Anja Butter, Fabio Catalano(University and INFN Torino (IT)), Lorenzo Moneta(CERN), Michael Kagan(SLAC National Accelerator Laboratory (US)), DrPietro Vischia(Universite Catholique de Louvain (UCL) (BE)), Simon Akar(University of Cincinnati (US)), Stefano Carrazza(CERN)
Two Invertible Networks for the Matrix Element Method25m
The matrix element method is widely considered the ultimate LHC inference tool for small event numbers, but computationally expensive. We show how a combination of two conditional generative neural networks encodes the QCD radiation and detector effects without any simplifying assumptions and allows us to efficiently compute the likelihood for individual hard-scattering events. We illustrate our approach for the CP-violating phase of the top Yukawa coupling in associated Higgs and single-top production. The limiting factor for the precision of our approach is jet combinatorics.