Henghes, Ben;
(2022)
Novel applications of machine learning in astronomy and beyond.
Doctoral thesis (Ph.D), UCL (University College London).
Preview |
Text
Henghes_B_Thesis_Final.pdf - Accepted Version Download (29MB) | Preview |
Abstract
The field of astronomy is currently experiencing a period of unprecedented expansion, predominantly brought about by the vast amounts of data being produced by the latest telescopes and surveys. New methods will be required to have any hope of being able to analyse the data collected, the most widespread of which is machine learning. Machine learning has evolved rapidly over the past decade in an attempt to match the rate of increasing data, and aided by advancements in computer hardware, analyses that would have been impossible in the past are now common place on astronomers’ laptops. However, despite machine learning becoming a favourite tool for many, there is often little consideration for which algorithms are best suited for the job. In this thesis, machine learning is implemented in a variety of different problems ranging from Solar System science and searching for Trans-Neptunian Objects (TNOs), to the cosmological problem of obtaining accurate photometric redshift (photo-z) estimations for distant galaxies. In chapter 2 I implement many different machine learning classifiers to aid the Dark Energy Survey’s search for TNOs, comparing the classifiers to find the most suitable, and demonstrating how machine learning can provide significant increases in efficiency. In chapter 3 I implement machine learning algorithms to provide photo-z estimations for a million galaxies, using the method as an example for how it is possible to benchmark machine learning algorithms to provide information about the scalibility of different methods. In chapter 4 I expand upon the benchmarking of methods developed for obtaining photo-z estimates, applying them instead to deep learning algorithms which directly use image data, before discussing future work and concluding in chapter 5.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Novel applications of machine learning in astronomy and beyond |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2022. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10143132 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
