Mapping out the thermodynamic stability of a QCD equation of state with a critical point using active learning

Abstract

The Beam Energy Scan Theory (BEST) collaboration's equation of state (EoS) incorporates a 3D Ising model critical point into the Quantum Chromodynamics (QCD) equation of state from lattice simulations. However, it contains 4 free parameters related to the size and location of the critical region in the QCD phase diagram. Certain combinations of the free parameters lead to acausal or unstable realizations of the EoS that should not be considered. In this work, we use an active learning framework to rule out pathological EoS efficiently. We find that checking stability and causality for a small portion of the parameters' range is sufficient to construct algorithms that perform with $>$96% accuracy across the entire parameter space. Though in this work we focus on a specific case, our approach can be generalized to any EoS containing a parameter space-class correspondence.

arXiv comment14 pages, 9 figures

References in corpus (41)

• The order of the quantum chromodynamics transition predicted by the standard model of particle physics
• The chiral and deconfinement aspects of the QCD transition
• Full result for the QCD equation of state with 2+1 flavors
• Signatures of the Tricritical Point in QCD
• A high-bias, low-variance introduction to Machine Learning for physicists
• Is there still any Tc mystery in lattice QCD? Results with physical masses in the continuum limit III
• The QCD transition temperature: results with physical masses in the continuum limit II.
• Chiral crossover in QCD at zero and non-zero chemical potentials
• Applying Bayesian parameter estimation to relativistic heavy-ion collisions: simultaneous characterization of the initial state and quark-gluon plasma medium
• On the sign of kurtosis near the QCD critical point
• Mapping the Phases of Quantum Chromodynamics with Beam Energy Scan
• The QCD Equation of State to $\mathcal{O}({\mu }_B^6)$ from Lattice QCD
• The QCD phase transition with physical-mass, chiral quarks
• The QCD crossover at finite chemical potential from lattice simulations
• Multi-system Bayesian constraints on the transport coefficients of QCD matter
• Fluctuations and correlations in high temperature QCD
• Non-monotonic energy dependence of net-proton number fluctuations
• Bayesian analysis of heavy ion collisions with the heavy ion computational framework Trajectum
• 3D Ising Model:The Scaling Equation of State
• Hydro+: hydrodynamics with parametric slowing down and fluctuations near the critical point
• Constraining the Eq. of State of Super-Hadronic Matter from Heavy-Ion Collisions
• Hydrodynamical evolution near the QCD critical end point
• The QCD equation of state at finite density from analytical continuation
• Nonequilibrium chiral fluid dynamics including dissipation and noise
• Diffusive dynamics of critical fluctuations near the QCD critical point
• Proton number fluctuations in $\sqrt{s_{NN}}$ = 2.4 GeV Au+Au collisions studied with HADES
• Flow and interferometry results from Au+Au collisions at $\sqrt{{\text{s}}_{NN}}$ = 4.5 GeV
• Future Physics Perspectives on the Equation of State from Heavy Ion Collisions to Neutron Stars
• A machine learning study to identify spinodal clumping in high energy nuclear collisions
• QCD Equation of State at Finite Chemical Potentials for Relativistic Nuclear Collisions
• Evolution of fluctuations near QCD critical point
• Evolution of Non-Gaussian Hydrodynamic Fluctuations
• Quartic cumulant of baryon number in the presence of QCD critical point
• Universality of the critical point mapping between Ising model and QCD at small quark mass
• Constraining the Parameters of High-Dimensional Models with Active Learning
• Lattice Constraints on the QCD Chiral Phase Transition at Finite Temperature and Baryon Density
• Critical lensing and kurtosis near a critical point in the QCD phase diagram in and out-of-equilibrium
• Self-learning Emulators and Eigenvector Continuation
• Estimation of Impact Parameter and Transverse Spherocity in heavy-ion collisions at the LHC energies using Machine Learning
• Picking the low-hanging fruit: testing new physics at scale with active learning
• Identifying Heavy-Flavor Jets Using Vectors of Locally Aggregated Descriptors

Cited by in corpus (6)

• Theoretical and Experimental Constraints for the Equation of State of Dense and Hot Matter
• Bayesian quantification of strongly-interacting matter with color glass condensate initial conditions
• Nonmonotonic specific entropy on the transition line near the QCD critical point
• Modern nuclear and astrophysical constraints of dense matter in a renormalized chiral approach
• Quantum Active Learning
• Active Learning in Physics: From 101, to Progress, and Perspective