Separation of Pair and Single Top Quark Production in tWb Associated Final State Using a Neural Network

The paper presents a method for separating contributions of pair and single top quark production to tWb associated final state using a neural network. The proposed method makes it possible to calculate such processes in a gauge-invariant way, taking into account interference contributions and dividing the phase space into single-resonant and double-resonant regions. The optimized set of observables is used to separate single-resonant and double-resonant contributions to the overall process for neural network training. A usage of the method allows for avoiding the disadvantages that are inherent in the schemes used in collider physics for calculation of the tWb associated top quark production with the removal of Feynman diagrams, which leads to violation of gauge invariance, or the addition of a subtraction scheme, which leads to the appearance of negative weights for the part of simulated events. The proposed method can be used to increase the efficiency of the search for deviations from the predictions of the Standard Model in the interaction of the top quark with the W boson and b-quark.