随机图的沙堆群及其对的分布

Hodges, Eliot
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引用次数: 0

摘要

我们确定了Erd\H{o} - r \ {e}nyi随机图$G(n,q)$的沙堆群(也称为雅可比矩阵)及其正则对偶对在$n$趋于无穷时的分布,充分解决了Clancy、Leake和Payne在2015年提出的一个猜想,并推广了Wood在群上的结果。特别地,我们证明了具有完美对称配对$\delta$的有限阿贝算子$p$-群$G$表现为沙堆群的Sylow $p$-部分及其频率与$|G||\ mathm {Aut}(G,\delta)|$成反比,其中$\ mathm {Aut}(G,\delta)$是$G$保持配对$\delta$的自同构集。虽然这种分布与Cohen-Lenstra分布有关,但由于配对的额外代数数据,这两个分布并不相同。证明使用矩法:我们首先计算随机变量的完整矩集(从我们的随机对象到感兴趣的类别中的固定对象的外胚的平均数目),然后显示矩决定分布。为了得到这些矩,我们证明了对偶群具有对称对的随机对称积分矩阵的核矩的普适性,该普适性足以处理对角项的依赖性和对的附加数据。然后我们应用Sawin和Wood的结果来证明这些矩决定了一个唯一的分布。
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The Distribution of Sandpile Groups of Random Graphs with their Pairings
We determine the distribution of the sandpile group (also known as the Jacobian) of the Erd\H{o}s-R\'{e}nyi random graph $G(n,q)$ along with its canonical duality pairing as $n$ tends to infinity, fully resolving a conjecture from 2015 due to Clancy, Leake, and Payne and generalizing the result by Wood on the groups. In particular, we show that a finite abelian $p$-group $G$ equipped with a perfect symmetric pairing $\delta$ appears as the Sylow $p$-part of the sandpile group and its pairing with frequency inversely proportional to $|G||\mathrm{Aut}(G,\delta)|$, where $\mathrm{Aut}(G,\delta)$ is the set of automorphisms of $G$ preserving the pairing $\delta$. While this distribution is related to the Cohen-Lenstra distribution, the two distributions are not the same on account of the additional algebraic data of the pairing. The proof utilizes the moment method: we first compute a complete set of moments for our random variable (the average number of epimorphisms from our random object to a fixed object in the category of interest) and then show the moments determine the distribution. To obtain the moments, we prove a universality result for the moments of cokernels of random symmetric integral matrices whose dual groups are equipped with symmetric pairings that is strong enough to handle both the dependence in the diagonal entries and the additional data of the pairing. We then apply results due to Sawin and Wood to show that these moments determine a unique distribution.
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