arXiv subjects

Charles Devlin VI

Publications and source records attributed to Charles Devlin VI.

5 recordsLinked to original sources

arXiv2026

The coordinate change formula for the Liouville quantum gravity metric holds for all conformal maps simultaneously

Liouville quantum gravity (LQG) is, heuristically, a theory of random Riemannian geometry with Riemannian metric tensor $e^{\gamma h} (\mathrm{d} x^2 + \mathrm{d} y^2)$, where $h$ is a variant of the Gaussian free field and $\gamma > 0$ is a parameter. If $U \subset \mathbb{C}$ is an open set, $\phi \colon U \to \phi(U)$ is a conformal map, and $h^{\phi} = h \circ \phi^{-1} + Q \log|(\phi^{-1})'|$ (where $Q = Q(\gamma)$ is a parameter), then the LQG surface on $U$ defined with field $h$ is equivalent to the LQG surface on $\phi(U)$ with field $h^{\phi}$. This equivalence is meant in the sense that the area measures and distance functions on these surfaces are almost surely equivalent. It is known for the area measure that, in fact, this equivalence holds almost surely for all conformal maps $\phi$ simultaneously (Sheffield-Wang 2016). We prove the corresponding result for the distance function. This makes precise the frequently used heuristic definition that a quantum surface is a random equivalence class of domains equipped with the LQG area measure and LQG distance function.

math.PR↗

arXiv2024

Almost Sure Convergence of Liouville First Passage Percolation

Liouville first passage percolation (LFPP) with parameter $ξ> 0$ is the family of random distance functions (metrics) $(D_h^ε)_{ε> 0}$ on $\mathbb{C}$ obtained heuristically by integrating $e^{ξh}$ along paths, where $h$ is a variant of the Gaussian free field. There is a critical value $ξ_{\text{crit}} \approx 0.41$ such that for $ξ\in (0, ξ_{\text{crit}})$, appropriately rescaled LFPP converges in probability uniformly on compact subsets of $\mathbb{C}$ to a limiting metric $D_h$ on $γ$-Liouville quantum gravity with $γ= γ(ξ) \in (0,2)$. We show that the convergence is almost sure, giving an affirmative answer to a question posed by Gwynne and Miller (2019).

math.PR↗

arXiv2021

Distribution of Eigenvalues of Matrix Ensembles arising from Wigner and Palindromic Toeplitz Blocks

Random Matrix Theory (RMT) has successfully modeled diverse systems, from energy levels of heavy nuclei to zeros of $L$-functions; this correspondence has allowed RMT to successfully predict many number theoretic behaviors. However there are some operations which to date have no RMT analogue. Our motivation is to find an RMT analogue of Rankin-Selberg convolution, which constructs a new $L$-functions from an input pair. We report one such attempt; while it does not appear to model convolution, it does create new ensembles with properties hybridizing those of its constituents. For definiteness we concentrate on the ensemble of palindromic real symmetric Toeplitz (PST) matrices and the ensemble of real symmetric matrices, whose limiting spectral measures are the Gaussian and semi-circular distributions, respectively; these were chosen as they are the two extreme cases in terms of moment calculations. For a PST matrix $A$ and a real symmetric matrix $B$, we construct an ensemble of random real symmetric block matrices whose first row is $\lbrace A, B \rbrace$ and whose second row is $\lbrace B, A \rbrace$. By Markov's Method of Moments and the use of free probability, we show this ensemble converges weakly and almost surely to a new, universal distribution with a hybrid of Gaussian and semi-circular behaviors. We extend this construction by considering an iterated concatenation of matrices from an arbitrary pair of random real symmetric sub-ensembles with different limiting spectral measures. We prove that finite iterations converge to new, universal distributions with hybrid behavior, and that infinite iterations converge to the limiting spectral measure of the dominant component matrix.

math.PR↗

arXiv2020

Determining optimal test functions for $2$-level densities

Katz and Sarnak conjectured a correspondence between the $n$-level density statistics of zeros from families of $L$-functions with eigenvalues from random matrix ensembles. In many cases the sums of smooth test functions, whose Fourier transforms are finitely supported, over scaled zeros in a family converge to an integral of the test function against a density $W_{n, G}$ depending on the symmetry $G$ of the family (unitary, symplectic or orthogonal). This integral bounds the average order of vanishing at the central point of the corresponding family of $L$-functions. We can obtain better estimates on this vanishing by finding better test functions to minimize the integral. We pursue this problem when $n=2$, minimizing \[ \frac{1}{\Phi(0, 0)} \int_{{\mathbb R}^2} W_{2,G} (x, y) \Phi(x, y) dx dy \] over test functions $\Phi \colon {\mathbb R}^2 \to [0, \infty)$ with compactly supported Fourier transform. We study a restricted version of this optimization problem, imposing that our test functions take the form $\phi(x) \psi(y)$ for some fixed admissible $\psi(y)$ and $\mathrm{supp}({\hat \phi}) \subseteq [-1, 1]$. Extending results from the $1$-level case, namely the functional analytic arguments of Iwaniec, Luo and Sarnak and the differential equations method introduced by Freeman and Miller, we explicitly solve for the optimal $\phi$ for appropriately chosen fixed test function $\psi$. The solution allows us to deduce strong estimates for the proportion of newforms of rank $0$ or $2$ in the case of $\mathrm{SO}(\mathrm{even})$, rank $1$ or $3$ in the case of $\mathrm{SO}(\mathrm{odd})$, and rank at most $2$ for $\mathrm{O}$, $\mathrm{Sp}$, and $\mathrm{U}$; our estimates are a significant strengthening of the best known estimates obtained with the $1$-level density. We conclude by discussing further improvements on estimates by the method of iteration.

math.NT↗

arXiv2019

Distribution of Eigenvalues of Random Real Symmetric Block Matrices

Random Matrix Theory (RMT) has successfully modeled diverse systems, from energy levels of heavy nuclei to zeros of $L$-functions. Many statistics in one can be interpreted in terms of quantities of the other; for example, zeros of $L$-functions correspond to eigenvalues of matrices, and values of $L$-functions to values of the characteristic polynomials. This correspondence has allowed RMT to successfully predict many number theory behaviors; however, there are some operations which to date have no RMT analogue. The motivation of this paper is to try and find an RMT equivalent to Rankin-Selberg convolution, which builds a new $L$-functions from an input pair. For definiteness we concentrate on two specific families, the ensemble of palindromic real symmetric Toeplitz (PST) matrices and the ensemble of real symmetric (RS) matrices, whose limiting spectral measures are the Gaussian and semicircle distributions, respectively; these were chosen as they are the two extreme cases in terms of moment calculations. For a PST matrix $A$ and a RS matrix $B$, we construct an ensemble of random real symmetric block matrices whose first row is $\lbrace A, B \rbrace$ and whose second row is $\lbrace B, A \rbrace$. By Markov's Method of Moments, we show this ensemble converges weakly and almost surely to a new, universal distribution with a hybrid of Gaussian and semicircle behaviors. We extend this construction by considering an iterated concatenation of matrices from an arbitrary pair of random real symmetric sub-ensembles with different limiting spectral measures. We prove that finite iterations converge to new, universal distributions with hybrid behavior, and that infinite iterations converge to the limiting spectral measures of the component matrices.

math.PR↗