Study of enhancing the signal-background separation for the Higgs boson production in association with a single top-quark in $pp$ collisions at the LHC

In this paper, a multivariate analysis based approach for enhancing the extraction of the rare Standard Model (SM) process being studied - Higgs boson production in association with a single top quark (tH) - from the prevailing background processes is presented. The analysis was performed using a Monte Carlo generated dataset simulating the outcomes of proton-proton collisions at the Large Hadron Collider (LHC). A deep fully connected neural network was developed for signal-background discrimination, incorporating a masking mechanism designed to suppress the contribution of input variables associated with objects that were not reconstructed in a given event. The performance of the masked architecture was evaluated on independent Monte Carlo samples. The results of the proposed methodology are compared with a conventional cut-based approach in terms of the statistical significance of the tH production channel. The developed framework was tested using a simulated dataset corresponding approximately to the combined statistical samples of LHC Runs 1, 2, and 3.