MSc Thesis Proposal Announcement of Jaykumar Patel:"Classifying Galaxy Morphologies: Improved Residual Networks "

 

Date: Thursday December 15, 2022 

Time:  11:30am-1:00pm 

Location: Essex Hall, Room 122 

Reminders: Two-part attendance mandatory, arrive 5-10 minutes prior to event starting - LATECOMERS WILL NOT BE ADMITTED once the door has been closed and the presentation has begun. Please be respectful of the presenter by NOT knocking on the door for admittance. 

 

For the classification of images of galaxy morphology (a term used by astronomers to categorize galaxies based on how they appear visually), a variant of Residual Neural Network (RNN) was developed in this research. The variant, together with other popular convolutional neural networks (CNNs), is applied to a sample of 28790 and 25941 galaxy images from the Galaxy Zoo 2 dataset, to classify galaxies into five and seven classes respectively. The five classes are categorized as Completely round, In-between, Cigar shaped, Edge-on, and Spiral, and the seven classes are categorized as Completely round, In-between, Cigar shaped, Edge-on, Barred spiral, Unbarred spiral, and Irregular. Various metrics, such as accuracy, precision, recall, and F1 score, show that the proposed network achieves state-of-the-art classification performance among other networks, namely, VGG16, VGG19, Inception, ResNets, and EfficientNets. The overall classification accuracy of our network on the testing sets is 96% and 93% for datasets with five and seven classes respectively. The variant of the Residual Network model proposed in this study can be applied to large-scale galaxy classification in optical space surveys which provides a large amount of data. 

 

Keywords: Morphology, Neural Networks, Galaxy Zoo  

Internal Reader: Dr. Saeed Samet
External Reader: Dr. Dan Xiao
Advisor: Dr. Dan Wu

 

 

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