Speaker
Description
This poster summarizes the current progress of a project developing a supervised machine learning (ML) framework to decompose galaxy structures using multi-wavelength data. A dataset from the 50 Mpc Galaxy Catalog (50MGC) has been assembled, and standardized g, r, i, z data-cube FITS images have been downloaded and generated. A full preprocessing pipeline covering star removal, segmentation, masking, and band normalization has been completed. A convolutional neural network (CNN) architecture has been selected for morphological classification and structural analysis, and initial model training is underway. Ongoing and future efforts will focus on model validation and multi-component decomposition of bulges, disks, and bars to support studies of galaxy evolution.
| Stream | Science or Engineering |
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