A morphological segmentation approach to determining bar lengths

TL;DR

This paper introduces a deep learning-based morphological segmentation method using U-Nets to accurately measure bar lengths in galaxies across different datasets, revealing how bar properties depend on galaxy characteristics and redshift.

Contribution

We develop a versatile deep learning segmentation technique for galaxy bars that works across various datasets and imaging types, enabling large-scale analysis of bar properties.

Findings

Bar length varies with galaxy morphology and stellar mass.

Normalized bar length decreases with redshift, especially in early-type galaxies.

Monochrome imaging can effectively distinguish bars and spiral arms, though length estimates differ.

Abstract

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 $\lambda_{R_e}$. 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 normalised 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.