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Statistical inference on the periodicity of irregularly sampled light curves

Derezea, Efthymia (2023) Statistical inference on the periodicity of irregularly sampled light curves. Doctor of Philosophy (PhD) thesis, University of Kent,. (doi:10.22024/UniKent/01.02.100735) (KAR id:100735)


This thesis deals with the problem of period estimation of irregularly sampled time series, and specifically light curves of variable young stars. Knowing the period of these objects can provide important information about the stars' formation and other characteristics. The light curves are measurements of brightness over time conducted in multiple astronomical filters. We examine this problem from three different points of view. First, we need to obtain an accurate period estimate. For that purpose we introduce a weighted t-process regression model for period estimation as a flexible alternative to Gaussian process regression, since it is common for such data to exhibit a fat-tail behaviour, and we extend these models in order to include measurements from multiple astronomical filters. Secondly, we need to accompany our estimates with some credibility as to whether they represent a real periodic signal. This is usually addressed through hypothesis testing. To that end, we introduce a flexible testing scheme using saddlepoint approximation, that can be applied on a range of periodic models including Gaussian process regression. These tests are also extended for data contaminated with red noise, a type of correlated noise that usually appears in such data. Finally, we explore the asymptotic properties of simple harmonic models with additional red noise and show that this estimates are consistent and asymptotically normal. We test our results through extensive simulation studies which are reported along with an application on some real light curves from the Hunting Outbursting Young Stars citizen science project.

Item Type: Thesis (Doctor of Philosophy (PhD))
Thesis advisor: Kume, Alfred
Thesis advisor: Froebrich, Dirk
DOI/Identification number: 10.22024/UniKent/01.02.100735
Uncontrolled keywords: time series; light curves; variable young stars
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
SWORD Depositor: System Moodle
Depositing User: System Moodle
Date Deposited: 04 Apr 2023 09:10 UTC
Last Modified: 05 Apr 2023 08:26 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

Derezea, Efthymia.

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