Galaxies

Galaxies

The University of Toronto’s astronomy department features a powerful instrumentation-driven effort in galaxy science. Roberto Abraham leads with his work on the Dragonfly array (and its future successor MOTHRA) to image ultra-diffuse and low-surface-brightness galaxies that have long evaded deep observations. Suresh Sivanandam complements this by developing infrared adaptive-optics integral-field spectrographs (such as GIRMOS) to dissect galaxies in clusters, exploring their star-formation, stripping, and stellar populations. Together, their efforts to both detect the faintest galactic structures and resolve their internal properties are deeply synergistic with Norman Murray‘s cosmological simulations: Murray’s models of gas flows, winds, and the circumgalactic medium supply theoretical predictions that can now be probed with Abraham’s and Sivanandam’s instrumentation.

On the theoretical and high-redshift front, Pratika Dayal crafts semi-numerical and semi-analytic models of galaxy formation, black-hole growth, and dust enrichment during the Epoch of Reionization, tightly linked to observations from ALMA and JWST. Seiji Fujimoto’s observational programs span optical to mm/radio wavelengths (especially ALMA and a 300-hour JWST campaign) to trace the earliest galaxies, black holes, and their gas and dust content. His work on high-redshift systems and galaxy kinematics provides an empirical counterpoint to Dayal’s models. In the local Universe, Jo Bovy applies tools from galactic dynamics and Astrostatistics (e.g., his widely used “galpy” code) to map the Milky Way’s structure and dark-matter distribution, while Ting Li, through her Southern Stellar Stream Spectroscopic Survey (S5), traces stellar streams to constrain how our Galaxy assembled — linking the cosmic dawn to the fossil record of the present day.

Underpinning both instrumentation and theory is a strong statistical and inferential research program. Gwen Eadie develops hierarchical Bayesian methods to infer dark matter halo properties across a range of galactic systems— from dwarf galaxies to globular clusters — thereby refining our understanding of galaxy mass distributions. Josh Speagle works at the intersection of statistics, AI, and astrophysics: he builds scalable inference frameworks to analyze massive datasets of stars and galaxies, with the goal of reconstructing their formation histories. Speagle’s methodological advances feed directly into the observational programs of Abraham and Sivanandam, and into the dynamical modeling of Bovy and Li — together building a coherent research ecosystem that unites faint-galaxy detection, high-redshift theory, and rigorous statistical inference.

Department members conduct research in the areas of: Evolution of galaxies and galaxy clusters through time, galaxy dynamics and  galactic Magnetism.

Roberto Abraham
Jo Bovy
Ray Carlberg (Emeritus)
Pratika Dayal (CITA)
Gwen Eadie (DAA & Statistics)
Seiji Fujimoto
Bryan Gaensler (Status Only)
Ting Li
Norm Murray (CITA)
Suresh Sivanandam (DAA & Dunlap)
Josh Speagle (DAA & Statistics)
Howard Yee (Emeritus)