TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.17.5.129
TI  - Precision-machine learning for the matrix element method
PY  - 2024/11/08
UR  - https://scipost.org/SciPostPhys.17.5.129
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 17
IS  - 5
SP  - 129
A1  - Heimel, Theo
AU  - Huetsch, Nathan
AU  - Winterhalder, Ramon
AU  - Plehn, Tilman
AU  - Butter, Anja
AB  - 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.
ER  -

@Article{10.21468/SciPostPhys.17.5.129,
	title={{Precision-machine learning for the matrix element method}},
	author={Theo Heimel and Nathan Huetsch and Ramon Winterhalder and Tilman Plehn and Anja Butter},
	journal={SciPost Phys.},
	volume={17},
	pages={129},
	year={2024},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.17.5.129},
	url={https://scipost.org/10.21468/SciPostPhys.17.5.129},
}