How many eigenvalues of a Gaussian random matrix are positive?
Satya N. Majumdar, C\'eline Nadal, Antonello Scardicchio, Pierpaolo, Vivo

TL;DR
This paper analytically characterizes the probability distribution of the number of positive eigenvalues in large Gaussian random matrices, revealing a universal large deviation form and confirming results with exact finite-size formulas.
Contribution
It provides an explicit large deviation rate function for the index distribution of Gaussian matrices, independent of Dyson index, and confirms the variance growth with exact finite-size analysis.
Findings
Large deviation form of index distribution derived
Variance of positive eigenvalues grows as log N / (βπ²)
Exact finite N formula confirms asymptotic variance result
Abstract
We study the probability distribution of the index , i.e., the number of positive eigenvalues of an Gaussian random matrix. We show analytically that, for large and large with the fraction of positive eigenvalues fixed, the index distribution where is the Dyson index characterizing the Gaussian ensemble. The associated large deviation rate function is computed explicitly for all . It is independent of and displays a quadratic form modulated by a logarithmic singularity around . As a consequence, the distribution of the index has a Gaussian form near the peak, but with a variance of index fluctuations growing as for large . For , this result is…
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Taxonomy
TopicsRandom Matrices and Applications · Theoretical and Computational Physics · Markov Chains and Monte Carlo Methods
