Ferguson, Matthew Chase2025-01-222025-01-222025-01-21vt_gsexam:42354https://hdl.handle.net/10919/124287This thesis explores the use of machine learning redshift estimation models trained on segmented galactic images. Segmentation of galaxies from the background is accomplished using a flood fill segmentation method which is novel to the field of galactic segmentation. Astronomy datasets are so large due to high volume modern surveys that automated analysis techniques are now required. Redshift is a prime example of an expensive measurement that is a candidate for automation. The Sloan Digital Sky Survey alone imaged more than 1 billion objects in 9 years, but only produced 4 million spectra over more than 20 years. Machine learning is an automation technology that promises to efficiently analyze imaging data alone such that redshift can be estimated with a high degree of accuracy. Ground truth redshift and multi-band galactic images were obtained for 200,000 galaxies from the Sloan Digital Sky Survey. Two model architectures were experimented with, a fully connected artificial neural network, and a convolutional neural network. Experiments were conducted on flood fill parameters, crop sizes, color spaces, and thresholding. We demonstrated that model performance on flood fill segments is higher than on unsegmented images across many crop sizes. The best achieved model performances for artificial neural networks, and convolutional neural networks are median absolute dispersions of 0.024 and 0.031, respectively.ETDenIn CopyrightRedshiftFlood FillSegmentationMachine LearningEstimationRegressionGalaxyDeep LearningThesis