7th Inter-Experimental LHC Machine Learning Workshop

Constraining the Higgs trilinear self-coupling from off-shell production using neural simulation-based inference

Not scheduled

20m

61/1-201 - Pas perdus - Not a meeting room - (CERN)

Poster 2 ML for analysis: Event classification, statistical analysis and inference, anomaly detection Poster Session

Speaker

Tae Hyoun Park (Max Planck Society (DE))

Description

Experimental verification of the Higgs trilinear self-coupling is one of the next major challenges of particle physics. While prospects from proton-proton collisions have centred around measuring the on-shell single- and di-Higgs production processes, the off-shell Higgs production process has also been suggested as a complementary channel to resolve the degeneracy in Higgs couplings. We employ neural simulation-based inference (NSBI) to evaluate an expansion of the likelihood ratio that fully describes the Higgs signal under effective field theory modifications to the self-coupling, relevant backgrounds, and possible quantum interference effects between them in the $pp\to ZZ$ process. We demonstrate that NSBI approaches the best possible sensitivity and report the expected constraint at the High-Luminosity Large Hadron Collider.