Speaker
Description
We present a novel machine learning framework for the characterization of leptoquark (LQ) signals at the Large Hadron Collider, focusing on the discrimination between scalar (SLQ) and vector (VLQ) hypotheses. The method is based on a two-stage inference pipeline that combines a classifier trained to separate Standard Model backgrounds from a mixed LQ signal with a second classifier designed to distinguish between SLQ and VLQ scenarios within mass-conditioned sectors. A test statistic is constructed from the classifier outputs and interpreted using reference probability density functions, allowing for the definition of a log-likelihood ratio and the corresponding statis- tical significance. The approach is applied to realistic LHC final states with hadronically decaying tau leptons, multiple jets, and missing transverse momentum, and its performance is assessed using simulated pseudo-experiments. We show that the proposed strategy provides a robust and statisti- cally consistent procedure to determine the spin nature of a potential LQ signal, enabling efficient discrimination between SLQ and VLQ scenarios across a wide range of masses and signal strengths.