Skip to main content
Thesis defences

PhD Oral Exam - Fabrice Nonez, Mathematics

Spectral embeddings through nonstandard samplings


Date & time
Monday, May 12, 2025
3 p.m. – 6 p.m.
Cost

This event is free

Organization

School of Graduate Studies

Contact

Dolly Grewal

Where

J.W. McConnell Building
1400 De Maisonneuve Blvd. W.
Room 921-4

Accessible location

Yes

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

The Spectral Embedding Theorem, one of the formulations of the Spectral Theorem, states that any densely-defined symmetric operator A on a separable Hilbert space H can be extended by a multiplication operator through an isometric embedding of H in an L_2-space.

In this research project, the goal is to study a novel process. This process starts with a (real or complex) separable Hilbert space H and a densely-defined symmetric operator A. It results with a compact metric space Ω, a probability measure μ on Ω, an isometric embedding U : H ⟶ L_2(Ω, μ) and a multiplication operator T on L_2(Ω, μ) such that U ∘ A ⊂ T ∘ U, satisfying the Spectral Embedding Theorem.

The process uses two parameters, which are objects of Nonstandard Analysis: the nonstandard sampling and the standard-biased scale. We see in this Thesis that these objects allow for a new proof of the Spectral Embedding Theorem, as well as other forms of the Spectral Theorem.

Furthermore, we will observe that with specific operators, the process can result in explicit and natural objects through the careful tweaking of the parameters. Specifically, we work with landmark examples of the theory, the shift operator and the differential operator on the line, to derive the Fourier series and the Fourier Transform from the process.

Back to top

© Concordia University