Studies for the efficiency and performance of the triggers and the dilepton cuts as a pre-selection stage in the ttHH production mode.

The exploration of the Higgs self-coupling could give a promising way to answer some fundamental questions within the framework of the Standard Model, providing a better comprehension of the model. Additionally, studying the way in which the Higgs boson couples with other particles, particularly with gauge bosons, serves as a crucial way towards unraveling the mysteries underlying the electroweak symmetry breaking mechanism and the observable asymmetry between matter-antimatter. In this written report, we study the efficiency of leptons and multijets triggers, which are characteristic objects for the $ttHH$ process in its final state. This process, which is relatively unexplored at LHC, has the possibility to enable the simultaneous study for both the $t$ quark and Higgs sectors. The $ttHH$ channel considers each Higgs boson decaying into two quarks b while the two W bosons (coming from the top quarks decays) are allowed to decay leptonically, hadronically or semileptonically (one W boson decays leptonically and the other one does so hadronically). In order to optimize the analysis, we consider the number of accepted events and the significance values derived from applying all the triggers to both signal and background MC samples. By identifying the regions for high overlapping of events being triggered by certain groups of triggers, we choose the triggers that boosts the significance while preserves a substantial quantity of signal events, prioritizing the statistic robustness of signal events for future pre-selection stages in the analysis. Subsequently, a loose pre-selection criterion is applied to the events passing the triggers. This approach focuses around the use of dilepton cuts, where the main requirement is to select events with only two leptons in the final state, and is carried out to determine the behavior of the distributions for some kinematic variables, as well as the identification of some possible inconsistencies within the dataset used.