Precision-machine learning for the matrix element method - Theo Heimel, Nathan Huetsch, Ramon Winterhalder, Tilman Plehn, Anja Butter (SciPost Physics, 2024)
The matrix element method is the LHC inference method of choice for limited statistics. We present a dedicated machine learning framework, based on efficient phase-space integration, a learned acceptance and transfer function. It is based on a choice of INN and diffusion networks, and a transformer to solve jet combinatorics. We showcase this setup for the CP-phase of the top Yukawa coupling in associated Higgs and single-top production.