Evolution of the galaxy merger fraction in the CLAUDS+HSC-SSP deep fields

TL;DR

This study uses deep imaging surveys and machine learning to measure how the galaxy merger fraction evolves from redshift 0.25 to 1, revealing a significant increase with cosmic time and quantifying merger history for massive galaxies.

Contribution

It introduces a novel approach combining deep ground-based imaging, machine learning, and visual verification to accurately identify galaxy mergers up to redshift 1.

Findings

Merger fraction at z=0 is 1.0±0.2%.

Merger fraction evolves as (1+z)^{2.3±0.4}.

Typical massive galaxy has ~0.3 major mergers since z=1.

Abstract

We estimate the evolution of the galaxy-galaxy merger fraction for $M_\star>10^{10.5}M_\odot$ galaxies over $0.25<z<1$ in the $\sim$18.6 deg$^2$ deep CLAUDS+HSC-SSP surveys. We do this by training a Random Forest Classifier to identify merger candidates from a host of parametric morphological features, and then visually follow-up likely merger candidates to reach a high-purity, high-completeness merger sample. Correcting for redshift-dependent detection bias, we find that the merger fraction at $z=0$ is 1.0$\pm$0.2%, that the merger fraction evolves as $(1+z)^{2.3 \pm 0.4}$, and that a typical massive galaxy has undergone $\sim$0.3 major mergers since $z=1$. This pilot study illustrates the power of very deep ground-based imaging surveys combined with machine learning to detect and study mergers through the presence of faint, low surface brightness merger features out to at least $z\sim1$.