Precision-Machine Learning for the Matrix Element Method
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2023
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866929588057145344 |
|---|---|
| author | Heimel, Theo Huetsch, Nathan Winterhalder, Ramon Plehn, Tilman Butter, Anja |
| author_facet | Heimel, Theo Huetsch, Nathan Winterhalder, Ramon Plehn, Tilman Butter, Anja |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_07752 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Precision-Machine Learning for the Matrix Element Method Heimel, Theo Huetsch, Nathan Winterhalder, Ramon Plehn, Tilman Butter, Anja High Energy Physics - Phenomenology 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. |
| title | Precision-Machine Learning for the Matrix Element Method |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2310.07752 |