Estimating morphological parameter of galaxies
School:
School of Computer and Information Sciences
Category:
ResearchPrimary
Project Overview
One Liner: Bayesian Neural Network for inferring morphological parameters of Low Surface Brightness Galaxy
Abstract
In this thesis, we aim to filter out biases of Bayesian Neural Network trained on synthetically generated images of Low Surface Brightness Galaxies.
Video available at this link.
Screenshots
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