tZ FCNC Case study: LLM Application in signal/Background discrimination analyses in Particle Physics

Abstract

We present a case study exploring the potential of OpenAI's o3 model as a process-agnostic tool (within fixed topology) for predicting optimized selection cuts in high-energy physics analyses. Specifically, we investigate the effectiveness of the model in separating signal from relevant Standard Model backgrounds in the context of Flavour-Changing Neutral Current (FCNC) top-quark couplings, focusing on the rare decay process t \rightarrow uZ. The study is performed at the Future Circular Collider in hadron-hadron mode (FCC-hh) setup. We prompt the o3 model on detector level data for signal and background processes to predict selection cuts that enhance Signal-to-Background (S/B) discrimination. A comparative analysis is then carried out between the efficiencies resulting from o3-predicted cuts, TMVA BDT separation and those derived from traditional manually designed strategies used by a control group; all utilizing the same parameters for the sake of the integrity of the study. Our results demonstrate that the o3 model performs a degree better than the control group, suggesting its promise as a fast and generalizable tool for new physics searches. Meanwhile, BDT results were considerably higher than both the o3 and the traditional cut-based methods. Furthermore, in order to test its limitations, o3 cuts were applied to data generated via the approved High-Luminosity Large Hadron Collider (HL-LHC) and the proposed High-Energy LHC (HE-LHC) setup in order to examine its effectiveness at different energy scales when provided with data at FCC energies.