SciPost Submission Page
Precision-Machine Learning for the Matrix Element Method
by Theo Heimel, Nathan Huetsch, Ramon Winterhalder, Tilman Plehn, Anja Butter
Submission summary
Authors (as registered SciPost users): | Nathan Huetsch · Tilman Plehn · Ramon Winterhalder |
Submission information | |
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Preprint Link: | https://arxiv.org/abs/2310.07752v3 (pdf) |
Date accepted: | 2024-10-21 |
Date submitted: | 2024-10-04 09:51 |
Submitted by: | Winterhalder, Ramon |
Submitted to: | SciPost Physics |
Ontological classification | |
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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
Current status:
Accepted in target Journal
Editorial decision:
For Journal SciPost Physics: Publish
(status: Editorial decision fixed and (if required) accepted by authors)