SciPost Submission Page
Differentiable MadNIS-Lite
by Theo Heimel, Olivier Mattelaer, Tilman Plehn, Ramon Winterhalder
Submission summary
| Authors (as registered SciPost users): | Ramon Winterhalder |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2408.01486v1 (pdf) |
| Date submitted: | 2024-08-19 17:16 |
| Submitted by: | Winterhalder, Ramon |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approach: | Computational |
Abstract
Differentiable programming opens exciting new avenues in particle physics, also affecting future event generators. These new techniques boost the performance of current and planned MadGraph implementations. Combining phase-space mappings with a set of very small learnable flow elements, MadNIS-Lite, can improve the sampling efficiency while being physically interpretable. This defines a third sampling strategy, complementing VEGAS and the full MadNIS.
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:
In refereeing