Galaxy Mergers in UNIONS -- II: Predicting Timescales in the Post-Merger Regime

TL;DR

This paper introduces a machine learning framework called extsc{Mummi} to predict post-merger timescales of galaxies using mock observations from simulations, applied to real survey data to study galaxy evolution.

Contribution

The study extends the extsc{Mummi} framework to classify post-merger galaxy timescales with over 70% accuracy and applies it to the UNIONS survey, creating a large catalog of post-merger galaxies with estimated timescales.

Findings

Achieved >70% accuracy in classifying post-merger timescales

Created a catalog of 8,716 post-merger galaxies with estimated timescales

Enabled detailed studies of galaxy evolution in the post-merger phase

Abstract

Galaxy mergers are critical events that influence galaxy evolution by driving processes such as enhanced star formation, quenching, and active galactic nucleus (AGN) activity. However, constraining the timescales over which these processes occur in the post-merger phase has remained a significant challenge. This study extends the MUlti-Model Merger Identifier (\textsc{Mummi}) framework to predict post-merger timescales ($T_{PM}$) for galaxies, leveraging machine learning models trained on realism-enhanced mock observations derived from the IllustrisTNG simulations. By classifying post-merger galaxies into four temporal bins spanning 0 to 1.76 Gyr after coalescence, \textsc{Mummi} achieves time classification accuracies exceeding 70 per cent. We apply this framework to the Ultraviolet Near Infrared Optical Northern Survey (UNIONS), yielding a catalog of 8,716 post-merger galaxies with $T_{PM}$ predictions and stellar masses $\log(M_*/M_\odot) \geq 10$ at redshifts 0.03 < z < 0.3. These results provide a robust methodology to connect galaxy interaction timescales with physical processes, enabling detailed studies of galaxy evolution in the post-merger regime.