Orateur
Description
The search for the Higgs boson decay to a pair of muons provides a direct probe of the Higgs coupling to second-generation fermions and represents an important test of the Standard Model. Due to the small branching fraction of this decay and the large backgrounds from Drell–Yan and other processes, achieving high sensitivity requires efficient exploitation of the kinematic and event-level information available in the data.
In this talk, we present a machine-learning-based strategy for the Higgs-to-two-muon analysis, employing both Boosted Decision Trees (BDTs) and Neural Network architectures to classify signal and background events. The resulting discriminants are used to categorise events into several mutually exclusive and collectively exhaustive categories, designed to target different production modes and regions of signal-to-background sensitivity. This categorisation enables the analysis to exploit complementary event topologies and kinematic information while maintaining statistically independent event samples.
The analysis is then performed simultaneously across all the different categories, combining their individual contributions to maximise the overall sensitivity to the Higgs-to-two-muon signal. We discuss the role of the machine-learning models in the event categorisation, the optimisation of the category scheme, and the impact of this approach on the sensitivity of the measurement.