Quantum graphs -- Generic eigenfunctions and their nodal count and Neumann count statistics

TL;DR

This thesis investigates eigenfunctions on quantum graphs, focusing on nodal and Neumann counts, establishing statistical behaviors, symmetries, and generic properties, with implications for spectral geometry and graph topology.

Contribution

It introduces a probabilistic framework for nodal and Neumann count statistics, proves Gaussian behavior for certain graph families, and generalizes eigenfunction non-vanishing properties.

Findings

Nodal count statistics reflect the graph's topology via the first Betti number.

Neumann count approaches a Gaussian distribution for growing tree graphs.

Eigenfunction derivatives at vertices are generically non-zero.

Abstract

In this thesis, we study Laplacian eigenfunctions on metric graphs, also known as quantum graphs. We restrict the discussion to standard quantum graphs. These are finite connected metric graphs with functions that satisfy Neumann vertex conditions. The first goal of this thesis is the study of the nodal count problem. That is the number of points on which the $n$th eigenfunction vanishes. We provide a probabilistic setting using which we are able to define the nodal count\textquoteright s statistics. We show that the nodal count statistics admit a topological symmetry by which the first Betti number of the graph can be obtained. We revise a conjecture that predicts a universal Gaussian behavior of the nodal count statistics for large graphs and we prove it for a certain family of graphs. The second goal is to formulate and study the Neumann count, which is the number of local extrema of the $n$th eigenfunction. This counting problem is motivated by the Neumann partitions of planar domains, a novel concept in spectral geometry. We provide uniform bounds on the Neumann count and investigate its statistics. We show that the Neumann count provides complimentary geometrical information to that obtained from the nodal count. We show that for a certain family of growing tree graphs the Neumann count statistics approaches a Gaussian distribution. The third goal is a genericity result, which justifies the generality of the Neumann count discussion. To this day it was known that generically, eigenfunctions do not vanish on vertices. We generalize this result to derivatives at vertices as well. That is, generically, the derivatives of an eigenfunction on interior vertices do not vanish.