SciPost Submission Page
Forecasting Generative Amplification
by Henning Bahl, Sascha Diefenbacher, Nina Elmer, Tilman Plehn, Jonas Spinner
Submission summary
| Authors (as registered SciPost users): | Nina Elmer · Tilman Plehn · Jonas Spinner |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2509.08048v3 (pdf) |
| Code repository: | https://github.com/heidelberg-hepml/gan_estimate |
| Date submitted: | Oct. 17, 2025, 1:58 p.m. |
| Submitted by: | Jonas Spinner |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
|
| Approaches: | Computational, Phenomenological |
Abstract
Generative networks are perfect tools to enhance the speed and precision of LHC simulations. It is important to understand their statistical precision, especially when generating events beyond the size of the training dataset. We present two complementary methods to estimate the amplification factor without large holdout datasets. Averaging amplification uses Bayesian networks or ensembling to estimate amplification from the precision of integrals over given phase-space volumes. Differential amplification uses hypothesis testing to quantify amplification without any resolution loss. Applied to state-of-the-art event generators, both methods indicate that amplification is possible in specific regions of phase space, but not yet across the entire distribution.
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