A machine learning study to identify spinodal clumping in high energy nuclear collisions

Abstract

The coordinate and momentum space configurations of the net baryon number in heavy ion collisions that undergo spinodal decomposition, due to a first-order phase transition, are investigated using state-of-the-art machine-learning methods. Coordinate space clumping, which appears in the spinodal decomposition, leaves strong characteristic imprints on the spatial net density distribution in nearly every event which can be detected by modern machine learning techniques. On the other hand, the corresponding features in the momentum distributions cannot clearly be detected, by the same machine learning methods, in individual events. Only a small subset of events can be systematically differentiated if only the momentum space information is available. This is due to the strong similarity of the two event classes, with and without spinodal decomposition. In such scenarios, conventional event-averaged observables like the baryon number cumulants signal a spinodal non-equilibrium phase transition. Indeed the third-order cumulant, the skewness, does exhibit a peak at the beam energy (${\mathrm{E}}_{\mathrm{lab}}=3-4$ A GeV), where the transient hot and dense system created in the heavy ion collision reaches the first-order phase transition.

arXiv comment24 pages, 14 figures, accepted for publication by JHEP

References in corpus (33)

• Microscopic Models for Ultrarelativistic Heavy Ion Collisions
• The order of the quantum chromodynamics transition predicted by the standard model of particle physics
• Relativistic Hadron-Hadron Collisions in the Ultra-Relativistic Quantum Molecular Dynamics Model (UrQMD)
• Color superconductivity in dense quark matter
• Signatures of the Tricritical Point in QCD
• The QCD equation of state with dynamical quarks
• A high-bias, low-variance introduction to Machine Learning for physicists
• Phases of Dense Quarks at Large N_c
• Non-Gaussian fluctuations near the QCD critical point
• Collective Flow signals the Quark Gluon Plasma
• Search for the QCD Critical Point with Fluctuations of Conserved Quantities in Relativistic Heavy-Ion Collisions at RHIC : An Overview
• Relativistic Hydrodynamics for Heavy--Ion Collisions -- I. General Aspects and Expansion into Vacuum
• Particlization in hybrid models
• Antiflow of Nucleons at the Softest Point of the EoS
• Third flow component as QGP signal
• Spinodal amplification of density fluctuations in fluid-dynamical simulations of relativistic nuclear collisions
• Light nuclei production as a probe of the QCD phase diagram
• Hydrodynamics Near a Chiral Critical Point
• Density fluctuations in the presence of spinodal instabilities
• First-Order Chiral Phase Transition in High-Energy Collisions: Can Nucleation Prevent Spinodal Decomposition?
• Spinodal decomposition during the hadronization stage at RHIC?
• Equation of state dependence of directed flow in a microscopic transport model
• Deconfinement and chiral transition with the highly improved staggered quark (HISQ) action
• Formation of droplets with high baryon density at the QCD phase transition in expanding matter
• Particlization with local event-by-event conservation laws
• Density inhomogeneities in heavy ion collisions around the critical point
• Enhancement of elliptic flow can signal a first order phase transition in high energy heavy ion collisions
• Signals of spinodal phase decomposition in high-energy nuclear collisions
• The directed flow maximum near c_s=0
• Effect of finite particle number sampling on baryon number fluctuations
• The enhancement of $v_4$ in nuclear collisions at the highest densities signals a first order phase transition
• Event-by-Event Identified Particle Ratio Fluctuations in Pb-Pb Collisions with ALICE using the Identity Method
• The 3rd Flow Component as a QGP Signal

Cited by in corpus (31)

• Machine Learning in Nuclear Physics
• Machine learning the nuclear mass
• Exploring QCD matter in extreme conditions with Machine Learning
• Nuclear liquid-gas phase transition with machine learning
• Phase Transition Study meets Machine Learning
• QCD Equation of State of Dense Nuclear Matter from a Bayesian Analysis of Heavy-Ion Collision Data
• Unsupervised Outlier Detection in Heavy-Ion Collisions
• Quartic cumulant of baryon number in the presence of QCD critical point
• Machine-learning-based identification for initial clustering structure in relativistic heavy-ion collisions
• Application of machine learning in the determination of impact parameter in the ${}^{132}$Sn+${}^{124}$Sn system
• Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies
• Shared Data and Algorithms for Deep Learning in Fundamental Physics
• An equation-of-state-meter for CBM using PointNet
• Applications of deep learning to relativistic hydrodynamics
• Fourier-Flow model generating Feynman paths
• Dilepton Signature of a First-Order Phase Transition
• Mapping out the thermodynamic stability of a QCD equation of state with a critical point using active learning
• Continuous-mixture Autoregressive Networks for efficient variational calculation of many-body systems
• Enhanced yield ratio of light nuclei in heavy ion collisions with a first-order QCD phase transition
• Machine learning phase transitions of the three-dimensional Ising universality class
• Machine learning approach to analyze heavy quark diffusion coefficient in relativistic heavy-ion collisions
• Machine learning study to identify collective flow in small and large colliding systems
• Classification of Equation of State in Relativistic Heavy-Ion Collisions Using Deep Learning
• A Neural Network Perturbation Theory Based on the Born Series
• How machine learning conquers the unitary limit
• Mapping properties of the quark gluon plasma in Pb-Pb and Xe-Xe collisions at energies available at the CERN Large Hadron Collider
• Light-nuclei production in heavy-ion collisions within a thermodynamical approach
• Covariant formulation of spinodal decomposition in rapidly expanding quark gluon plasma
• Identifying weak critical fluctuations of intermittency in heavy-ion collisions with topological machine learning
• From Cosmic Matter to the Laboratory
• A deep learning approach for predicting multiple observables in Au+Au collisions at RHIC