Nonlinear Time-Series Analysis for Sparsely Sampled, Noisy Stellar Light Curves

About the Project

These projects are open to students worldwide, but have no funding attached. Therefore, the successful applicant will be expected to fund tuition fees at the relevant level (home or international) and any applicable additional research costs. Please consider this before applying.

Nonlinear analysis of light curves of variable stars has revealed complex patterns in their dynamics [1,2]. These patterns provide insight into the physical processes driving the variability. However, most nonlinear analyses of this type focus on long, finely and evenly sampled light curves from space telescopes such as Kepler or TESS. These assumptions are not met by most ground-based and many space-based telescopes, which observe at irregular intervals and typically produce short, noisy light curves.

In this PhD project we propose to modify and extend nonlinear time-series analysis techniques so that they are applicable to unevenly sampled, short and noisy datasets. These methods will be developed and benchmarked on standard nonlinear dynamical systems and then applied to search for nonlinear dynamical signatures in objects such as compact binaries and young stellar objects (YSOs). In both classes, the underlying variability is believed to be driven by nonlinear processes. However nonlinear time-series methods have not been extensively applied due to sampling constraints. For reference and comparison, we will also consider classes where nonlinear analysis is more established (e.g. certain pulsating variables) [3]. We will make use of datasets from ground-based observatories such as the North-PHASE survey and space-based observatories such as the Rossi X-ray Timing Explorer (RXTE) [4,5].

By the end of the project we envisage that this work will have extended or developed techniques for analysing real-world light-curve datasets of this type, and applied them to evaluate whether YSOs and compact binaries exhibit detectable hallmarks of nonlinear dynamics (e.g. low-dimensional chaos, intermittency, or complexity transitions) under realistic observational constraints.

This project can be offered fully remotely.

Informal enquiries can be made by contacting Dr G Sandip (sandip.george@abdn.ac.uk).

Decisions will be based on academic merit. The successful applicant should have, or expect to obtain, a UK Honours Degree at 2.1 (or equivalent) in physics, mathematics or computing sciences. Some experience with coding is desirable.

We encourage applications from all backgrounds and communities, and are committed to having a diverse, inclusive team.

Application Procedure:

Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php.

You should apply for Degree of Doctor of Philosophy in Physics to ensure your application is passed to the correct team for processing.

Please clearly note the name of the lead supervisor and project titleon the application form. If you do not include these details, it may not be considered for the project.

Your application must include: A personal statement, an up-to-date copy of your academic CV, and clear copies of your educational certificates and transcripts.

Please note: you do not need to provide a research proposal with this application.

If you require any additional assistance in submitting your application or have any queries about the application process, please don't hesitate to contact us at researchadmissions@abdn.ac.uk

Funding Notes

This is a self-funding project open to students worldwide. Our typical start dates for this programme are February or October.

Fees for this programme can be found here Finance and Funding | Study Here | The University of Aberdeen.