Schönrock, Björn (2025) Point Source Detection and Removal in Astronomical Images. Master's Thesis / Essay, Computing Science.
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Abstract
With the increasing amount of astronomical data, analysis by hand becomes infeasible, and automated methods are necessary to perform this task. Point sources, such as stars, are a significant challenge in astronomical images, since they appear as bright spots that can hide or distort fainter structures in the image, such as galaxies or nebulae. Removing point sources can help making these structures more visible. The research on point source removal is not new, but accurate and efficient removal remains challenging. Many approaches, for example neural networks, are complex and computationally expensive, and they are often black boxes that are hard to understand. In this thesis, we aim to develop an efficient and explainable method for detecting and removing point sources in astronomical images using the Point Spread Function (PSF), which describes how a point appears in an image. The first part of the project involves building a classifier to separate point sources from other objects. In the second part, we will subtract the PSF to remove the identified point sources. We have obtained good results on point source detection, being able to achieve high recall scores on both simulated and real data. Removal of point sources proved much more challenging, with mixed results. We were able to subtract simulated stars with a well-known PSF quite well; however, point sources in real astronomical images leave residuals after subtraction.
| Item Type: | Thesis (Master's Thesis / Essay) |
|---|---|
| Supervisor name: | Wilkinson, M.H.F. |
| Degree programme: | Computing Science |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 18 Jul 2025 06:33 |
| Last Modified: | 18 Jul 2025 06:33 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/36361 |
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