Sara Ellison
Position
Contact
Area of expertise
Multi-wavelength studies of galaxies and AGN, QSO absorption lines
Astronomy Research
My main research interests can be divided into two basic categories: quasar absorption lines and galaxy evolution as a function of environment. The underlying theme of this research is to understand galaxy evolution through cosmic time, with a particular focus on chemical enrichment of the interstellar and intergalactic media. The work is built on three pillars which span the main tools we use to study galaxy evolution: observations, simulations and machine learning.My research has been funded by NSERC Discovery grants ($821,695, from 2007-2023), a $120,000 NSERC Discovery Accelerator grant (2007-2010) and half a million dollars in infrastructure from CFI (2003-2012).
A list of past and current students and postdocs in my group and a full list of publications is available..
Observational studies
My group has made extensive use of large observational datasets to tackle a broad range of questions in the field of galaxy evolution. We are perhaps most well known for our work on galaxy mergers, where we have used the SDSS to extensively characterize the effect of interactions at low redshift with superlative statistics. This work has been complemented by both public and PI multi-wavelength observations spanning the X-ray to the radio, in order to study AGN, triggered star formation, gas depletion (or lack thereof!) and chemistry. Most recently, we have been extending this work to study spatial variations of merger-induced star formation and chemistry using the MaNGA IFU survey.Other recent observational projects have tackled the physical processes responsible for galaxy quenching, the gas content and star formation rates of AGN host galaxies, searching for (and finding!) dual AGN, galaxy morphologies, compact groups and barred galaxies and using MaNGA to trace spatial variations of star formation.
Finally, I continue to work actively in the field of QSO absorption line spectroscopy. Most significant recent projects in this domain include the XQ-100 large program (on which I was co-PI) and the COS-AGN survey. Within the XQ-100 project, I led the DLA science working group, which has contributed to the census of both neutral gas and metals at high redshift.
Simulation studies
To complement the observational work in my group, I maintain close ties with theoretical colleagues, and support galaxy simulation research in my group. Again, this work has had a significant focus on galaxy mergers, using a range of cosmological, zoom-in and binary galaxy simulations. Recent work includes the study of the impact of mergers on the circum-galactic medium, the use of mid-IR colours in identifying merger triggered AGN and tracing the spatial dependence of triggered star formation in mergers. I also work closely with the group of Hugo Martel to simulate the effect of bars on galaxy evolution. All of the above projects aim to make direct comparisons with simulations and data, or even directly merge the two by producing mock data from simulations.
Machine learning
The availability of massive public galaxy surveys, such as the SDSS, has revolutionized the power of archival research in extra-galacic astronomy. Multi-wavelength complements, over large fractions of the sky, leverage incredible diagnostic power, at energies ranging from the UV (e.g. GALEX) to the mid-IR (e.g. WISE) all the way to the radio (e.g. ALFALFA). This revolution is set to continue in the coming decades with even more massive datasets from projects such as Euclid and LSST. My group has been at the leading edge of using machine learning techniques in astronomy, publishing on topics that range from predictions of gas content and star formation rates, to analysing the likely physical mechanisms that quench star formation.In recognition of the enormous scope of machine learning applications in extra-galactic astronomy, I lead the GalNet collaboration, one branch of UVic's ARCNet research centre.