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

TL;DR

This paper introduces a generative diffusion model that simulates ultra-relativistic heavy-ion collisions end-to-end, accurately reproducing complex observables and fluctuations, offering an efficient tool for high-energy physics research.

Contribution

It presents the first end-to-end generative diffusion model for heavy-ion collisions, capturing complex relationships and fluctuations in particle spectra from initial conditions.

Findings

Successfully reproduces integrated and differential observables

Captures higher-order fluctuations and correlations

Learns complex initial-to-final state relationships

Abstract

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.