This work investigates the process jj → W± W±hjj with a focus on the quartic coupling vertex WWhh. The first objective is to compare the Monte Carlo event generators MadGraph and Sherpa in terms of the datasets they produce for this process.
Furthermore, the behaviour of sensitive observables is studied as a function of the coupling parameter κ2V. On the one hand, selected observables are analyzed as a basis for training a Deep Neural Network (DNN) aimed at identifying Monte Carlo samples with anomalous values of κ2V. On the other hand, differences between the generators are explored using observables that are strongly correlated with the signal process. In addition, a brief outlook is provided on the behaviour of the signal process at a center-of-mass energy of 100 TeV. The impact of varying the coupling parameter κ 2V on the total cross section of the process is illustrated and quantified. Moreover, the sensitivity of individual observables to changes in κ2V , as well as the discrepancies between the datasets generated by MadGraph and Sherpa, are examined in detail.