Speaker
Description
Galactic bars play a crucial role in the evolution of disk galaxies, influencing gas dynamics, star formation, and secular evolution. However, identifying bars in a large sample of galaxies has been challenging. Our understanding of the impact of bars has leaped forward with Galaxy Zoo 3D: a citizen science project aimed at identifying bars in thousands of galaxies.
In this work, we use a machine learning model trained to identify bars in optical ground-based data to look at bars across a much larger sample. We leverage the imaging of almost 100,000 galaxies in the UNIONS survey and identify weak and strong bars within them. Using Spectral Energy Distribution fitting, we compare the stellar populations inside and outside the bar, to trace the influence of the bar on the stellar dynamics and star formation. Our results provide new insights into the role of bars in shaping galaxy evolution, whilst also demonstrating the power of machine learning in large-scale extragalactic studies.
