A morphological segmentation approach to determining bar lengths ...

• Main Authors: Cavanagh, Mitchell K., Bekki, Kenji, Groves, Brent A.
• Format: Text
• Language: unknown
• Published: arXiv 2023
• Subjects: Astrophysics of Galaxies astro-ph.GA; Instrumentation and Methods for Astrophysics astro-ph.IM; FOS Physical sciences; sami
• Online Access: https://dx.doi.org/10.48550/arxiv.2309.02380; https://arxiv.org/abs/2309.02380

Bars are important drivers of galaxy evolution, influencing many physical processes and properties. Characterising 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 datasets with different source imagery, and to RGB colour and monochromatic galaxy imaging. We apply our models to analyse SDSS and Subaru HSC imaging of barred galaxies from the NA10 and SAMI catalogues in order to determine the dependence of bar length on stellar mass, morphology, redshift and the spin parameter proxy $λ_{R_e}$. Based on the predicted bar masks, we show that the relative bar scale length varies with morphology, with early type galaxies ... : 22 pages, 18 figures, submitted to MNRAS ...