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Needles in a haystack: advanced statistical techniques and large stellar spectroscopic datasets

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posted on 28.03.2022, 18:53 by Arvind Hughes
With the development of advanced astronomical instruments, many survey teams are producing datasets that are too large for traditional analysis. Thanks to recent improvements in computing and statistical methods, it is now possible to extract information more efficiently. In this thesis, data from the GALactic Archaeology with HERMES spectroscopic survey (GALAH), is used to show how machine learning methods can identify rare but interesting stars. This thesis tests a new methodology that employs the t-SNE dimensionality reduction technique with the clustering method, HDBSCAN, and a new tool developed by the researcher, the t-SNE Visualiser. This method was applied to ∼ 200,000 stars in the GALAH dataset with the aim of detecting extremely metal-poor stars and Solar twins. Applying this approach lead to the discovery of 66 possible extremely metal-poor stars and 20 Solar twin candidates. A verification of the success of the new methodology is also presented -- abstract.


Table of Contents

1. Introduction -- 2. Methodology -- 3. Results -- 4. Conclusion -- References.


Bibliography: pages 43-44 Theoretical thesis.

Awarding Institution

Macquarie University

Degree Type

Thesis MRes


MRes, Macquarie University, Faculty of Science & Engineering, Department of Physics

Department, Centre or School

Department of Physics

Year of Award


Principal Supervisor

Lee Spitler


Copyright Arvind Hughes 2017. Copyright disclaimer: http://mq.edu.au/library/copyright




1 online resource (vii, 44 pages): illustrations

Former Identifiers

mq:72405 http://hdl.handle.net/1959.14/1284747