Kenji Bekki

Bars are important drivers of galaxy evolution, influencing many physical processes and properties. Characterizing bars is a difficult task, especially in large-scale surveys. In this work, we propose a novel morphological segmentation technique for determining bar lengths based on deep learning. We develop U-Nets capable of decomposing galaxy images into pixel masks highlighting the regions corresponding to bars and spiral arms. We demonstrate the versatility of this technique through applying our models to galaxy images from two different observational data sets with different source imagery, and to RGB colour and monochromatic galaxy imaging. We apply our models to analyse SDSS and Subaru HyperSuprime Cam imaging of barred galaxies from the NA10 and Sydney AAO Multi-object IFS catalogues in order to determine the dependence of bar length on stellar mass, morphology, redshift and the spin parameter proxy $\lambda _{R_e}$. Based on the predicted bar masks, we show that the relative bar scale length varies with morphology, with early type galaxies hosting longer bars. While bars are longer in more massive galaxies in absolute terms, relative to the galaxy disc they are actually shorter. We also find that the normalized bar length decreases with increasing redshift, with bars in early type galaxies exhibiting the strongest rate of decline. We show that it is possible to distinguish spiral arms and bars in monochrome imaging, although for a given galaxy the estimated length in monochrome tends to be longer than in colour imaging. Our morphological segmentation technique can be efficiently applied to study bars in large-scale surveys and even in cosmological simulations.