Abstract:
Galaxy mergers are known to trigger starbursts and star formation, and are a fundamental process when discussing galaxy evolution. However, there are still many unknowns when it comes to quantitative studies. This study aims to further our understanding of galaxy mergers by investigating their star formation histories. Using Convolutional Neural Network (CNN) models by Ackerman et al. (2018), we have classified galaxies in the MaNGA Firefly value-added catalog (Goddard et al. 2017 and Parikh et al. 2018) as merging or non-merging. Through an analysis of SSP properties and spatial distribution of star formation, we have been able to draw a clearer picture of how the merging galaxies have evolved. We will discuss the results.