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Rank fluctuations of matrix products and a moment method for growing groups

Published 4 Sep 2024 in math.PR, math.CO, and math.NT | (2409.03099v1)

Abstract: We consider the cokernel Gn=Cok(AkA2A1)G_n = \mathbf{Cok}(A_{k} \cdots A_2 A_1) of a product of independent n×nn \times n random integer matrices with iid entries from generic nondegenerate distributions, in the regime where both nn and kk are sent to \infty simultaneously. In this regime we show that the cokernel statistics converge universally to the reflecting Poisson sea, an interacting particle system constructed in arXiv:2312.11702, at the level of $1$-point marginals. In particular, corank(AkA2A1(modp))logpk\operatorname{corank}(A_{k} \cdots A_2 A_1 \pmod{p}) \sim \log_p k, and its fluctuations are O(1)O(1) and converge to a discrete random variable defined in arXiv:2310.12275. The main difference with previous works studying cokernels of random matrices is that GnG_n does not converge to a random finite group; for instance, the pp-rank of GnG_n diverges. This means that the usual moment method for random groups does not apply. Instead, we proceed by proving a `rescaled moment method' theorem applicable to a general sequence of random groups of growing size. This result establishes that fluctuations of pp-ranks and other statistics still converge to limit random variables, provided that certain rescaled moments $\mathbb{E}[#\operatorname{Hom}(G_n,H)]/C(n,H)$ converge.

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