Machine learning approaches to star-galaxy classification

Kim, Junhyung

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https://hdl.handle.net/2142/100895 Copy

Description

Title

Machine learning approaches to star-galaxy classification

Author(s)

Kim, Junhyung

Issue Date

2018-01-31

Director of Research (if dissertation) or Advisor (if thesis)

Brunner, Robert J.

Doctoral Committee Chair(s)

Thaler, Jon J.

Committee Member(s)

• Gollin, George D.
• Schwing, Alexander G.

Department of Study

Physics

Discipline

Physics

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• data analysis
• image processing
• photometric surveys
• star-galaxy classification
• cosmology
• deep learning
• convolutional neural networks
• generative adversarial networks

Abstract

Accurate star-galaxy classification has many important applications in modern precision cosmology. However, a vast number of faint sources that are detected in the current and next-generation ground-based surveys may be challenged by poor star-galaxy classification. Thus, we explore a variety of machine learning approaches to improve star-galaxy classification in ground-based photometric surveys. In Chapter 2, we present a meta-classification framework that combines existing star-galaxy classifiers, and demonstrate that our Bayesian combination technique improves the overall performance over any individual classification method. In Chapter 3, we show that a deep learning algorithm called convolutional neural networks is able to produce accurate and well-calibrated classifications by learning directly from the pixel values of photometric images. In Chapter 4, we study another deep learning technique called generative adversarial networks in a semi-supervised setting, and demonstrate that our semi-supervised method produces competitive classifications using only a small amount of labeled examples.

Graduation Semester

2018-05

Type of Resource

text

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http://hdl.handle.net/2142/100895

Copyright and License Information

Copyright 2018 Junhyung Kim

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