Investigating the Violation of Charge-parity Symmetry Through Top-quark ChromoElectric Dipole Moments by Using Machine Learning Techniques

There are a number of studies in the literature on the search for Charge-Parity (CP) violating signals in top-quark productions at the LHC. In most of these studies, ChromoMagnetic Dipole Moments (CMDM) and ChromoElectric Dipole Moments (CEDM) of top quarks are bounded either by deviations from the Standard Model (SM) cross sections or by T-odd asymmetries in di-muon channels. However, the required precision on these cross section values is far beyond from that ATLAS or CMS experiments can reach. In this letter, the investigation of CEDM-based asymmetries in the semileptonic top-pair decays is presented as T-odd asymmetries in the CMS experiment. Expected asymmetry values are determined at the detector level using MadGraph5, Pythia 8, and Delphes softwares along with the discrimination of the signal and the background with Deep Neural Networks (DNN).