Improving the $t\bar{t}t\bar{t}$ event selection with Graph Neural Networks in multilepton final states at the ATLAS detector

A study on the use of Graph Neural Networks for the selection of$t\bar{t}t\bar{t}$ events in the ATLAS detector is presented. Data used is the Monte-Carlo simulated proton-proton collision events at $\sqrt{s} = 13$ TeV. The analysis is only concerned with the same-sign multilepton channel. After optimization, GNNs achieved a performance of AUC = $0.8744 \pm 0.0017$ which is an improvement over the previous studies conducted on the same data using Boosted Decision Trees and Feedforward Neural Networks.