An end-to-end generative diffusion model for heavy-ion collisions
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917005648461824 |
|---|---|
| author | Sun, Jing-An Yan, Li Gale, Charles Jeon, Sangyong |
| author_facet | Sun, Jing-An Yan, Li Gale, Charles Jeon, Sangyong |
| contents | We train a generative diffusion model (DM) to simulate ultra-relativistic heavy-ion collisions from end to end. The model takes initial entropy density profiles as input and produces two-dimensional final particle spectra, successfully reproducing integrated and differential observables. It also captures higher-order fluctuations and correlations. These findings suggest that the generative model has successfully learned the complex relationship between initial conditions and final particle spectra for various shear viscosities, as well as the fluctuations introduced during initial entropy production and hadronization stages, providing an efficient framework for resource-intensive physical goals. The code and trained model are available at https://huggingface.co/Jing-An/DiffHIC/tree/main. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13069 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An end-to-end generative diffusion model for heavy-ion collisions Sun, Jing-An Yan, Li Gale, Charles Jeon, Sangyong Nuclear Theory High Energy Physics - Phenomenology Nuclear Experiment We train a generative diffusion model (DM) to simulate ultra-relativistic heavy-ion collisions from end to end. The model takes initial entropy density profiles as input and produces two-dimensional final particle spectra, successfully reproducing integrated and differential observables. It also captures higher-order fluctuations and correlations. These findings suggest that the generative model has successfully learned the complex relationship between initial conditions and final particle spectra for various shear viscosities, as well as the fluctuations introduced during initial entropy production and hadronization stages, providing an efficient framework for resource-intensive physical goals. The code and trained model are available at https://huggingface.co/Jing-An/DiffHIC/tree/main. |
| title | An end-to-end generative diffusion model for heavy-ion collisions |
| topic | Nuclear Theory High Energy Physics - Phenomenology Nuclear Experiment |
| url | https://arxiv.org/abs/2410.13069 |