Galactic Flood Fill Segmentation and Machine Learning Redshift Estimation

This 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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2025

Author dc:creator

• Ferguson, Matthew Chase

Chair dc:contributor.committeechair

• Jones, Creed Farris

Committee members dc:contributor.committeemember

• Talty, Timothy Joseph
• Plassmann, Paul E.

• Redshift
• Flood Fill
• Segmentation
• Machine Learning
• Estimation
• Regression
• Galaxy
• Deep Learning

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:42354

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/124287