Max Kurzner

Topic

Galaxy Morphology and Nuclear Structure in the Virgo Cluster

Department of Physics and Astronomy

Date & location

• Friday, July 31, 2026
• 1:00 P.M.
• Clearihue Building, Room B007

Examining Committee

Supervisory Committee

• Dr. Patrick Côté, Department of Physics and Astronomy, University of Victoria (Co-Supervisor)
• Dr. Jon Willis, Department of Physics and Astronomy, UVic (Co-Supervisor)
• Dr. Sébastien Fabbro, Department of Physics and Astronomy, UVic (Member)
• Dr. Nishant Mehta, Department of Computer Science, UVic (Outside Member)

External Examiner

• Dr. Adam Muzzin, Department of Physics and Astronomy, York University

Chair of Oral Examination

• Dr. Lenora Marcellus, School of Nursing, UVic

Abstract

Physical processes acting over a range of temporal and spatial scales drive the formation of structure in the Universe, and dictate the morphologies of its constituent galaxies. The appearance of a galaxy is thus directly related to its dynamical state and encodes information on how it has evolved over its lifetime. Attempts to classify the shapes and structures of galaxies have improved since the foundational efforts in the early to mid 20th century — pioneering schemes that were developed to describe bright, nearby galaxies. Modern extensions to these systems require more representative samples that covering an expanded range in mass and a broader diversity of structural types. Such samples in the local universe can serve as training sets to guide the outputs of the machine learning models and other automated tools that are being deployed to analyze the billions of galaxies detected in the coming era of large-scale surveys.

To create a state-of-the-art morphological catalogue of galaxies, I utilize the Next Generation Virgo Cluster Survey (NGVS), a large survey on the Canada–France–Hawaii Telescope, which conducted a census of galaxies in the Virgo cluster over roughly seven decades in stellar mass. I use the NGVS to construct the most complete morphological catalogue of a cluster to date through physically motivated visual classifications. I complement these morphologies by classifying nuclear star clusters (NSCs) belonging to Virgo’s member galaxies and compare the results to the predictions theoretical models for the formation and evolution of NSCs. I demonstrate the power and promise of machine learning models for galaxy classification, while highlighting the pitfalls of deploying these models without detailed knowledge of underlying galaxy properties, or being overly reliant on shallower datasets.

I use the morphological catalogue to present compilations of galaxies of special interest, such as ultra-compact and ultra-diffuse galaxies, post-merger objects, early-type galaxies with blue cores, or galaxies with possible offset nuclei. I provide some of the first clear evidence that morphology plays a key role in determining the incidence of NSCs in galaxies, and argue that NSCs arise in a hybrid of processes involving star cluster infall and central star formation. This dissertation highlights the continuing importance of visual morphological classification, both for connecting galaxy appearance to physical formation processes and for training/optimizing automated methods to classify galaxies in the era of wide field imaging surveys.