Full metadata
Title
Specialized Noise Elimination in Astronomical Data using Deep Learning
Description
Astronomy has a data de-noising problem. The quantity of data produced by astronomical instruments is immense, and a wide variety of noise is present in this data including artifacts. Many types of this noise are not easily filtered using traditional handwritten algorithms. Deep learning techniques present a potential solution to the identification and filtering of these more difficult types of noise. In this thesis, deep learning approaches to two astronomical data de-noising steps are attempted and evaluated. Pre-existing simulation tools are utilized to generate a high-quality training dataset for deep neural network models. These models are then tested on real-world data. One set of models masks diffraction spikes from bright stars in James Webb Space Telescope data. A second set of models identifies and masks regions of the sky that would interfere with sky surface brightness measurements. The results obtained indicate that many such astronomical data de-noising and analysis problems can use this approach of simulating a high-quality training dataset and then utilizing a deep learning model trained on that dataset.
Date Created
2024
Contributors
- Jeffries, Charles George (Author)
- Bansal, Ajay (Thesis advisor)
- Windhorst, Rogier (Committee member)
- Acuna, Ruben (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
67 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.193524
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2024
Field of study: Software Engineering
System Created
- 2024-05-02 01:57:18
System Modified
- 2024-05-02 01:57:29
- 6 months 3 weeks ago
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