Orateur
Description
Exploring exotic Higgs boson decays often requires access to challenging regions of phase space where standard reconstruction techniques become limited, making potential signals effectively invisible. In particular, when decay products become highly collimated and overlap in the calorimeter, conventional algorithms lose sensitivity. We present a novel machine learning based technique for reconstructing the decays of highly Lorentz-boosted particles directly from raw detector information. We use a Graph Neural Network to directly reconstruct exotic light particle decays to photons using minimally processed calorimeter hits, bypassing conventional reconstruction steps. The method is applied in the CMS search for exotic Higgs boson decays H→AA→4γ, where the decay of a light pseudoscalar A→γγ often produces complicated photon decay patterns. The model significantly improves mass reconstruction in the semi-merged regime and achieves sensitivity beyond traditional techniques, enabling stronger constraints than comparable analyses. In this talk we discuss the challenges of reconstructing such exotic decays, the model developed for this purpose, training strategies and the results.
