For gravitational-wave detections and analyses, the raw outputs from the gravitational-wave detectors need to be converted into analysable data through some calibration apparatus. This project investigates new techniques to improve calibration accuracy and precision and better integrate the calibration bias into astrophysical analyses. 

Recent advances in laser technology now enable the combination of multiple high-quality lasers into a single high-power beam. This project aims to investigate such 'coherently-combined' laser systems within the context of Earth-to-Space laser transmission. Applications of this technology include space debris tracking, free-space optical communications, and propulsion of light-sails for interstellar travel, such as Breakthrough Starshot.

The next big discovery in gravitational wave astronomy may be a first detection of continuous gravitational waves from rapidly-spinning neutron stars. This projects aims to develop the data analysis methods needed for such a discovery.

The project aims to develop methods to improve the sensitivity of optical metasurfaces for the detection of chemical and biological markers. By tailoring a high-precision optical interferometric sensing solution to the optical properties of a metasurface under-test, the project will improve the sensitivity of these devices, developing a new range of targeted ultra-precise metasurface sensors.

Construct a small dual tosion pendulum which have their centre of mass co-incide and their rotational axis colinear. Inital diagnostics will be done using shadow sensors.

Inter-satellite laser links are an emerging technology with applications in Earth Observation, telecommunications, security, and, the focus of the CGA space technology group.

This project aims to enhance the educational value of the online adaptation of Particle Builder by developing comprehensive physics and mathematical explanations. Through a literature review, content creation, and analysis of engagement data, the student researcher will contribute their physics expertise to create an engaging and effective learning resource.

This project analyses pre- and post-test data from students learning quantum computing through the spin-first approach. The aim is to evaluate question reliability, identify learning gains, and help develop a validated concept inventory tailored to this increasingly common teaching method.

By leveraging hybrid digital-optical methods, we develop new distributed and quasi-distributed fibre-optic acoustic sensors. These acoustic sensors aim to measure vibration, strain and displacement all while localising the signal source along an optical fibre.

Ultralight boson particles have been predicted to solve problems in particle and high-energy physics and are compelling dark matter candidates. We develop algorithms and search for these conjectured ultralight bosons around black holes via gravitational-wave observations. 

Gravitational wave detectors have reached the thermodynamic limit of optical coating performance and require novel coating materials and coating noise suppression techniques for further sensitivity improvements. This project is to design a high-bandwidth feedback control system to stabilise the intensity and frequency of a 2µm-band laser for investigations of thermal noise in experimental mirror coatings.

A project to advance a prototype displacement sensor to test-type phase, via improved compact mechanical design,  vacuum compatibility, and improved sensor testing.

We study the numerical waveforms for the gravitational waves emitted during the black hole ringdown stage, implement tools and data analysis frameworks, and analyze the latest gravitational-wave data to estimate black hole properties and test the general theory of relativity.

Optical cavities are widely used in physics and precision measurement.  This project will explore the use of modern machine learning methods for the control of suspended optical cavities.

This project aims to develop a three-mirror coupled optical cavity system with automated alignment and control. Machine learning will be used to identify optical modes and optimize cavity operation, enabling advanced studies in precision optical control and interferometry.