Fast prediction of the hydrodynamic QGP evolution in ultra-relativistic heavy-ion collisions using Fourier Neural Operators

Abstract

Recent research in machine learning has employed neural networks to learn mappings between function spaces on bounded domains termed ``neural operators''. As such, these operators can provide alternatives to standard numerical methods for partial differential equation (PDE) solutions. In particular, the Fourier Neural Operator (FNO) has been shown to map solutions for classical fluid flow problems with accuracy competitive with traditional PDE solvers and with much greater computing speed. This paper explores the first application of FNOs to model ultra-relativistic hydrodynamic flow of the quark-gluon plasma (QGP) generated in relativistic heavy-ion collisions. The application in ultra-relativistic flow is novel relative to classical flow, due to the hydrodynamic evolution of the QGP occurring in femtometer-scaled explosions characterized by rapid expansion cooling. In this study we investigate the applicability of FNOs as computationally fast alternatives to standard numerical PDE solvers. The FNO predictions are evaluated by comparing to standard PDE solutions, using \MUSIC in the \JETSCAPE Monte Carlo event generator framework. The performance of calculating established experimental observables for flow and jet quenching using FNOs in the MC framework are also reported.

arXiv comment15 pages, 11 figures, submitted to Physics Review C, 9 pages of appendices, 9 figures in appendices

References in corpus (25)

• The anti-k_t jet clustering algorithm
• Experimental and Theoretical Challenges in the Search for the Quark Gluon Plasma: The STAR Collaboration's Critical Assessment of the Evidence from RHIC Collisions
• Formation of dense partonic matter in relativistic nucleus-nucleus collisions at RHIC: Experimental evaluation by the PHENIX collaboration
• Quark Gluon Plasma an Color Glass Condensate at RHIC? The perspective from the BRAHMS experiment
• The PHOBOS Perspective on Discoveries at RHIC
• Glauber Modeling in High Energy Nuclear Collisions
• Elliptic flow of charged particles in Pb-Pb collisions at 2.76 TeV
• Heavy Ion Collisions: The Big Picture, and the Big Questions
• Elliptic and triangular flow in event-by-event (3+1)D viscous hydrodynamics
• Observation of a Centrality-Dependent Dijet Asymmetry in Lead-Lead Collisions at sqrt(S(NN))= 2.76 TeV with the ATLAS Detector at the LHC
• Observation and studies of jet quenching in PbPb collisions at nucleon-nucleon center-of-mass energy = 2.76 TeV
• 3+1D hydrodynamic simulation of relativistic heavy-ion collisions
• EPPS16: Nuclear parton distributions with LHC data
• The production of photons in relativistic heavy-ion collisions
• The initial energy density of gluons produced in very high energy nuclear collisions
• Multi-system Bayesian constraints on the transport coefficients of QCD matter
• Dynamical initial state model for relativistic heavy-ion collisions
• Net baryon diffusion in fluid dynamic simulations of relativistic heavy-ion collisions
• Heavy quarks and jets as probes of the QGP
• LHC Forward Physics
• The hadronic nucleus-nucleus cross section and the nucleon size
• Applications of deep learning to relativistic hydrodynamics
• Probing QGP with flow: An experimental overview
• Towards the understanding of heavy quarks hadronization: from leptonic to heavy-ion collisions
• Neural network biased corrections: Cautionary study in background corrections for quenched jets

Cited by in corpus (1)

• Neural network enhanced Bayesian global analysis of relativistic heavy ion collisions