9–12 Sept 2024
Imperial College London
Europe/London timezone

Precision-Machine Learning for the Matrix Element Method

Not scheduled
20m
Lecture Theatre 2, Blackett Laboratory (Imperial College London)

Lecture Theatre 2, Blackett Laboratory

Imperial College London

Poster

Speaker

Nathan Huetsch (Heidelberg University, ITP Heidelberg)

Description

The matrix element method is the LHC inference method of choice for limited statistics, as it allows for optimal use of available information. 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.

Primary Field of Research Particle Physics

Authors

Anja Butter (Centre National de la Recherche Scientifique (FR)) Nathan Huetsch (Heidelberg University, ITP Heidelberg) Ramon Winterhalder (UCLouvain) Theo Heimel (Heidelberg University) Tilman Plehn (Heidelberg University)

Presentation materials

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