QCD Phase Diagram at finite Magnetic Field and Chemical Potential: A 
Holographic Approach Using Machine Learning

Cai, Rong-Gen; He, Song; Li, Li; Zeng, Hong-An

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QCD Phase Diagram at finite Magnetic Field and Chemical Potential: A Holographic Approach Using Machine Learning

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He, Song
Canonical and Covariant Dynamics of Quantum Gravity, AEI Golm, MPI for Gravitational Physics, Max Planck Society;

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2406.12772.pdf
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引用

Cai, R.-G., He, S., Li, L., & Zeng, H.-A. (in preparation). QCD Phase Diagram at finite Magnetic Field and Chemical Potential: A Holographic Approach Using Machine Learning.

引用: https://hdl.handle.net/21.11116/0000-000F-7422-8

要旨

By leveraging neural networks, we address the inverse problem of constructing
a quantitative 2+1-flavor holographic QCD model based on state-of-the-art
lattice QCD data. Our model demonstrates quantitative agreement with the latest
lattice QCD results. We construct the full phase diagram at finite magnetic
field $B$, baryon chemical potential $\mu_B$ and temperature $T$. We uncover
rich phase structure with a first-order phase transition surface and a critical
endpoint line within the 3-dimensional phase diagram. The critical endpoint at
vanishing chemical potential aligns with current speculations in the lattice
QCD literature. In particular, for large magnetic field, we find two critical
endpoints in the $T$-$\mu_B$ plane. The critical exponents of the critical
endpoints adhere to scaling relations and depend on the background magnetic
field. Moreover, they are exhibit deviations from mean-field theory,
highlighting the distinctive features of our holographic approach.