# VBF Event Classification with Recurrent Neural Networks at ATLAS's LHC Experiment
## 5 Performance of the Method  
The RNN model was developed in order to classify the production mechanism of both SM and BSM events. Examples of new hypothetical models that predict a VBF signal production at LHC are the heavy vector triplets (HVT) [18] and Randall-Sundrum (RS) frameworks [19; 20; 21; 22]. For these models, multiple independent signal hypotheses were considered, varying the spin and the mass of the particle. Spin values of 0, 1 and 2 and masses in the 0.3-5 TeV range were considered. The final state where the model was studied is called _semi-leptonic_: the signal mass X decay to two vector bosons, which can be a Z or a W boson that subsequently decays to two leptons and two quarks. According to the boson decaying leptonically, three distinct channels are possible: 0-lepton (\(X\to ZV\to\
u\
u q\)), 1-lepton (\(X\to WV\to\ell\
u qq\)) and 2-lepton (\(X\to ZV\to\ell\ell qq\)). Here, \(V\) denotes a vector boson (Z or W) and \(\ell\) a lepton (an electron \(e\) or a muon \(\mu\)). The model reaches high performances for this classification task with the value of the area under the ROC curve \(\sim\)85-90%; the performances were found stable across the different signal hypotheses. The discrimination power is represented in the shapes distributions of the RNN score for a GGF/DY signal hypothesis and for a VBF hypothesis in Figure 7 where the three different spin hypotheses for RS Radion, HVT and RS gravitons BSM models were shown in the 2-lepton decay channel. The performances of the RNN score were found to be quite stable across the different spin hypotheses considered; this is related to the fact that the main features of this topology are weakly dependent on the spin of the produced resonance, and the kinematics of the two extra associated jets produced in the VBF mechanism are dominant with respect to the other production mechanisms in all the theoretical scenarios. Figure 7: RNN score distributions for the production of a 1 TeV resonance in the signal models considered for this search for the 2-lepton channel. The RNN score was used to categorise the observed events; in particular, a cut equal to 0.8 was applied. Therefore, events with an RNN score \(\geq\) 0.8 were categorised as VBF events, and events with an RNN score \(<\) 0.8 as GGF/DY (no-VBF) events. Figure 8 shows the signal efficiency in the VBF category for VBF and GGF/DY signal hypotheses. The choice of the cut was made to preserve a similar background contamination from other SM processes in the VBF category as observed in previous studies with standard data analysis techniques [12]. The improved confusion matrix allows enhancing the VBF signal efficiency from 10% at low mass up to 60% at the high-mass range, and therefore, the final sensitivity to such BSM models were improved. Such improvement is quite significant in the context of BSM searches, especially when this is coming from one source. In this case, an ML approach used to solve the classification problem is improving the confusion matrix and, in the end, the results of the physics analysis. In general, the result of a physics analysis may be improved using more data collected by the experiment, but larger data gathering may happen in the scale of many years. Therefore, significant improvements introduced by a new data analysis strategy may allow us to reach better physics results beforehand. Furthermore, this approach based on the RNN allowed us to naturally recover a significant part of signal events that would otherwise be lost due to the geometrical acceptance of the detector. Indeed, the two VBF jets are produced in the forward region of the detector, high pseudo-rapidity (\(\eta\)), and the resulting separation between the two jets is higher and higher as a function of the X particle mass. A relative fraction equal to 20% at low-mass range and 50% at high-mass range with just 1 VBF jet reconstructed in the events are expected [23]; this represents an additional conceptual improvement coming from using this novel architecture.