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Thermodynamic and transport properties of the quark-gluon plasma at finite chemical potential with a deep neural network framework

Rishabh Kumar Tiwari1, Kangkan Goswami1, Suraj Prasad1,2, Captain R. Singh1, Raghunath Sahoo1,*, and Mohammad Yousuf Jamal3

  • *Contact author: Raghunath.Sahoo@cern.ch
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Phys. Rev. D 114, 074015 – Published 6 October, 2026

DOI: https://doi.org/10.1103/xfl6-4t69

Abstract

The characteristics of a thermal system depend strongly on its response to thermal gradients and the underlying microscopic interactions among constituents. In the present study, we investigate the thermodynamic and transport properties of the quark-gluon plasma (QGP) at finite baryon chemical potential within a deep-learning-assisted quasiparticle model. The temperature- (T) and baryon chemical-potential- (μB) dependent thermal masses of quasiparticles are estimated using neural networks trained to reproduce lattice QCD results for the equation of state, obtained via a Taylor-like expansion around vanishing baryon chemical potential. The trained model acts as an effective emulator, enabling us to estimate the thermodynamic and transport properties at finite μB. We compute the speed of sound, specific heat, viscosity, and conductivity of the deconfined medium. Our findings are in good agreement with available lattice calculations and other phenomenological models. The present study demonstrates that a deep-neural-network-based approach provides an efficient framework for studying the properties of the QGP at finite baryon density.

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Phys. Rev. D 114, 074015– Published 6 October, 2026
  • Received 8 April 2026
  • Accepted 11 September 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.

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