Search for High-Mass Dijet Resonances Decaying into Top Quark Pairs using Machine Learning Techniques in the ATLAS Experiment

Abstract: This thesis presents an application of machine learning techniques in a search for a high-mass resonance particle decaying into top quark pairs in high energy dijet events using the ATLAS experiment at the Large Hadron Collider. Top tagging is applied to the dijet events to select the events with top signature and suppress the background, increasing the sensitivity of the search. All the data used in this work come from Monte Carlo simulations. Performance studies are carried out to compare four boosted top taggers available in the ATLAS framework: a conventional 2-variable tagger, two jet substructure-based machine learning tagger using Boosted Decision Tree (BDT) and Deep Neural Network (DNN), and the Topocluster Tagger which uses a DNN to process the kinematics of jets’ topoclusters. It is shown that the three machine learning taggers are capable of suppressing more background than the conventional 2-variable tagger by roughly a factor of two at 80% constant signal efficiency. The Topocluster Tagger is chosen to be applied to the dijet mass distribution to be analyzed. The effect of the tagging is studied by performing Sliding Window Fit (SWiFt) resonance search method to the distribution before and after top tagging. The method scans the dijet mass distribution in the range of 1100 - 6787 GeV, with the assumed integrated luminosity of 100 fb$^{-1}$. The search is conducted on two distributions: background-only distribution, and signal-injected distribution. The 95\% Confidence Level limit plots show an increase in the sensitivity of the search on background-only distribution. This is further confirmed in the signal-injected case where the method manages to pick up significant signal after top tagging, but not before.