Jet Diffusion versus JetGPT -- Modern Networks for the LHC
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917945097060352 |
|---|---|
| author | Butter, Anja Huetsch, Nathan Schweitzer, Sofia Palacios Plehn, Tilman Sorrenson, Peter Spinner, Jonas |
| author_facet | Butter, Anja Huetsch, Nathan Schweitzer, Sofia Palacios Plehn, Tilman Sorrenson, Peter Spinner, Jonas |
| contents | We introduce two diffusion models and an autoregressive transformer for LHC physics simulations. Bayesian versions allow us to control the networks and capture training uncertainties. After illustrating their different density estimation methods for simple toy models, we discuss their advantages for Z plus jets event generation. While diffusion networks excel through their precision, the transformer scales best with the phase space dimensionality. Given the different training and evaluation speed, we expect LHC physics to benefit from dedicated use cases for normalizing flows, diffusion models, and autoregressive transformers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_10475 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Jet Diffusion versus JetGPT -- Modern Networks for the LHC Butter, Anja Huetsch, Nathan Schweitzer, Sofia Palacios Plehn, Tilman Sorrenson, Peter Spinner, Jonas High Energy Physics - Phenomenology We introduce two diffusion models and an autoregressive transformer for LHC physics simulations. Bayesian versions allow us to control the networks and capture training uncertainties. After illustrating their different density estimation methods for simple toy models, we discuss their advantages for Z plus jets event generation. While diffusion networks excel through their precision, the transformer scales best with the phase space dimensionality. Given the different training and evaluation speed, we expect LHC physics to benefit from dedicated use cases for normalizing flows, diffusion models, and autoregressive transformers. |
| title | Jet Diffusion versus JetGPT -- Modern Networks for the LHC |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2305.10475 |