4.4 Article

Confinement/deconfinement transition in the D0-brane matrix model - A signature of M-theory?

期刊

JOURNAL OF HIGH ENERGY PHYSICS
卷 -, 期 5, 页码 -

出版社

SPRINGER
DOI: 10.1007/JHEP05(2022)096

关键词

Gauge-Gravity Correspondence; Lattice Quantum Field Theory; Matrix Models

资金

  1. Deutsche Forschungsgemeinschaft (DFG) [BE 5942/3-1]
  2. International Junior Research Group grant of the Elite Network of Bavaria
  3. Nippon Telegraph and Telephone Corporation (NTT) Research
  4. STFC Ernest Rutherford Grant [ST/R003599/1]
  5. JSPS KAKENHI [JP 21J13014]

向作者/读者索取更多资源

In this study, we investigate the confinement/deconfinement transition in the DO-brane matrix model and its deformation numerically through lattice Monte Carlo simulations. Our findings confirm the general expectations from the dual string/M-theory picture for strong coupling. Furthermore, we suggest that these models offer an ideal framework for studying the Schwarzschild black hole, M-theory, and the parameter region of the phase transition between type IIA superstring theory and M-theory. Additionally, a detailed study of M-theory using lattice Monte Carlo simulations of the DO-brane matrix model may be feasible with smaller computational resources than previously anticipated.
We study the confinement/deconfinement transition in the DO-brane matrix model (often called the BFSS matrix model) and its one-parameter deformation (the BMN matrix model) numerically by lattice Monte Carlo simulations. Our results confirm general expectations from the dual string/M-theory picture for strong coupling. In particular, we observe the confined phase in the BFSS matrix model, which is a nontrivial consequence of the M-theory picture. We suggest that these models provide us with an ideal framework to study the Schwarzschild black hole, M-theory, and furthermore, the parameter region of the phase transition between type IIA superstring theory and M-theory. A detailed study of M-theory via lattice Monte Carlo simulations of the DO-brane matrix model might be doable with much smaller computational resources than previously expected.

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