Detecting Galaxy Tidal Features using Self-Supervised Representation Learning
Submitted to MNRAS, 2023
Note:
This website is a work in progress - apologies for any broken links.
My Research
My area of focus is galaxy evolution, more specifically how we can detect merging galaxies using galaxy images from optical imaging surveys. When galaxies merge with each other, the immense gravitational forces cause stellar material to be pulled out of the galaxies, forming diffuse regions of stars around the galaxies, known as tidal features. Studying these tidal features and their host galaxies can teach us a lot about the galaxy evolution process. Currently, I am constructing self-supervised machine learning model to automate the detection of tidal features.
Publications
Galaxy And Mass Assembly (GAMA): Comparing Visually and Spectroscopically Identified Galaxy Merger Samples
Published in MNRAS, 2023.
Posters and Figures
Detection of Galaxy Tidal Features using Self-Supervised Machine Learning
Presented at ASA 2023
High resolution UMAP plot
From SSL paper
Presentations
- September 2023: Accepted abstract to present at the Australia-ESO 2023 meeting in Canberra, Australia.
- July 2023: Accepted abstract to present at the 2023 International Conference on Machine Learning (ICML) Workshop on Machine Learning for Astrophysics in Honolulu, Hawai'i.
- May 2023: Presented at the 2023 ASTRO3D Science Meeting in Perth, Australia.
- June 2023: Presented at the Rubin Galaxies collaboration 2023 meeting.
- December 2022: Presented at the Australian LSST 2022 Workshop . Check out the slides here.
- June 2022: Presented at the European Astronomical Society (EAS) Annual 2022 Meeting in Valencia, Spain.
- June 2022: Presented at the Bayesian Deep Learning (BDL) for astronomy workshop in Paris, France. Watch the recording here.
Get In Touch
Like most scientists, I love talking about my work, so feel free to email me with any questions about my work, my code, ideas for collaborating, or just to chat. My email is: