Observationally derived change in the star formation rate as mergers progress
W. J. Pearson, L. Wang, V. Rodriguez-Gomez, B. Margalef-Bentabol, L. E. Suelves

TL;DR
This study uses observational data and machine learning to track how star formation rates change during galaxy mergers, revealing increased activity before and after coalescence, especially in more massive galaxies.
Contribution
It introduces a novel method combining CNN-based merger timing with SFR analysis to study star formation evolution in observed galaxy mergers.
Findings
SFR increases from 300 Myr before to 200 Myr after merger.
Higher stellar mass galaxies show greater SFR enhancement.
No clear trend of SFR change with local density.
Abstract
Galaxy mergers can change the rate at which stars are formed. We can trace when these changes occur in simulations of galaxy mergers. However, for observed galaxies we do not know how the star formation rate (SFR) evolves along the merger sequence as it is difficult to probe the time before or after coalescence. We aim to derive how SFR changes in observed mergers throughout the merger sequence, from a statistical perspective. Merger times were estimated for observed galaxy mergers in the Kilo Degree Survey (KiDS) using a convolutional neural network (CNN). The CNN was trained on mock KiDS images created using IllustrisTNG data. The SFRs were derived from spectral energy density fitting to KiDS and VIKINGs data. To determine the change in SFR for the merging galaxies, each merging galaxy was matched and compared to ten comparable non-merging galaxies; matching each galaxy in redshift,…
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