Ultra fast, event-by-event heavy-ion simulations for next generation experiments

TL;DR

This paper introduces a deep generative diffusion framework for ultra fast, event-by-event heavy-ion collision simulations, accurately reproducing complex particle distributions and enabling efficient inverse problem solving.

Contribution

A novel diffusion-based generative model trained on UrQMD data that produces detailed collision event outputs as point clouds, preserving event granularity and accelerating simulations.

Findings

Successfully reproduces UrQMD distributions for multiple hadron types.

Generates realistic collision events with full event-level detail.

Enables rapid event simulation and inverse problem applications.

Abstract

We present a novel deep generative framework that uses probabilistic diffusion models for ultra fast, event-by-event simulations of heavy-ion collision output. This new framework is trained on UrQMD cascade data to generate a full collision event output containing 26 distinct hadron species. The output is represented as a point cloud, where each point is defined by a particle's momentum vector and its corresponding species information (ID). Our architecture integrates a normalizing flow-based condition generator that encodes global event features into a latent vector, and a diffusion model that synthesizes a point cloud of particles based on this condition. A detailed description of the model and an in-depth analysis of its performance is provided. The conditional point cloud diffusion model learns to generate realistic output particles of collision events which successfully reproduce the UrQMD distributions for multiplicity, momentum and rapidity of each hadron type. The flexible point cloud representation of the event output preserves full event-level granularity, enabling direct application to inverse problems and parameter estimation tasks while also making it easily adaptable for accelerating any event-by-event model calculation or detector simulation.