Separation of left-handed and anomalous right-handed vector operators contributions into the Wtb vertex for single and double resonant top quark production processes using a neural network

TL;DR

This paper employs deep neural networks to distinguish and constrain anomalous right-handed vector contributions at the Wtb vertex in top quark production, enhancing sensitivity over traditional methods.

Contribution

It introduces a neural network-based approach with a two-level classification system to improve detection of anomalous Wtb vertex contributions in top quark events.

Findings

Neural network method increases search efficiency for anomalous contributions.

Constraints on right-handed vector operators are set in various phase space regions.

Enhanced sensitivity compared to previous analysis techniques.

Abstract

The paper describes the application of deep neural networks for the searchdeviations from the Standard Model predictions at the Wtb vertex in the processes of single and double resonant top quark production with identical final state tWb. Monte-Carlo events preliminary classified by first level neural network as corresponding to single or double resonant top quark production are analyzed by two second level neural networks if there is a possible contribution of the anomalous right-handed vector operator into Wtb vertex or events are corresponded to the Standard Model. The second level neural networks are different for single and double resonant classes. The classes depend differently on anomalous contribution and such splitting leads to better sensitivity. The developed statistical model is used to set constraints on the anomalous right-handed vector operator at the Wtb vertex in different regions of phase space. It is demonstrated that the proposed method allows to increase the efficiency of a search for the anomalous contributions to the Wtb vertex.