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Precision-Machine Learning for the Matrix Element Method
by Theo Heimel, Nathan Huetsch, Ramon Winterhalder, Tilman Plehn, Anja Butter
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Submission summary
| Authors (as registered SciPost users): | Theo Heimel · Nathan Huetsch · Tilman Plehn · Ramon Winterhalder |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2310.07752v3 (pdf) |
| Date accepted: | Oct. 21, 2024 |
| Date submitted: | Oct. 4, 2024, 9:51 a.m. |
| Submitted by: | Ramon Winterhalder |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approaches: | Computational, Phenomenological |
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.
Author indications on fulfilling journal expectations
- Provide a novel and synergetic link between different research areas.
- Open a new pathway in an existing or a new research direction, with clear potential for multi-pronged follow-up work
- Detail a groundbreaking theoretical/experimental/computational discovery
- Present a breakthrough on a previously-identified and long-standing research stumbling block
Published as SciPost Phys. 17, 129 (2024)