Bootstraps to Strings: Solving Random Matrix Models with Positivity

Abstract

A new approach to solving random matrix models directly in the large $N$ limit is developed. First, a set of numerical values for some low-pt correlation functions is guessed. The large $N$ loop equations are then used to generate values of higher-pt correlation functions based on this guess. Then one tests whether these higher-pt functions are consistent with positivity requirements, e.g., $⟨\text{tr }{M}^{2k}⟩\ge 0$. If not, the guessed values are systematically ruled out. In this way, one can constrain the correlation functions of random matrices to a tiny subregion which contains (and perhaps converges to) the true solution. This approach is tested on single and multi-matrix models and handily reproduces known solutions. It also produces strong results for multi-matrix models which are not believed to be solvable. A tantalizing possibility is that this method could be used to search for new critical points, or string worldsheet theories.

30 pages, 10 figures, 1 cartoon. See source for Mathematica notebook. v2: bootstrapped more complicated model, new Appendices. v3: journal version, v4: minor typos fixed

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