Galaxy mergers in UNIONS – II. Predicting time-scales in the post-merger regime

ABSTRACT Galaxy mergers are critical events that influence galaxy evolution by driving processes such as enhanced star formation, quenching, and active galactic nucleus activity. However, constraining the time-scales over which these processes occur in the post-merger phase has remained a significant challenge. This study extends the MUlti-Model Merger Identifier (mummi) framework to predict post-merger time-scales ($T_{\rm PM}$) 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, mummi achieves time classification accuracies exceeding 70 per cent. We apply this framework to the UNIONS (Ultraviolet Near Infrared Optical Northern Survey), yielding a catalogue of 8716 post-merger galaxies with $T_{\rm PM}$ predictions and stellar masses $\log (M_*/{\rm M}_\odot) \ge 10$ at redshifts 0.03 < z < 0.3. These results provide a robust methodology to connect galaxy interaction time-scales with physical processes, enabling detailed studies of galaxy evolution in the post-merger regime.