentz-boosted topologies. This type of event allows the study of three different types of coupling: trilinear Higgs boson self-couplings (HHH), and trilinear and quartic Higgs boson couplings to \(Z\) and W bosons (VVH and VVHH, respectively). A deep comprehension of these couplings could give insight into the properties of the [PERSON] boson, and precise measurement could shed light on its nature, allowing testing of the SM. At the LHC, HH nonresonant production via vector boson fusion is expected to be a very important signature of the [PERSON] boson. Figure 10: Normalized distribution of DNN score comparing the signal and the background (\(\rm t\bar{t}\) events) for two hypotheses of top squark and neutralino masses [35]. fusion has a small cross-section on the order of 2 fb. The selection can be optimised by choosing the most abundant branching fraction decay of the [PERSON] boson that turns to \(\mathrm{H}\to\mathrm{b}\bar{\mathrm{b}}\) (\(\sim 33\%\)). This final state is very challenging in the LHC hadronic environment, especially when looking for highly Lorentz-boosted topologies, for which the standard b-tagging algorithms start to fail. This problem is overcome by the ParticleNet algorithm, which is specifically trained for cases such as this. The analysis reported in [36] represents the first application of this novel technique. The \(\mathrm{H}\to\mathrm{b}\bar{\mathrm{b}}\) discrimination against the QCD processes is achieved by defining the \(D_{\mathrm{b}\bar{\mathrm{b}}}\) tagging discriminator as:  
\[D_{\mathrm{b}\bar{\mathrm{b}}}=\frac{score(\mathrm{H}\to\mathrm{b}\bar{ \mathrm{b}})}{score(\mathrm{H}\to\mathrm{b}\bar{\mathrm{b}})+score(QCD)},\]  
where the \(score(\mathrm{H}\to\mathrm{b}\bar{\mathrm{b}})\) and \(score(QCD)\) are the output scores provided by the ParticleNet algorithm. In order to maximise background rejection, a requirement of \(D_{\mathrm{b}\bar{\mathrm{b}}}>0.9\) is applied. The analysis makes use of three categories with different signal purity:  
* High purity: \(D_{\mathrm{b}\bar{\mathrm{b}}}>0.98\) for both the Higgs jets;
* Medium purity: \(0.94<D_{\mathrm{b}\bar{\mathrm{b}}}<0.98\) for both the Higgs jets;
* Low purity: \(0.9<D_{\mathrm{b}\bar{\mathrm{b}}}<0.94\) for both the Higgs jets. The signal is then extracted with a binned maximum-likelihood fit performed simultaneously in all the categories.