Po-Chieh Yu (YZU); En-Chi Chung (YZU); Jung-Fang Ke (YZU); Zhen-Sehng Cai (YZU); Ting-Ying Chien (YZU)
Investigating galaxy morphologies via stellar components using optical images has been extensively reported in past 30 years. However, the dust morphology in galaxy evolution is still poorly understood. Dust grains play an essential role in forming molecular gas that drives the star formation in the galaxies, investigating dust morphology of galaxies can provide another point of view to study galaxy evolution. Since stellar or dusty debris of mergers can be observed in some early-type galaxies, we study morphologies of residual images by using 2-dimensional decomposition algorithm GALFIT (Peng 2002) to investigate clues on the history of early-type galaxies. We decompose Sloan-Digital Sky Survey and Hyper Suprime-Cam images using GALFIT with an automated program to fit each image with possible parameters. We then use an unsupervised machine-learning algorithm to classify residual images of fitting results. We classify at least 3 possible types of residual images of early-type galaxies. Combining with spectroscopic data, we compare star formation rates and structural parameters among 3 types of early-type galaxies. Preliminary results will be shown in the presentation.