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.
Precision-machine learning for the matrix element method / T. Heimel, N. Huetsch, R. Winterhalder, T. Plehn, A. Butter. - In: SCIPOST PHYSICS. - ISSN 2542-4653. - 17:5(2024 Nov), pp. 129.1-129.33. [10.21468/SciPostPhys.17.5.129]
Precision-machine learning for the matrix element method
R. Winterhalder;
2024
Abstract
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.
| File | Dimensione | Formato | |
|---|---|---|---|
|
SciPostPhys_17_5_129.pdf
accesso aperto
Tipologia:
Publisher's version/PDF
Licenza:
Creative commons
Dimensione
1.79 MB
Formato
Adobe PDF
|
1.79 MB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
