Identification of tqg flavor-changing neutral current interactions using machine learning techniques

Flavor-changing neutral currents (FCNCs) are forbidden at tree level in the standard model (SM), but they can be enhanced in physics beyond the standard model (BSM) scenarios. In this paper, we investigate the effectiveness of deep learning techniques to enhance the sensitivity of current and future collider experiments to the production of a top quark and an associated parton through the tqg FCNC process, which originates from the tug and tcg vertices. The tqg FCNC events can be produced with a top quark and either an associated gluon or quark, while SM only has events with a top quark and an associated quark. We apply machine learning techniques to distinguish the tqg FCNC events from the SM backgrounds, including qg-discrimination variables. We use the Boosted Decision Tree (BDT) method as a baseline classifier, assuming that the leading jet originates from the associated parton. We compare with a transformer-based deep learning method known as the Self-Attention for Jet-parton Assignment (SaJa) network, which allows us to include information from all jets in the event, regardless of their number, eliminating the necessity to match the associated parton to the leading jet. The SaJa network with qg-discrimination variables has the best performance, giving expected upper limits on the branching ratios \({Br}(t \rightarrow qg)\) that are 25–35% lower than those from the BDT method.