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

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.

Matches published version. 17 pages, 9 figures

References in corpus (52)

• Microscopic Models for Ultrarelativistic Heavy Ion Collisions
• The order of the quantum chromodynamics transition predicted by the standard model of particle physics
• Diffusion Models in Vision: A Survey
• Relativistic Hadron-Hadron Collisions in the Ultra-Relativistic Quantum Molecular Dynamics Model (UrQMD)
• The equation of state in (2+1)-flavor QCD
• The chiral and deconfinement aspects of the QCD transition
• Signatures of the Tricritical Point in QCD
• The phase diagram of dense QCD
• The QCD equation of state with dynamical quarks
• Bulk Properties of the Medium Produced in Relativistic Heavy-Ion Collisions from the Beam Energy Scan Program
• The QCD Equation of State with almost Physical Quark Masses
• Applying Bayesian parameter estimation to relativistic heavy-ion collisions: simultaneous characterization of the initial state and quark-gluon plasma medium
• 2+1 Flavor Lattice QCD toward the Physical Point
• Collective Flow signals the Quark Gluon Plasma
• Fully integrated transport approach to heavy ion reactions with an intermediate hydrodynamic stage
• Parton-Hadron-String Dynamics: an off-shell transport approach for relativistic energies
• Study of relativistic nuclear collisions at AGS energies from p+Be to Au+Au with hadronic cascade model
• Challenges in QCD matter physics - The Compressed Baryonic Matter experiment at FAIR
• The High-Acceptance Dielectron Spectrometer HADES
• Constraining Neutron-Star Matter with Microscopic and Macroscopic Collisions
• Multi-system Bayesian constraints on the transport coefficients of QCD matter
• Bayesian analysis of heavy ion collisions with the heavy ion computational framework Trajectum
• Constraining the Eq. of State of Super-Hadronic Matter from Heavy-Ion Collisions
• Regressive and generative neural networks for scalar field theory
• Score-based Generative Models for Calorimeter Shower Simulation
• Proton number fluctuation as a signal of the QCD critical end-point
• Exploring QCD matter in extreme conditions with Machine Learning
• Fast Point Cloud Generation with Diffusion Models in High Energy Physics
• Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning
• CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation
• Mean-field update in the JAM microscopic model: Mean-field effects on collective flow in high-energy heavy-ion collisions at $\sqrt{s_{NN}}=2-20$ GeV energies
• Sensitivity of Au+Au collisions to the symmetric nuclear matter equation of state at 2 -- 5 nuclear saturation densities
• EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
• PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
• Microscopic models and effective equation of state in nuclear collisions at FAIR energies
• Deep learning stochastic processes with QCD phase transition
• QCD Equation of State of Dense Nuclear Matter from a Bayesian Analysis of Heavy-Ion Collision Data
• A fast centrality-meter for heavy-ion collisions at the CBM experiment
• Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
• Deep learning jet modifications in heavy-ion collisions
• CaloScore v2: Single-shot Calorimeter Shower Simulation with Diffusion Models
• Determining the temperature in heavy-ion collisions with multiplicity distribution
• Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies
• Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
• PC-Droid: Faster diffusion and improved quality for particle cloud generation
• Diffusion Models as Stochastic Quantization in Lattice Field Theory
• An equation-of-state-meter for CBM using PointNet
• Applications of deep learning to relativistic hydrodynamics
• Examination of nucleon distribution with Bayesian imaging for isobar collisions
• Machine learning study to identify collective flow in small and large colliding systems
• Effectiveness of denoising diffusion probabilistic models for fast and high-fidelity whole-event simulation in high-energy heavy-ion experiments
• Toward a foundation model for heavy-ion collision experiments based on point-cloud diffusion

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