An end-to-end generative diffusion model for heavy-ion collisions

Autor:	Sun, Jing-An, Yan, Li, Gale, Charles, Jeon, Sangyong
Rok vydání:	2024
Předmět:	Nuclear Theory High Energy Physics - Phenomenology Nuclear Experiment
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.13069 Zobrazit plný text záznamu View this record from Arxiv