Event categorisation and Machine-learning Techniques in Searches for Higgs Boson Pairs in the ATLAS Experiment at the LHC

Abstract: This thesis investigates the pair production of Higgs bosons (di-Higgs events) at the ATLAS experiment in the Large Hadron Collider (LHC), focusing on the channel where one Higgs boson decays into two bottom quarks and the other decays into two tau leptons. The main objective was to determine whether introducing a split in the invariant mass of the decay products from the two Higgs bosons (the di-Higgs mass) and using this as an analysis variable improves the sensitivity of the Boosted Decision Tree (BDT) machine learning algorithm to the di-Higgs signal. A mass split was performed at 350 GeV, and the BDT algorithm was trained on both the split and un-split data sets, where the split data set included a high-mass region (di-Higgs mass above 350 GeV) using the Standard Model Higgs boson coupling constant of 1 and a low-mass region (di-Higgs mass below 350 GeV) using the enhanced coupling constant of 10 to create a low-mass region more sensitive to the signal.  The results showed that the BDT algorithm training performed on the split data set provided a 3.6% improvement in the exclusion limits, indicating an improvement in the algorithm's sensitivity to the di-Higgs signal compared to the training performed on the un-split data set. This finding suggests that the introduction of a split at 350 GeV can enhance the accuracy and efficiency of machine learning algorithms in detecting di-Higgs boson production at the LHC.  The improvement in sensitivity was attributed to the enhanced discrimination between signal and background events provided by the split in the di-Higgs mass analysis variable. The improved separation between the signal and background events lead to a higher signal-to-background ratio and a corresponding increase in the BDT algorithm's sensitivity to the di-Higgs signal.  In conclusion, this thesis provided evidence that introducing a split in the di-Higgs mass analysis variable can improve the sensitivity of machine learning algorithms to the di-Higgs signal in the channel where one Higgs boson decays into two bottom quarks and the other into two tau particles. This finding has important implications for future research on di-Higgs boson production at the LHC and could lead to more accurate and efficient detection of this rare and important process.