# Machine Learning Applications for Jet Tagging in the CMS Experiment
## 4 Analysis Applications  
Many analyses have started to use ML techniques at different stages of the workflow, from physics objects selection, which make use of vertexes and tracks, to the final signal-to-background discrimination based on high-level variables. The first class of these methods has already been described in the previous section. Here the focus is on the second class of use by discussing some of the newest or most original approaches proposed in CMS Collaboration papers. The first of such analyses is reported in [32] and presents the search for a \(\mathrm{W^{\prime}}\) boson decaying to a top quark, decaying hadronically, and a bottom quark. This analysis aims to study the processes for which the Feynman diagrams are reported in Figure 8.  
The DeepJet algorithm is used for the b tagging of the jets related to bottom quark production and hadronisation. The jets are AK4 jets with \(\mathrm{p_{T}}>500\) GeV, and the thresholds for the taggers correspond to a misidentification rate of 5% for jets initiated by light quarks or gluons. However, the efficiency is reduced from 75% to 65% for jets with \(\mathrm{p_{T}}=500\) GeV and jets with \(\mathrm{p_{T}}=1000\) GeV, respectively, in the barrel region. For top tagging, the DeepAK8 algorithm is used; it takes into consideration jets with \(R=0.8\) and \(105<m_{\mathrm{SD}}(\mathrm{t})<210\) GeV. The threshold used on the DeepAK8 tagger score corresponds to a misidentification rate of 0.5% for jets initiated by light quarks or gluons, and to an efficiency of approximately 35-45% for jets initiated by top quarks. The use of these ML techniques improves the final state selection compared to previous studies in [33], excluding \(\mathrm{W^{\prime}}\) boson mass below 3.4 TeV. The search for \(\mathrm{W^{\prime}}\) boson is also characterised by a different final state with a vector-like quark and a top or bottom quark in the all-jets final state. The Feynman diagram of the process is shown in Figure 9.  
The final state foresees the presence of a top and bottom quark in association with a Higgs or \(Z\) boson. In order to successfully identify events with such topology, two different ML methods are used. The top quark is recognised by means of the newest top tagger developed by the CMS Collaboration: the ImageTop tagger. This study represents the first application of this tagger in a physics analysis. The improvement of ImageTop is quantified by a factor-six gain in tagging efficiency with respect to previous algorithms. This analysis uses the MD version of the algorithm, and since, in this case, the dependence of the tagger response is barely dependent on the top quark mass, a requirement on this  
Figure 8: \(\mathrm{W^{\prime}}\) boson production and its decay into top and bottom quarks [32]. Figure 9: Feynman diagrams at lowest order for the production of a \(\mathrm{W^{\prime}}\) boson decaying to a vector-like quark and a top or bottom quark [34]. variable is also applied. The values for the mass of the top jet are within the window \(40<m_{\rm SD}(\rm t)<220\) GeV. The Higgs or the \(Z\) jet are instead recognised with the DeepAK8 tagger after imposing a veto on the top jet tagger for the selected jet. Additionally, in this case, a window for the soft-drop mass is applied: precisely \(105<m_{\rm SD}(\rm H)<140\) GeV and \(65<m_{\rm SD}(Z)<105\) GeV, respectively, for the Higgs jet and the \(Z\) jet candidate. A third example of analysis in which the final signal-to-background discrimination uses an ML algorithm is reported in [35]. In such analyses, a combined search for the production of a SUSY top quark partner, or top squark, is presented. The new analysis includes a parameter space in which the mass difference (\(\Delta m_{cor}\)) between the top squark and the neutralino is close to the top quark mass, the so-called top quark corridor region. The final state consists of a dilepton pair with opposite charge and missing \(\rm p_{T}\).