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Related papers: A Gaussian Process Generative Model for QCD Equati…

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We present realistic equations of state for QCD matter at vanishing net-baryon density which embed recent lattice QCD results at high temperatures combined with a hadron resonance gas model in the low-temperature, confined phase. In the…

High Energy Physics - Phenomenology · Physics 2014-07-17 M. Bluhm , P. Alba , W. Alberico , A. Beraudo , C. Ratti

We employ the lattice QCD data on Taylor expansion coefficients to extend our previous parametrization of the equation of state to finite baryon density. When we take into account lattice spacing and quark mass dependence of the hadron…

Nuclear Theory · Physics 2017-11-22 Pasi Huovinen , Peter Petreczky , Christian Schmidt

We review the equation of state of QCD matter at finite densities. We discuss the construction of the equation of state with net baryon number, electric charge, and strangeness using the results of lattice QCD simulations and hadron…

Nuclear Theory · Physics 2021-04-21 Akihiko Monnai , Björn Schenke , Chun Shen

We present a four-dimensional equation of state for strongly interacting matter at finite temperature and conserved charge densities, constructed using a deep neural network. It is designed for direct use in hybrid models of relativistic…

High Energy Physics - Phenomenology · Physics 2026-05-22 Musfer Adzhymambetov

Based on the quasi-particle description of the QCD medium at finite temperature and density we formulate the phenomenological model for the equation of state that exhibits crossover or the first order deconfinement phase transition. The…

Nuclear Theory · Physics 2008-11-26 A. S. Khvorostukhin , V. V. Skokov , V. D. Toneev , K. Redlich

The properties of dense QCD matter are delineated through the construction of equations of state which should be consistent with QCD calculations in the low and high density limits, nuclear laboratory experiments, and the neutron star…

High Energy Astrophysical Phenomena · Physics 2019-09-04 Toru Kojo

Generation of high fidelity photonic non-Gaussian states is a crucial ingredient for universal quantum computation using continous-variable platforms, yet it remains a challenge to do so efficiently. We present a general framework for a…

Quantum Physics · Physics 2019-11-06 Daiqin Su , Casey R. Myers , Krishna Kumar Sabapathy

The QCD equation of state at finite temperature and densities of conserved charges is considered in the framework of a Hagedorn bag-like model, incorporating both the finite sizes of hadrons as well as their exponential mass spectrum.…

Nuclear Theory · Physics 2019-11-18 Volodymyr Vovchenko , Mark I. Gorenstein , Carsten Greiner , Horst Stoecker

We present a mean-field model of the dense nuclear matter equation of state designed for use in computationally demanding hadronic transport simulations. Our approach, based on the relativistic Landau Fermi-liquid theory, allows us to…

Nuclear Theory · Physics 2021-09-17 Agnieszka Sorensen

Gaussian processes provide a promising framework by which to extrapolate the equation of state (EoS) of cold, catalyzed matter beyond $1-2$ times nuclear saturation density. Here we discuss how to extend Gaussian processes to include…

High Energy Astrophysical Phenomena · Physics 2023-02-17 Debora Mroczek , M. Coleman Miller , Jacquelyn Noronha-Hostler , Nicolas Yunes

It has been suggested that for QCD at finite baryon density the distribution of the phase angle, i.e. the angle defined as the imaginary part of the logarithm of the fermion determinant, has a simple Gaussian form. This distribution…

High Energy Physics - Lattice · Physics 2015-06-17 Jeff Greensite , Joyce C. Myers , K. Splittorff

We present a Bayesian analysis to constrain the equation of state of dense nucleonic matter by exploiting the available data from symmetric nuclear matter at saturation and from observations of compact X-ray sources and from the…

High Energy Astrophysical Phenomena · Physics 2020-07-23 Silvia Traversi , Prasanta Char , Giuseppe Pagliara

We present a supervised machine learning-based method using convolutional neural networks to estimate the covariance matrix of Gaussian quantum states in the presence of thermal noise. Unlike computationally intensive density matrix…

Capturing the correlation emerging between constituents of many-body systems accurately is one of the key challenges for the appropriate description of various systems whose properties are underpinned by quantum mechanical fundamentals.…

Quantum Physics · Physics 2023-08-17 Yannic Rath

Exploration of the QCD phase diagram is pivotal in particle and nuclear physics. We construct a full four-dimensional equation of state of QCD with net baryon, electric charge, and strangeness by extending the NEOS model beyond the…

Nuclear Theory · Physics 2025-03-06 Akihiko Monnai , Grégoire Pihan , Björn Schenke , Chun Shen

The quantum statistical Clausius-based equation of state is used to describe the system of interacting nucleons. The interaction parameters $a$, $b$, and $c$ of the model are fixed by the empirically known nuclear ground state properties…

Nuclear Theory · Physics 2018-04-04 Volodymyr Vovchenko , Mark I. Gorenstein , Horst Stoecker

The gauge/gravity duality, combined with information from lattice QCD, nuclear theory, and perturbative QCD, can be used to constrain the equation of state of hot and dense QCD. I discuss an approach based on the holographic V-QCD model. I…

High Energy Physics - Phenomenology · Physics 2023-07-06 Matti Jarvinen

We employ the lattice QCD data on Taylor expansion coefficients to extend our previous parametrization of the equation of state to finite baryon density. When we take into account lattice spacing and quark mass dependence of the hadron…

Nuclear Theory · Physics 2015-05-28 Pasi Huovinen , Peter Petreczky

We derive robust bounds on the equation of state (EoS) at finite baryon chemical potential using QCD inequalities and input from recent lattice-QCD calculations of thermodynamic properties of matter at nonzero isospin chemical potential. We…

Nuclear Theory · Physics 2023-10-17 Yuki Fujimoto , Sanjay Reddy

This paper is concerned with a state-space approach to deep Gaussian process (DGP) regression. We construct the DGP by hierarchically putting transformed Gaussian process (GP) priors on the length scales and magnitudes of the next level of…

Machine Learning · Statistics 2021-09-24 Zheng Zhao , Muhammad Emzir , Simo Särkkä
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