Maximizing Sensitivity for LHC Searches for vector Leptoquarks using Machine Learning Techniques

In the quest for uncovering new physics phenomena, the Large Hadron Collider (LHC) has been at the forefront of experimental research. Leptoquarks, hypothetical particles that bridge the gap between leptons and quarks, hold great promise as potential indicators of Beyond Standard Model physics. This presentation delves into the innovative integration of machine learning methods to enhance the sensitivity of LHC searches for U_1 leptoquarks. By harnessing the power of advanced data analysis techniques, we aim to optimize the identification and discrimination of signal events from background.