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 (Mummi) framework to predict post-merger timescales (TPM) 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 Ultraviolet Near Infrared Optical Northern Survey (UNIONS), yielding a catalog of 8,716 post-merger galaxies with TPM predictions and stellar masses log (M*/M⊙) ≥ 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.
Finethy et al. (Fri,) studied this question.